diff --git a/unsloth/__init__.py b/unsloth/__init__.py
index a1a39fd19..4da08da13 100644
--- a/unsloth/__init__.py
+++ b/unsloth/__init__.py
@@ -22,7 +22,7 @@
 already_imported = [mod for mod in critical_modules if mod in sys.modules]
 
 # This check is critical because Unsloth optimizes these libraries by modifying
-# their code at import time. If they're imported first, the original (slower, 
+# their code at import time. If they're imported first, the original (slower,
 # more memory-intensive) implementations will be used instead of Unsloth's
 # optimized versions, potentially causing OOM errors or slower training.
 
@@ -73,6 +73,17 @@ def get_device_type():
 pass
 DEVICE_TYPE : str = get_device_type()
 
+def get_device_count():
+    if DEVICE_TYPE == "cuda":
+        return torch.cuda.device_count()
+    elif DEVICE_TYPE == "xpu":
+        return torch.xpu.device_count()
+    else:
+        return 0
+pass
+
+DEVICE_COUNT : int = get_device_count()
+
 # Reduce VRAM usage by reducing fragmentation
 # And optimize pinning of memory
 if DEVICE_TYPE == "cuda" and os.environ.get("UNSLOTH_VLLM_STANDBY", "0")=="0":
@@ -237,4 +248,4 @@ def is_bf16_supported(): return SUPPORTS_BFLOAT16
 from .trainer import *
 
 # Patch TRL trainers for backwards compatibility
-_patch_trl_trainer()
\ No newline at end of file
+_patch_trl_trainer()
diff --git a/unsloth/kernels/utils.py b/unsloth/kernels/utils.py
index 1c65246b3..645319d42 100644
--- a/unsloth/kernels/utils.py
+++ b/unsloth/kernels/utils.py
@@ -18,7 +18,7 @@
 next_power_of_2 = triton.next_power_of_2
 import functools
 from typing import Optional
-from unsloth import DEVICE_TYPE
+from unsloth import DEVICE_TYPE, DEVICE_COUNT
 
 # torch.cuda.amp.custom_fwd is deprecated >= 2.4
 import torch
@@ -89,10 +89,11 @@ def get_ptr(x: Optional[torch.Tensor]):
     get_ptr = bnb.functional.get_ptr
 
 
-if DEVICE_TYPE == "cuda" and torch.cuda.device_count() > 1:
-    torch_gpu_device = torch.cuda.device
-elif DEVICE_TYPE == "xpu" and torch.xpu.device_count() > 1:
-    torch_gpu_device = torch.xpu.device
+if DEVICE_COUNT > 1:
+    if DEVICE_TYPE == "cuda":
+        torch_gpu_device = torch.cuda.device
+    elif DEVICE_TYPE == "xpu":
+        torch_gpu_device = torch.xpu.device
 else:
     from contextlib import nullcontext
     def torch_gpu_device(device): return nullcontext()
@@ -100,7 +101,7 @@ def torch_gpu_device(device): return nullcontext()
 
 # INTEL GPU Specific Logic
 if DEVICE_TYPE == "xpu":
-    _gpu_getCurrentRawStream = torch._C._xpu_getCurrentRawStream 
+    _gpu_getCurrentRawStream = torch._C._xpu_getCurrentRawStream
 # NVIDIA GPU Default Logic
 else:
     _gpu_getCurrentRawStream = torch._C._cuda_getCurrentRawStream
@@ -121,12 +122,12 @@ def _get_tensor_stream(tensor: torch_Tensor) -> c_void_p:
 if DEVICE_TYPE == "xpu":
     _XPU_STREAMS = {
         (index := torch.xpu.device(i).idx) : ctypes.c_void_p(torch._C._xpu_getCurrentRawStream(index))
-        for i in range(torch.xpu.device_count())
+        for i in range(DEVICE_COUNT)
     }
     XPU_STREAMS   = [None] * (max(_XPU_STREAMS.keys()) + 1)
     WEIGHT_BUFFERS = [None] * (max(_XPU_STREAMS.keys()) + 1)
     ABSMAX_BUFFERS = [None] * (max(_XPU_STREAMS.keys()) + 1)
-    for k, v in _XPU_STREAMS.items(): 
+    for k, v in _XPU_STREAMS.items():
         XPU_STREAMS[k] = v
     XPU_STREAMS = tuple(XPU_STREAMS)
     del _XPU_STREAMS
@@ -134,7 +135,7 @@ def _get_tensor_stream(tensor: torch_Tensor) -> c_void_p:
     # NVIDIA GPU Default Logic
     _CUDA_STREAMS = {
         (index := torch.cuda.device(i).idx) : ctypes.c_void_p(torch._C._cuda_getCurrentRawStream(index))
-        for i in range(torch.cuda.device_count())
+        for i in range(DEVICE_COUNT)
     }
     CUDA_STREAMS   = [None] * (max(_CUDA_STREAMS.keys()) + 1)
     WEIGHT_BUFFERS = [None] * (max(_CUDA_STREAMS.keys()) + 1)
@@ -152,16 +153,16 @@ def _get_tensor_stream(tensor: torch_Tensor) -> c_void_p:
     # TODO: After adding XPU BNB support, this function should be implemented
     def cdequantize_blockwise_fp32(*args, **kwargs):
         raise RuntimeError("XPU BNB support is not implemented yet. cdequantize_blockwise_fp32 should not be called now.")
-    
+
     def cdequantize_blockwise_fp16_nf4(*args, **kwargs):
         raise RuntimeError("XPU BNB support is not implemented yet. cdequantize_blockwise_fp16_nf4 should not be called now.")
-    
+
     def cdequantize_blockwise_bf16_nf4(*args, **kwargs):
         raise RuntimeError("XPU BNB support is not implemented yet. cdequantize_blockwise_bf16_nf4 should not be called now.")
-    
+
     def cgemm_4bit_inference_naive_fp16(*args, **kwargs):
         raise RuntimeError("XPU BNB support is not implemented yet. cgemm_4bit_inference_naive_fp16 should not be called now.")
-    
+
     def cgemm_4bit_inference_naive_bf16(*args, **kwargs):
         raise RuntimeError("XPU BNB support is not implemented yet. cgemm_4bit_inference_naive_bf16 should not be called now.")
 else:
@@ -193,7 +194,7 @@ def get_lora_parameters(proj):
     adapter = getattr(proj, "active_adapters", None)
     if adapter is None: adapter = getattr(proj, "active_adapter", ("default"))
     adapter = adapter[0]
-    
+
     return (
         W,
         getattr(W, "quant_state", None),
@@ -232,7 +233,7 @@ def get_lora_parameters_bias(proj):
 if DEVICE_TYPE == "xpu" and HAS_XPU_STREAM:
     @torch.inference_mode
     def fast_dequantize(W, quant_state = None, out = None, use_global_buffer = False):
-        # TODO: After adding XPU BNB support, check this function 
+        # TODO: After adding XPU BNB support, check this function
         if quant_state is None: return W
         if type(quant_state) is not list:
             # New quant_state as a class
@@ -535,7 +536,7 @@ def fast_gemv(X, W, quant_state, out = None):
         device = W.device
         device_index = device.index
         CUDA_STREAM = CUDA_STREAMS[device_index]
-        
+
         # assert(dtype == X.dtype)
         bout = shape[0]
 
@@ -669,7 +670,7 @@ def fast_linear_forward(proj, X, temp_lora = None, out = None):
             lora_A._fast_lora = lora_A.to(dtype)
             lora_B._fast_lora = lora_B.to(dtype)
         pass
-        
+
         if bsz == 1:
             out = out.view(out_dim)
             temp_lora = torch_mv(lora_A._fast_lora, X.ravel(), out = temp_lora)
@@ -709,6 +710,6 @@ def matmul_lora(X, W, W_quant, A, B, s, out = None):
         out.addmm_(XA, B.to(dtype), alpha = s)
         # out += (X @ A.to(dtype)) @ (s * B.to(dtype))
     pass
-    
+
     return out.view(batch, seq_len, -1) if reshape else out
 pass
diff --git a/unsloth/models/_utils.py b/unsloth/models/_utils.py
index f576d17dc..c6ff7b626 100644
--- a/unsloth/models/_utils.py
+++ b/unsloth/models/_utils.py
@@ -77,7 +77,7 @@
 import re
 import warnings, subprocess, re, inspect, psutil, os, math
 from unsloth_zoo.utils import Version
-from unsloth_zoo import DEVICE_TYPE
+from unsloth import DEVICE_TYPE, DEVICE_COUNT
 
 from unsloth_zoo.tokenizer_utils import (
     patch_tokenizer as _patch_tokenizer,
@@ -142,12 +142,6 @@
 import logging
 logging.getLogger("transformers.tokenization_utils_base").setLevel(logging.CRITICAL+1)
 
-def get_device_num():
-    if DEVICE_TYPE == "xpu":
-        return torch.xpu.device_count()
-    else:
-        return torch.cuda.device_count()
-
 # Ignore logging messages
 class HideLoggingMessage(logging.Filter):
     __slots__ = "text",
@@ -746,8 +740,7 @@ def get_statistics():
         pass
     pass
     try:
-        devices = get_device_num()
-        _get_statistics(f"{devices if devices <= 8 else 9}")
+        _get_statistics(f"{DEVICE_COUNT if DEVICE_COUNT <= 8 else 9}")
     except:
         pass
     if disabled: enable_progress_bars()
@@ -773,7 +766,7 @@ def get_statistics():
 )
 exec(BitsAndBytesConfig__init__, globals())
 
-if get_device_num() == 1:
+if DEVICE_COUNT == 1:
     from accelerate.utils.dataclasses import DistributedType
     def _prepare_backend(self, *args, **kwargs): return None, DistributedType.NO
     import accelerate.state
@@ -781,6 +774,36 @@ def _prepare_backend(self, *args, **kwargs): return None, DistributedType.NO
     accelerate.accelerator.Accelerator.distributed_type = lambda *args, **kwargs: DistributedType.NO
 pass
 
+# to move multiple tensors to the same device
+def move_to_device(target_device, *tensors):
+    """
+    Move multiple tensors to target device if they're not already there.
+
+    Args:
+        target_device: The target device to move tensors to
+        *tensors: Variable number of tensors to potentially move
+
+    Returns:
+        tuple: The tensors on the target device (same objects if already on device, new if moved)
+    """
+    if isinstance(target_device, int):
+        target_device = torch.device(target_device)
+    elif isinstance(target_device, str):
+        # if string we expect it to be a device name like "cuda:0"
+        target_device = torch.device(target_device)
+    elif isinstance(target_device, torch.device):
+        pass
+    else:
+        raise ValueError(f"Invalid target device: {target_device}")
+    pass
+    moved_tensors = []
+    for tensor in tensors:
+        if tensor.device != target_device:
+            moved_tensors.append(tensor.to(target_device))
+        else:
+            moved_tensors.append(tensor)
+    return tuple(moved_tensors) if len(moved_tensors) > 1 else moved_tensors[0]
+
 import transformers.utils.quantization_config
 transformers.utils.quantization_config.BitsAndBytesConfig.__init__ = _BitsAndBytesConfig__init__
 # =============================================
diff --git a/unsloth/models/cohere.py b/unsloth/models/cohere.py
index bfa833be9..d4691fb5d 100644
--- a/unsloth/models/cohere.py
+++ b/unsloth/models/cohere.py
@@ -78,7 +78,7 @@ def CohereAttention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -254,6 +254,7 @@ def CohereAttention_fast_forward_inference(
     do_prefill = False,
     attention_mask = None,
 ):
+
     Xn = hidden_states
     bsz, _, hd = hidden_states.size()
     K1, V1 = past_key_value
@@ -281,14 +282,14 @@ def CohereAttention_fast_forward_inference(
         self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = "cuda:0")
         self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = "cuda:0")
         self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = "cuda:0")
-        
+
         # Mistral Nemo 12b has weird dimensions
         if attention_size != hidden_size:
             self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = "cuda:0")
         else:
             self.temp_O = self.temp_QA[1][:,:,:hidden_size]
         pass
-        
+
         self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = "cuda:0")
         self.scalar = 1.0 / math_sqrt(self.head_dim)
         self.half_head_dim = head_dim // 2
@@ -320,7 +321,7 @@ def CohereAttention_fast_forward_inference(
 
     # cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
     # Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
-    cos, sin = self.rotary_emb.get_cached(kv_seq_len)
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
     cos = cos[position_ids].unsqueeze(1)
     sin = sin[position_ids].unsqueeze(1)
     h = self.half_head_dim
@@ -338,7 +339,7 @@ def CohereAttention_fast_forward_inference(
     torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
@@ -397,7 +398,7 @@ def CohereModel_fast_forward_inference(
     position_ids,
     attention_mask = None,
 ):
-    out_weight = torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = "cuda:0")
+    out_weights = tuple(torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = torch.device(x)) for x in range(DEVICE_COUNT))
     input_ids = input_ids[:,:self.max_seq_length]
     hidden_states = self.model.embed_tokens(input_ids)
     hidden_states = hidden_states.to(self.config.torch_dtype)
@@ -417,8 +418,12 @@ def CohereModel_fast_forward_inference(
 
     next_decoder_cache = []
     for idx, decoder_layer in enumerate(self.model.layers):
+        device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
+        hidden_states, position_ids = move_to_device(
+            device_index, hidden_states, position_ids
+        )
         residual = hidden_states
-        hidden_states = fast_layernorm_inference(decoder_layer.input_layernorm, hidden_states, out_weight)
+        hidden_states = fast_layernorm_inference(decoder_layer.input_layernorm, hidden_states, out_weights[device_index])
         hidden_states_attention, present_key_value = CohereAttention_fast_forward_inference(
             decoder_layer.self_attn,
             hidden_states = hidden_states,
@@ -435,7 +440,7 @@ def CohereModel_fast_forward_inference(
 
         next_decoder_cache.append(present_key_value)
     pass
-    hidden_states = fast_layernorm_inference(self.model.norm, hidden_states, out_weight)
+    hidden_states = fast_layernorm_inference(self.model.norm, hidden_states, out_weights[device_index])
 
     return BaseModelOutputWithPast(
         last_hidden_state = hidden_states,
@@ -468,7 +473,7 @@ def pre_patch():
         CohereForCausalLM    .forward = CausalLM_fast_forward(CohereModel_fast_forward_inference)
         PeftModelForCausalLM .forward = PeftModel_fast_forward
         fix_prepare_inputs_for_generation(CohereForCausalLM)
-        
+
         import transformers.models.cohere.modeling_cohere
         transformers.models.cohere.modeling_cohere.CohereRotaryEmbedding = LlamaRotaryEmbedding
         return
diff --git a/unsloth/models/falcon_h1.py b/unsloth/models/falcon_h1.py
index 8978e4db0..2cbb78f8a 100644
--- a/unsloth/models/falcon_h1.py
+++ b/unsloth/models/falcon_h1.py
@@ -69,7 +69,7 @@ def FalconH1Attention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -110,12 +110,13 @@ def FalconH1Attention_fast_forward(
         # Extend RoPE dynamically to fit in VRA
         rotary_emb = self.rotary_emb
         rotary_emb.extend_rope_embedding(V, seq_len = kv_seq_len)
+        device_index = Q.device.index
 
         if position_ids is None:
             # Useful for LongRoPE
-            cos, sin = rotary_emb.get_cached(kv_seq_len)
+            cos, sin = rotary_emb.get_cached(kv_seq_len, device_index)
         else:
-            cos, sin = rotary_emb(V, seq_len = kv_seq_len)
+            cos, sin = rotary_emb.get_cached(kv_seq_len, device_index)
     Q, K = fast_rope_embedding(Q, K, cos, sin)
 
     if past_key_value is not None:
@@ -245,14 +246,14 @@ def FalconH1Attention_fast_forward_inference(
         self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
         self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = device)
         self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)
-        
+
         # Mistral Nemo 12b has weird dimensions
         if attention_size != hidden_size:
             self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)
         else:
             self.temp_O = self.temp_QA[1][:,:,:hidden_size]
         pass
-        
+
         self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = device)
         self.scalar = 1.0 / math_sqrt(self.head_dim)
         self.half_head_dim = head_dim // 2
@@ -280,7 +281,7 @@ def FalconH1Attention_fast_forward_inference(
     # Need to do it prior 2 steps before hitting full on short KV cache
     # or else error
     self.rotary_emb.extend_rope_embedding(Vn, seq_len + 2)
-    cos, sin = self.rotary_emb.get_cached(kv_seq_len)
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
     cos = cos[position_ids].unsqueeze(1)
     sin = sin[position_ids].unsqueeze(1)
     h = self.half_head_dim
@@ -298,7 +299,7 @@ def FalconH1Attention_fast_forward_inference(
     RH_K[:,:,:,:h].neg_() #torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
@@ -580,7 +581,7 @@ def _fast_prepare_inputs_for_generation(
     **kwargs,):
     # Overwitten -- has a unique cache type, `FalconHybridMambaAttentionDynamicCache`
     empty_past_kv = past_key_values is None
-    
+
     # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens
     # Exception 1: when passing input_embeds, input_ids may be missing entries
     # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here
diff --git a/unsloth/models/gemma.py b/unsloth/models/gemma.py
index 8ad1c7e62..e43b205ec 100644
--- a/unsloth/models/gemma.py
+++ b/unsloth/models/gemma.py
@@ -149,7 +149,7 @@ def GemmaModel_fast_forward_inference(
     position_ids,
     attention_mask = None,
 ):
-    out_weight = torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = "cuda:0")
+    out_weights = tuple(torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = torch.device(x)) for x in range(DEVICE_COUNT))
     input_ids = input_ids[:,:self.max_seq_length]
     hidden_states = self.model.embed_tokens(input_ids)
     hidden_states = hidden_states.to(self.config.torch_dtype)
@@ -170,8 +170,13 @@ def GemmaModel_fast_forward_inference(
 
     next_decoder_cache = []
     for idx, decoder_layer in enumerate(self.model.layers):
+        device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
+        hidden_states, position_ids = move_to_device(
+            device_index, hidden_states, position_ids
+        )
+
         residual = hidden_states
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.input_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.input_layernorm, hidden_states, out_weights[device_index])
         hidden_states, present_key_value = LlamaAttention_fast_forward_inference(
             decoder_layer.self_attn,
             hidden_states = hidden_states,
@@ -183,13 +188,13 @@ def GemmaModel_fast_forward_inference(
         hidden_states += residual
 
         residual = hidden_states
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_attention_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_attention_layernorm, hidden_states, out_weights[device_index])
         hidden_states = fast_geglu_inference(decoder_layer.mlp, hidden_states)
         hidden_states += residual
 
         next_decoder_cache.append(present_key_value)
     pass
-    hidden_states = fast_rms_layernorm_inference_gemma(self.model.norm, hidden_states, out_weight)
+    hidden_states = fast_rms_layernorm_inference_gemma(self.model.norm, hidden_states, out_weights[device_index])
 
     return BaseModelOutputWithPast(
         last_hidden_state = hidden_states,
@@ -224,9 +229,16 @@ def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=
         self.base = base
         # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
         self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
+        self.multi_gpu_cos_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_sin_cached = [None]*DEVICE_COUNT
 
         # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
+        for device in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(seq_len=self.current_rope_size, device=torch.device(device), dtype=torch.get_default_dtype())
+
+        # dummy so that patch_utils doesn't fail for now
+        self.cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
@@ -245,32 +257,38 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
 
         emb = torch.cat((radians_new, radians_new), dim = -1)
         # We must do RoPE in float32!
-        cos = emb.cos().to(device = "cuda", non_blocking = True)#, dtype = dtype)
-        sin = emb.sin().to(device = "cuda", non_blocking = True)#, dtype = dtype)
-        self.register_buffer("cos_cached", cos, persistent = False)
-        self.register_buffer("sin_cached", sin, persistent = False)
+        cos = emb.cos().to(device = device, non_blocking = True)#, dtype = dtype)
+        sin = emb.sin().to(device = device, non_blocking = True)#, dtype = dtype)
+        self.multi_gpu_cos_cached[device.index] = cos
+        self.multi_gpu_sin_cached[device.index] = sin
+        return cos, sin
     pass
 
     def forward(self, x, position_ids=None, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.current_rope_size:
+        if seq_len is not None and seq_len > self.current_rope_size:
             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
 
+        device_index = x.device.index
+
         return (
-            self.cos_cached[:seq_len].to(dtype=x.dtype),
-            self.sin_cached[:seq_len].to(dtype=x.dtype),
+            self.multi_gpu_cos_cached[device_index][:seq_len],
+            self.multi_gpu_sin_cached[device_index][:seq_len],
         )
     pass
 
-    def get_cached(self, seq_len = None):
-        return self.cos_cached, self.sin_cached
+    def get_cached(self, seq_len = None, device_index = None):
+        if device_index is None:
+            device_index = torch.cuda.current_device()
+        return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]
     pass
 
     def extend_rope_embedding(self, x, seq_len):
         if seq_len <= self.current_rope_size: return
         # Iteratively grow by increments of 8192
         self.current_rope_size = math.ceil(seq_len / 8192) * 8192
-        self._set_cos_sin_cache(self.current_rope_size, device = "cuda", dtype = x.dtype)
+        for device in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(self.current_rope_size, device = torch.device(device), dtype = x.dtype)
     pass
 pass
 
@@ -288,7 +306,7 @@ def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
-# Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
+        # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
         self.current_rope_size = seq_len
 
@@ -304,10 +322,11 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
 
         emb = torch.cat((radians_new, radians_new), dim = -1)
         # We must do RoPE in float32!
-        cos = emb.cos().to(device = "cuda", non_blocking = True)#, dtype = dtype)
-        sin = emb.sin().to(device = "cuda", non_blocking = True)#, dtype = dtype)
-        self.register_buffer("cos_cached", cos, persistent = False)
-        self.register_buffer("sin_cached", sin, persistent = False)
+        cos = emb.cos().to(device = device, non_blocking = True)#, dtype = dtype)
+        sin = emb.sin().to(device = device, non_blocking = True)#, dtype = dtype)
+        self.multi_gpu_cos_cached[device.index] = cos
+        self.multi_gpu_sin_cached[device.index] = sin
+        return cos, sin
     pass
 pass
 
diff --git a/unsloth/models/gemma2.py b/unsloth/models/gemma2.py
index 23b91ff6f..5597995b0 100644
--- a/unsloth/models/gemma2.py
+++ b/unsloth/models/gemma2.py
@@ -84,7 +84,7 @@ def Gemma2Attention_fast_forward(
     padding_mask:         Optional[torch.LongTensor] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -113,12 +113,13 @@ def Gemma2Attention_fast_forward(
     if past_key_value is not None:
         kv_seq_len += past_key_value[0].shape[-2]
 
+    device_index = Q.device.index
     if position_ids is None:
-        cos = self.rotary_emb.cos_cached
-        sin = self.rotary_emb.sin_cached
+        cos = self.rotary_emb.multi_gpu_cos_cached[device_index]
+        sin = self.rotary_emb.multi_gpu_sin_cached[device_index]
         Q, K = fast_rope_embedding(Q, K, cos, sin)
     else:
-        cos, sin = self.rotary_emb(V, seq_len = kv_seq_len)
+        cos, sin = self.rotary_emb.get_cached(kv_seq_len, device_index)
         Q, K = inplace_rope_embedding(Q, K, cos, sin, position_ids)
     pass
 
@@ -281,7 +282,7 @@ def Gemma2Attention_fast_forward_inference(
         # Only for Gemma2
         self.temp_O  = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)
         self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = device)
-        
+
         # See https://github.com/google/gemma_pytorch/commit/03e657582d17cb5a8617ebf333c1c16f3694670e
         # Gemma 9b should use 256 and not 224 (hs / nah). 27b uses the below
         # We default to using the config file itself
@@ -307,8 +308,9 @@ def Gemma2Attention_fast_forward_inference(
 
     # cos, sin = self.rotary_emb(Vn, seq_len = kv_seq_len)
     # Qn, Kn = inplace_rope_embedding(Qn, Kn, cos, sin, position_ids)
-    cos = self.rotary_emb.cos_cached[position_ids].unsqueeze(1)
-    sin = self.rotary_emb.sin_cached[position_ids].unsqueeze(1)
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
+    cos = cos[position_ids].unsqueeze(1)
+    sin = sin[position_ids].unsqueeze(1)
     h = self.half_head_dim
 
     RH_Q = self.RH_Q
@@ -324,7 +326,7 @@ def Gemma2Attention_fast_forward_inference(
     torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
@@ -386,7 +388,7 @@ def Gemma2Model_fast_forward_inference(
     position_ids,
     attention_mask = None,
 ):
-    out_weight = torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = "cuda:0")
+    out_weights = tuple(torch.empty_like(self.model.layers[0].input_layernorm.weight, dtype = torch.float32, device = torch.device(x)) for x in range(DEVICE_COUNT))
     input_ids = input_ids[:,:self.max_seq_length]
     hidden_states = self.model.embed_tokens(input_ids)
     hidden_states = hidden_states.to(self.config.torch_dtype)
@@ -422,10 +424,17 @@ def Gemma2Model_fast_forward_inference(
     next_decoder_cache = []
     for idx, decoder_layer in enumerate(self.model.layers):
 
+        # For pipeline parallelism, we need to move all tensors to the same device
+        # note that this movement is once per GPU in PP
+        device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
+        hidden_states, position_ids = move_to_device(
+            device_index, hidden_states, position_ids
+        )
+
         use_sliding_window = idx % 2 == 0
 
         residual = hidden_states
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.input_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.input_layernorm, hidden_states, out_weights[device_index])
         hidden_states, present_key_value = Gemma2Attention_fast_forward_inference(
             decoder_layer.self_attn,
             hidden_states = hidden_states,
@@ -435,18 +444,18 @@ def Gemma2Model_fast_forward_inference(
             do_prefill = not hasattr(decoder_layer.self_attn, "paged_attention"),
             use_sliding_window = use_sliding_window,
         )
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_attention_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_attention_layernorm, hidden_states, out_weights[device_index])
         hidden_states += residual
 
         residual = hidden_states
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer. pre_feedforward_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer. pre_feedforward_layernorm, hidden_states, out_weights[device_index])
         hidden_states = fast_geglu_inference(decoder_layer.mlp, hidden_states)
-        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_feedforward_layernorm, hidden_states, out_weight)
+        hidden_states = fast_rms_layernorm_inference_gemma(decoder_layer.post_feedforward_layernorm, hidden_states, out_weights[device_index])
         hidden_states += residual
 
         next_decoder_cache.append(present_key_value)
     pass
-    hidden_states = fast_rms_layernorm_inference_gemma(self.model.norm, hidden_states, out_weight)
+    hidden_states = fast_rms_layernorm_inference_gemma(self.model.norm, hidden_states, out_weights[device_index])
 
     return BaseModelOutputWithPast(
         last_hidden_state = hidden_states,
@@ -479,7 +488,7 @@ def pre_patch():
         Gemma2ForCausalLM    .forward = CausalLM_fast_forward(Gemma2Model_fast_forward_inference)
         PeftModelForCausalLM .forward = PeftModel_fast_forward
         fix_prepare_inputs_for_generation(Gemma2ForCausalLM)
-        
+
         # Solves https://github.com/unslothai/unsloth/issues/168
         # Static KV Cache was introduced in 4.38.0, causing training to be much slower.
         # Inferene can now be CUDAGraphed, but we shall retain the old rotary embeddings.
diff --git a/unsloth/models/granite.py b/unsloth/models/granite.py
index 243922fc1..a3d79c833 100644
--- a/unsloth/models/granite.py
+++ b/unsloth/models/granite.py
@@ -71,7 +71,7 @@ def GraniteAttention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -162,7 +162,7 @@ def GraniteAttention_fast_forward(
         # Go back to (batch_size, seq_len, n_heads, head_dim)
         A = A.transpose(1, 2).contiguous()
     pass
-    
+
     attn_output = A.reshape(bsz, q_len, n_heads*head_dim)
     attn_output = self.apply_o(self, attn_output)
     attn_weights = None
@@ -256,7 +256,7 @@ def GraniteAttention_fast_forward_inference(
     use_sliding_window = False,
     position_embeddings : Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
 ):
-    
+
     assert position_embeddings is not None, f"Granite model requires position embeddings to be specified"
 
     Xn = hidden_states
@@ -326,7 +326,7 @@ def GraniteAttention_fast_forward_inference(
     torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
@@ -349,7 +349,7 @@ def GraniteAttention_fast_forward_inference(
 
     Qn *= self.scaling
     A = torch_matmul(Qn, Kn.transpose(2, 3), out = self.attention[:,:,:,:cached_len])
-    
+
     # if attention_mask is not None: A += attention_mask # Must add attention_mask for batched
 
     A[:] = torch_nn_functional_softmax(A, dim = -1, dtype = torch.float32)#.to(A.dtype)
@@ -395,10 +395,14 @@ def GraniteModel_fast_forward_inference(
         attention_mask = None
     pass
 
-    position_embeddings = self.model.rotary_emb(hidden_states, position_ids, self.max_seq_length)
+    position_embeddings = self.model.rotary_emb.get_cached(self.max_seq_length, hidden_states.device.index)
 
     next_decoder_cache = []
     for idx, decoder_layer in enumerate(self.model.layers):
+        device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
+        hidden_states, position_ids = move_to_device(
+            device_index, hidden_states, position_ids
+        )
 
         residual = hidden_states
         hidden_states = fast_rms_layernorm_inference(decoder_layer.input_layernorm, hidden_states)
@@ -532,7 +536,7 @@ def post_patch(model, tokenizer):
 
                 elif hasattr(module, "short_cos_cached") and \
                     (module.short_cos_cached.dtype != correct_dtype):
-                    
+
                     module.short_cos_cached = module.short_cos_cached.to(correct_dtype)
                     module.short_sin_cached = module.short_sin_cached.to(correct_dtype)
                 pass
@@ -547,4 +551,3 @@ def post_patch(model, tokenizer):
         return model, tokenizer
     pass
 pass
-
diff --git a/unsloth/models/llama.py b/unsloth/models/llama.py
index 1253e641e..db0e8843c 100644
--- a/unsloth/models/llama.py
+++ b/unsloth/models/llama.py
@@ -20,11 +20,12 @@
 from ._utils import *
 from ._utils import patch_unsloth_smart_gradient_checkpointing
 from ._utils import __version__
+from ._utils import move_to_device
 from torch.nn.functional import scaled_dot_product_attention
 from transformers import __version__ as transformers_version
 from unsloth_zoo.utils import Version, _get_dtype
 from unsloth_zoo.peft_utils import SKIP_QUANTIZATION_MODULES
-from unsloth import DEVICE_TYPE
+from unsloth import DEVICE_TYPE, DEVICE_COUNT
 
 transformers_version = Version(transformers_version)
 # Transformers moved rotary embeddings out of all attention layers
@@ -121,12 +122,12 @@ def _fast_prepare_inputs_for_generation(self, input_ids, attention_mask=None, **
         else:
             bs, cache_length = input_ids.shape
             input_ids = input_ids[:,[-1]]
-            
+
             # Get to the base model
             base_model = self
             if hasattr(base_model, 'base_model_prefix'):
                 base_model = getattr(base_model, base_model.base_model_prefix)
-                
+
             if hasattr(base_model, "_prepare_4d_causal_attention_mask_with_cache_position"):
                 def needs_device_kw(fn) -> bool:
                     try:
@@ -243,14 +244,14 @@ def LlamaAttention_fast_forward_inference(
         self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
         self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = device)
         self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)
-        
+
         # Mistral Nemo 12b has weird dimensions
         if attention_size != hidden_size:
             self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)
         else:
             self.temp_O = self.temp_QA[1][:,:,:hidden_size]
         pass
-        
+
         self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = device)
         self.scalar = 1.0 / math_sqrt(self.head_dim)
         self.half_head_dim = head_dim // 2
@@ -274,7 +275,7 @@ def LlamaAttention_fast_forward_inference(
     # Need to do it prior 2 steps before hitting full on short KV cache
     # or else error
     self.rotary_emb.extend_rope_embedding(Vn, seq_len + 2)
-    cos, sin = self.rotary_emb.get_cached(kv_seq_len)
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
     cos = cos[position_ids].unsqueeze(1)
     sin = sin[position_ids].unsqueeze(1)
     h = self.half_head_dim
@@ -292,7 +293,7 @@ def LlamaAttention_fast_forward_inference(
     RH_K[:,:,:,:h].neg_() #torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
@@ -361,10 +362,10 @@ def fast_swiglu_inference(self, X, temp_gate = None, temp_up = None, gate_multip
     # temp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = "cuda:0")
 
     gate = fast_linear_forward(self.gate_proj, X, out = temp_gate)
-    
+
     if gate_multiplier is not None:
         gate *= gate_multiplier
-    
+
     up   = fast_linear_forward(self.  up_proj, X, out = temp_up)
 
     gate = torch_nn_functional_silu(gate, inplace = True)
@@ -447,7 +448,7 @@ def LlamaAttention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -483,11 +484,12 @@ def LlamaAttention_fast_forward(
         rotary_emb = self.rotary_emb
         rotary_emb.extend_rope_embedding(V, seq_len = kv_seq_len)
 
-        if position_ids is None:
-            # Useful for LongRoPE
-            cos, sin = rotary_emb.get_cached(kv_seq_len)
-        else:
-            cos, sin = rotary_emb(V, seq_len = kv_seq_len)
+        # if position_ids is None:
+        #     # Useful for LongRoPE
+        #     cos, sin = rotary_emb.get_cached(kv_seq_len, device = Q.device)
+        # else:
+        #     cos, sin = rotary_emb.get_cached(seq_len = kv_seq_len, device = Q.device)
+        cos, sin = rotary_emb.get_cached(kv_seq_len, Q.device.index)
 
     # Q, K = (
     #     fast_rope_embedding(Q, K, cos, sin)
@@ -672,7 +674,7 @@ def LlamaModel_fast_forward(
     return_dict:          Optional[bool] = None,
     *args, **kwargs,
 ) -> Union[Tuple, BaseModelOutputWithPast]:
-    
+
     output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
     assert(output_attentions is False)
     output_hidden_states = (
@@ -707,7 +709,7 @@ def LlamaModel_fast_forward(
             inputs_embeds = inputs_embeds[:,:self.max_seq_length,:]
         pass
     pass
-    
+
     past_key_values_length = 0
 
     if past_key_values is not None:
@@ -794,7 +796,7 @@ def LlamaModel_fast_forward(
     # Ignore attention_mask
     if attention_mask is None:
         padding_mask = None
-    elif self.training and os.environ.get("UNSLOTH_KEEP_PADDING", "0") != '1':    
+    elif self.training and os.environ.get("UNSLOTH_KEEP_PADDING", "0") != '1':
         attention_mask = None
         padding_mask = None
     else:
@@ -911,7 +913,7 @@ def LlamaModel_fast_forward(
         # Also, transformers 4.45.0 supports granite but with the attention refactor (it always had the refactor)
         # unsloth's check for granite too has "version >= 4.45.0 (rightly so)".
         # so let granite always use the attention refactor implementation.
-        position_embeddings = self.rotary_emb(hidden_states, position_ids, self.config.max_position_embeddings)
+        position_embeddings = self.rotary_emb.get_cached(self.config.max_position_embeddings, hidden_states.device.index)
     else:
         position_embeddings = None
 
@@ -1021,7 +1023,7 @@ def LlamaModel_fast_forward_inference_custom(
         XX, XX2 = _XX[0], _XX[1]
         variance = torch.empty((bsz, q_len, 1), dtype = torch.float32, device = f"{DEVICE_TYPE}:0")
         temp_mlp = torch.empty((2, bsz, 1, mlp_size), dtype = X.dtype, device = f"{DEVICE_TYPE}:0")
-        temp_gate, temp_up = temp_mlp[0], temp_mlp[1]
+        temp_gates, temp_ups = tuple(temp_mlp[0].to(torch.device(x)) for x in range(DEVICE_COUNT)), tuple(temp_mlp[1].to(torch.device(x)) for x in range(DEVICE_COUNT))
 
         seq_len = past_key_values[0][0].shape[-2]
         if bsz != 1:
@@ -1039,6 +1041,10 @@ def LlamaModel_fast_forward_inference_custom(
         next_decoder_cache = []
 
         for idx, decoder_layer in enumerate(self.model.layers):
+            device_index = getattr(decoder_layer, "_per_layer_device_index", 0)
+            X, residual, position_ids = move_to_device(
+                device_index, X, residual, position_ids
+            )
             residual.copy_(X) # residual = X
             X = fast_rms_layernorm_inference(
                 decoder_layer.input_layernorm,
@@ -1068,8 +1074,8 @@ def LlamaModel_fast_forward_inference_custom(
             X = mlp_fast_forward_inference(
                 decoder_layer.mlp,
                 X,
-                temp_gate = temp_gate,
-                temp_up = temp_up,
+                temp_gate = temp_gates[device_index],
+                temp_up = temp_ups[device_index],
             )
             X += residual
 
@@ -1154,7 +1160,7 @@ def _CausalLM_fast_forward(
         logit_scaling     = getattr(self.config, "logit_scale", 0)
         dtype = lm_head.dtype
         num_logits_to_keep = max(num_logits_to_keep, logits_to_keep)
-        
+
         # Move items to same device as lm_head
         hidden_states = hidden_states.to(lm_head_device)
         if labels is not None: labels = labels.to(lm_head_device)
@@ -1181,7 +1187,7 @@ def _CausalLM_fast_forward(
             RETURN_LOGITS = os.environ.get("UNSLOTH_RETURN_LOGITS", "0") == "1"
             # < 1024 Normal Unsloth uses less VRAM!
             if bsz*q_len <= 1024: RETURN_LOGITS = True
-            
+
             if not RETURN_LOGITS and HAS_CUT_CROSS_ENTROPY and labels is not None:
 
                 n_items = kwargs.get("num_items_in_batch", None) or kwargs.get("n_items", None)
@@ -1295,15 +1301,15 @@ def PeftModel_fast_forward(
     **kwargs,
 ):
     is_classification = "Classification" in str(type(self.base_model.model))
-    if is_classification: 
+    if is_classification:
         return self.base_model(
             input_ids = input_ids,
-            attention_mask = attention_mask, 
-            inputs_embeds = inputs_embeds, 
-            labels = labels, 
+            attention_mask = attention_mask,
+            inputs_embeds = inputs_embeds,
+            labels = labels,
             output_attentions = output_attentions,
-            output_hidden_states = output_hidden_states, 
-            return_dict = return_dict, 
+            output_hidden_states = output_hidden_states,
+            return_dict = return_dict,
             **kwargs,
         )
     else:
@@ -1351,9 +1357,16 @@ def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=
         self.base = base
         # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
         self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
+        self.multi_gpu_cos_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_sin_cached = [None]*DEVICE_COUNT
 
         # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
+        for device_idx in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(seq_len=self.current_rope_size, device=torch.device(device_idx), dtype=torch.get_default_dtype())
+
+        # dummy so that patch_utils doesn't fail for now
+        self.cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
@@ -1368,30 +1381,37 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
         freqs = torch.outer(t, inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
         emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
+        cos = emb.cos().to(dtype=dtype, device=device, non_blocking=True)
+        sin = emb.sin().to(dtype=dtype, device=device, non_blocking=True)
+        self.multi_gpu_cos_cached[device.index] = cos
+        self.multi_gpu_sin_cached[device.index] = sin
+        return cos, sin
     pass
 
     def forward(self, x, position_ids=None, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.current_rope_size:
+        if seq_len is not None and seq_len > self.current_rope_size:
             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
 
+        device_index = x.device.index
         return (
-            self.cos_cached[:seq_len].to(dtype = x.dtype),
-            self.sin_cached[:seq_len].to(dtype = x.dtype),
+            self.multi_gpu_cos_cached[device_index][:seq_len],
+            self.multi_gpu_sin_cached[device_index][:seq_len],
         )
     pass
 
-    def get_cached(self, seq_len = None):
-        return self.cos_cached, self.sin_cached
+    def get_cached(self, seq_len = None, device_index = None):
+        if device_index is None:
+            device_index = torch.cuda.current_device()
+        return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]
     pass
 
     def extend_rope_embedding(self, x, seq_len):
         if seq_len <= self.current_rope_size: return
         # Iteratively grow by increments of 8192
         self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
-        self._set_cos_sin_cache(self.current_rope_size, device = DEVICE_TYPE, dtype = x.dtype)
+        for device_idx in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(self.current_rope_size, device = torch.device(device_idx), dtype = x.dtype)
     pass
 pass
 
@@ -1419,8 +1439,11 @@ def _set_cos_sin_cache(self, seq_len, device, dtype):
         freqs = torch.outer(t, inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
         emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
+        cos = emb.cos().to(dtype=dtype, device=device, non_blocking=True)
+        sin = emb.sin().to(dtype=dtype, device=device, non_blocking=True)
+        self.multi_gpu_cos_cached[device.index] = cos
+        self.multi_gpu_sin_cached[device.index] = sin
+        return cos, sin
     pass
 pass
 
@@ -1446,6 +1469,8 @@ def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=
         self.base = base
         # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
         self.current_rope_size = min(4 * 8192, self.max_position_embeddings)
+        self.multi_gpu_cos_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_sin_cached = [None]*DEVICE_COUNT
 
         # Normal Llama-3 RoPE
         inv_freq = 1.0 / (
@@ -1455,21 +1480,54 @@ def __init__(self, dim = None, max_position_embeddings=2048, base=10000, device=
         self.register_buffer("inv_freq", inv_freq, persistent = False)
 
         # Build here to make `torch.jit.trace` work.
-        self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
+        for device_idx in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(seq_len=self.current_rope_size, device=torch.device(device_idx), dtype=torch.get_default_dtype())
+
+        # dummy so that patch_utils doesn't fail for now
+        self.cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
         # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
         self.current_rope_size = seq_len
-        
+
         t = torch.arange(self.current_rope_size, device=self.inv_freq.device, dtype=torch.int64).float()
 
         freqs = torch.outer(t, self.inv_freq)
         # Different from paper, but it uses a different permutation in order to obtain the same calculation
         emb = torch.cat((freqs, freqs), dim=-1)
-        self.register_buffer("cos_cached", emb.cos().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
-        self.register_buffer("sin_cached", emb.sin().to(dtype=dtype, device=device, non_blocking=True), persistent=False)
+        cos = emb.cos().to(dtype=dtype, device=device, non_blocking=True)
+        sin = emb.sin().to(dtype=dtype, device=device, non_blocking=True)
+        self.multi_gpu_cos_cached[device.index] = cos
+        self.multi_gpu_sin_cached[device.index] = sin
+        return cos, sin
+    pass
+
+    def forward(self, x, position_ids=None, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len is not None and seq_len > self.current_rope_size:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+        device_index = x.device.index
+        return (
+            self.multi_gpu_cos_cached[device_index][:seq_len],
+            self.multi_gpu_sin_cached[device_index][:seq_len],
+        )
+    pass
+
+    def get_cached(self, seq_len = None, device_index = None):
+        if device_index is None:
+            device_index = torch.cuda.current_device()
+        return self.multi_gpu_cos_cached[device_index], self.multi_gpu_sin_cached[device_index]
+    pass
+
+    def extend_rope_embedding(self, x, seq_len):
+        if seq_len <= self.current_rope_size: return
+        # Iteratively grow by increments of 8192
+        self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
+        for device_idx in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(self.current_rope_size, device = torch.device(device_idx), dtype = x.dtype)
     pass
 
     # From https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/api/model.py#L41
@@ -1497,28 +1555,6 @@ def apply_scaling(self, freqs: torch.Tensor):
                 new_freqs.append((1 - smooth) * freq / scale_factor + smooth * freq)
         return torch.tensor(new_freqs, dtype=freqs.dtype, device=freqs.device)
     pass
-
-    def forward(self, x, position_ids=None, seq_len=None):
-        # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.current_rope_size:
-            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
-
-        return (
-            self.cos_cached[:seq_len].to(dtype = x.dtype),
-            self.sin_cached[:seq_len].to(dtype = x.dtype),
-        )
-    pass
-
-    def get_cached(self, seq_len = None):
-        return self.cos_cached, self.sin_cached
-    pass
-
-    def extend_rope_embedding(self, x, seq_len):
-        if seq_len <= self.current_rope_size: return
-        # Iteratively grow by increments of 8192
-        self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
-        self._set_cos_sin_cache(self.current_rope_size, device = DEVICE_TYPE, dtype = x.dtype)
-    pass
 pass
 
 
@@ -1554,6 +1590,10 @@ def __init__(self,
         self.base = base
         # Dynamic RoPE we first set it to a max of 4 * 8192 tokens then we iteratively grow this
         self.current_rope_size = min(original_max_position_embeddings, self.max_position_embeddings)
+        self.multi_gpu_short_cos_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_short_sin_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_long_cos_cached = [None]*DEVICE_COUNT
+        self.multi_gpu_long_sin_cached = [None]*DEVICE_COUNT
 
         # Long RoPE similar to RoPE except short sequences have 1 cos / sin
         # and long sequences have another cos / sin
@@ -1575,64 +1615,78 @@ def __init__(self,
         # Short and long inv_freq
         self.register_buffer("short_inv_freq", short_inv_freq, persistent = False)
         self.register_buffer("long_inv_freq",  long_inv_freq,  persistent = False)
-        # Build here to make `torch.jit.trace` work.
-        # self._set_cos_sin_cache(seq_len=self.current_rope_size, device=device, dtype=torch.get_default_dtype())
 
-        # Short sequences
+        # Build here to make `torch.jit.trace` work.
+        # Initialize short sequences cache for all devices
         dtype = torch.bfloat16 if is_bfloat16_supported() else torch.float16
         t = torch.arange(original_max_position_embeddings, device=self.short_inv_freq.device, dtype=torch.int64).float()
         freqs = torch.outer(t, self.short_inv_freq)
         emb = torch.cat((freqs, freqs), dim=-1)
-        cos_cached = (emb.cos() * self.scaling_factor).to(dtype=dtype, device=device, non_blocking=True)
-        sin_cached = (emb.sin() * self.scaling_factor).to(dtype=dtype, device=device, non_blocking=True)
-        self.register_buffer("short_cos_cached", cos_cached, persistent=False)
-        self.register_buffer("short_sin_cached", sin_cached, persistent=False)
+
+        for device_idx in range(DEVICE_COUNT):
+            device_obj = torch.device(device_idx)
+            cos_cached = (emb.cos() * self.scaling_factor).to(dtype=dtype, device=device_obj, non_blocking=True)
+            sin_cached = (emb.sin() * self.scaling_factor).to(dtype=dtype, device=device_obj, non_blocking=True)
+            self.multi_gpu_short_cos_cached[device_idx] = cos_cached
+            self.multi_gpu_short_sin_cached[device_idx] = sin_cached
+
+        # dummy so that patch_utils doesn't fail for now
+        self.short_cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.short_sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.long_cos_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
+        self.long_sin_cached = torch.empty(1, device=torch.cuda.current_device(), dtype=torch.get_default_dtype())
     pass
 
     def _set_cos_sin_cache(self, seq_len, device, dtype):
         # Note: on the original Llama codebase, these tensors are created on the target device (and not on CPU) and
         # in FP32. They are applied (multiplied) in FP32 as well.
         self.current_rope_size = seq_len
-        
+
         t = torch.arange(self.current_rope_size, device=self.long_inv_freq.device, dtype=torch.int64).float()
         # Long sequences
         freqs = torch.outer(t, self.long_inv_freq)
         emb = torch.cat((freqs, freqs), dim=-1)
         cos_cached = (emb.cos() * self.scaling_factor).to(dtype=dtype, device=device, non_blocking=True)
         sin_cached = (emb.sin() * self.scaling_factor).to(dtype=dtype, device=device, non_blocking=True)
-        self.register_buffer("long_cos_cached", cos_cached, persistent=False)
-        self.register_buffer("long_sin_cached", sin_cached, persistent=False)
+        self.multi_gpu_long_cos_cached[device.index] = cos_cached
+        self.multi_gpu_long_sin_cached[device.index] = sin_cached
+        return cos_cached, sin_cached
     pass
 
     def forward(self, x, position_ids=None, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        if seq_len > self.current_rope_size:
+        if seq_len is not None and seq_len > self.current_rope_size:
             self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
 
-        if seq_len < self.original_max_position_embeddings:
+        device_index = x.device.index
+
+        if seq_len is not None and seq_len < self.original_max_position_embeddings:
             return (
-                self.short_cos_cached[:seq_len].to(dtype = x.dtype),
-                self.short_sin_cached[:seq_len].to(dtype = x.dtype),
+                self.multi_gpu_short_cos_cached[device_index][:seq_len],
+                self.multi_gpu_short_sin_cached[device_index][:seq_len],
             )
         else:
             return (
-                self.long_cos_cached[:seq_len].to(dtype = x.dtype),
-                self.long_sin_cached[:seq_len].to(dtype = x.dtype),
+                self.multi_gpu_long_cos_cached[device_index][:seq_len],
+                self.multi_gpu_long_sin_cached[device_index][:seq_len],
             )
         pass
     pass
 
-    def get_cached(self, seq_len = None):
-        if seq_len < self.original_max_position_embeddings:
-            return self.short_cos_cached, self.short_sin_cached
-        return self.long_cos_cached, self.long_sin_cached
+    def get_cached(self, seq_len = None, device_index = None):
+        if device_index is None:
+            device_index = torch.cuda.current_device()
+        if seq_len is not None and seq_len < self.original_max_position_embeddings:
+            return self.multi_gpu_short_cos_cached[device_index], self.multi_gpu_short_sin_cached[device_index]
+        return self.multi_gpu_long_cos_cached[device_index], self.multi_gpu_long_sin_cached[device_index]
     pass
 
     def extend_rope_embedding(self, x, seq_len):
         if seq_len <= self.current_rope_size: return
         # Iteratively grow by increments of 8192
         self.current_rope_size = ((seq_len // 8192) + ((seq_len % 8192) != 0)) * 8192
-        self._set_cos_sin_cache(self.current_rope_size, device = DEVICE_TYPE, dtype = x.dtype)
+        for device_idx in range(DEVICE_COUNT):
+            self._set_cos_sin_cache(self.current_rope_size, device = torch.device(device_idx), dtype = x.dtype)
     pass
 pass
 
@@ -1754,7 +1808,7 @@ def from_pretrained(
         max_lora_rank     = 16,
         disable_log_stats = False,
         unsloth_vllm_standby = False,
-        num_labels =  None, 
+        num_labels =  None,
         **kwargs,
     ):
         os.environ["UNSLOTH_USE_NEW_MODEL"] = "0"
@@ -1787,7 +1841,6 @@ def from_pretrained(
             gpu_stats = torch.cuda.get_device_properties(0)
             gpu_version = torch.version.cuda
             gpu_stats_snippet = f"CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {gpu_version}."
-            num_gpus = torch.cuda.device_count()
 
             from importlib.metadata import version as importlib_version
             try:    vllm_version = f" vLLM: {importlib_version('vllm')}."
@@ -1795,7 +1848,6 @@ def from_pretrained(
         elif DEVICE_TYPE == "xpu":
             gpu_stats = torch.xpu.get_device_properties(0)
             gpu_version = torch.version.xpu
-            num_gpus = torch.xpu.device_count()
             gpu_stats_snippet = f"Intel Toolkit: {gpu_version}."
 
             try:    vllm_version = f" vLLM: {importlib_version('vllm')}."
@@ -1807,7 +1859,7 @@ def from_pretrained(
 
         statistics = \
         f"==((====))==  Unsloth {__version__}: Fast {model_patcher.__name__[4:-5]} patching. Transformers: {transformers_version}.{vllm_version}\n"\
-        f"   {chr(92)}{chr(92)}   /|    {gpu_stats.name}. Num GPUs = {num_gpus}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
+        f"   {chr(92)}{chr(92)}   /|    {gpu_stats.name}. Num GPUs = {DEVICE_COUNT}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
         f"O^O/ {chr(92)}_/ {chr(92)}    Torch: {torch.__version__}. {gpu_stats_snippet} Triton: {triton_version}\n"\
         f"{chr(92)}        /    Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"\
         f' "-____-"     Free license: http://github.com/unslothai/unsloth'
@@ -1827,7 +1879,7 @@ def from_pretrained(
         if old_hf_transfer != "0": os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
         model_patcher.pre_patch()
-        get_statistics() # For debugging - we use a download counter to see if environments are not breaking 
+        get_statistics() # For debugging - we use a download counter to see if environments are not breaking
 
         if dtype is None:
             dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
@@ -1914,7 +1966,7 @@ def from_pretrained(
 
         # Cannot be None, since HF now checks for the config
         if load_in_4bit: kwargs["quantization_config"] = bnb_config
-        
+
         if num_labels is not None:
             model = AutoModelForSequenceClassification.from_pretrained(
                 model_name,
@@ -2012,7 +2064,7 @@ def from_pretrained(
         except:
             raise RuntimeError('Unsloth: Unsuccessfully patched inner_training_loop')
         pass
-        
+
         import transformers.trainer
         items_in_trainer = dir(transformers.trainer)
         good_items = []
@@ -2362,7 +2414,7 @@ def get_peft_model(
                 )
                 loftq_config = LoftQConfig(loftq_bits = 4, loftq_iter = 1)
             pass
-            
+
             if hasattr(model.config, "quantization_config"):
                 raise ValueError(
                     "Unsloth: You are using `loftq` init, yet `load_in_4bit = True` was set.\n"\
@@ -2492,7 +2544,7 @@ def get_peft_model(
 
         is_classification =  "Classification" in str(type(model))
         # Get LoRA
-        # 
+        #
 
         arguments = dict(
             r                   = r,
@@ -2518,7 +2570,7 @@ def get_peft_model(
         input_embeddings_device  = model.get_input_embeddings().weight.device
         if is_classification:
              output_embeddings_device = model.score.weight.device
-        else: 
+        else:
             output_embeddings_device = model.get_output_embeddings().weight.device
 
         if use_gradient_checkpointing == "unsloth":
@@ -2678,7 +2730,7 @@ def patch_peft_model(
             # model.peft_config[active_adapter].revision = f"unsloth"
         pass
 
-        from transformers.trainer import Trainer 
+        from transformers.trainer import Trainer
         if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
             raise RuntimeError("Unsloth: Unsuccessfully patched Trainer! Please file a bug report!")
         pass
@@ -2812,7 +2864,7 @@ def patch_peft_model(
             internal_model.max_seq_length = max_seq_length
             internal_model = internal_model.model
         pass
-        internal_model.max_seq_length = max_seq_length        
+        internal_model.max_seq_length = max_seq_length
 
         # Patch tokenizer to pad to the right
         internal_model = model
diff --git a/unsloth/models/mistral.py b/unsloth/models/mistral.py
index a3e07e3b0..68d4ba43f 100644
--- a/unsloth/models/mistral.py
+++ b/unsloth/models/mistral.py
@@ -51,7 +51,7 @@ def MistralAttention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -83,12 +83,10 @@ def MistralAttention_fast_forward(
     # Extend RoPE dynamically to fit in VRAM
     self.rotary_emb.extend_rope_embedding(V, seq_len = kv_seq_len)
 
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Q.device.index)
     if position_ids is None:
-        cos = self.rotary_emb.cos_cached
-        sin = self.rotary_emb.sin_cached
         Q, K = fast_rope_embedding(Q, K, cos, sin)
     else:
-        cos, sin = self.rotary_emb(V, seq_len = kv_seq_len)
         Q, K = inplace_rope_embedding(Q, K, cos, sin, position_ids)
     pass
 
@@ -162,7 +160,7 @@ def MistralAttention_fast_forward(
         # Go back to (batch_size, seq_len, n_heads, head_dim)
         A = A.transpose(1, 2).contiguous()
     pass
-    
+
     attn_output = A.reshape(bsz, q_len, n_heads*head_dim)
     attn_output = self.apply_o(self, attn_output)
     attn_weights = None
@@ -201,7 +199,7 @@ def MistralForCausalLM_fast_forward(
                 causal_mask = xformers.attn_bias.BlockDiagonalCausalMask\
                     .from_seqlens([q_len]*bsz)\
                     .make_local_attention(window_size = sliding_window)
-        
+
         elif not HAS_XFORMERS and attention_mask is None:
             if sliding_window is None or sliding_window == "null" or sliding_window <= 0 or q_len <= sliding_window:
                 # Fully causal mask
@@ -212,10 +210,10 @@ def MistralForCausalLM_fast_forward(
                 # Sliding window attention
                 q_indices = torch.arange(q_len, device=input_ids.device).view(-1, 1)
                 k_indices = torch.arange(q_len, device=input_ids.device).view(1, -1)
-                
+
                 causal_bool_mask = k_indices <= q_indices
                 window_bool_mask = (q_indices - k_indices) < sliding_window
-                
+
                 mask = torch.where(causal_bool_mask & window_bool_mask, 0.0, -torch.inf)
                 attention_mask = mask[None, None, :, :].expand(bsz, 1, q_len, q_len)
 
@@ -258,7 +256,7 @@ def MistralForCausalLM_fast_forward(
     bsz, q_len, hd = hidden_states.shape
     lm_head = self.lm_head.weight
     lm_head_device = lm_head.device
-    
+
     # Move items to same device as lm_head
     hidden_states = hidden_states.to(lm_head_device)
     if labels is not None: labels = labels.to(lm_head_device)
@@ -301,7 +299,7 @@ def MistralForCausalLM_fast_forward(
             if not return_dict:
                 output = (logits,) + outputs[1:]
                 return (loss,) + output if loss is not None else output
-            
+
             output = CausalLMOutputWithPast(
                 loss = loss,
                 logits = EMPTY_LOGITS,
@@ -390,7 +388,7 @@ def pre_patch():
         MistralForCausalLM    .forward = MistralForCausalLM_fast_forward
         PeftModelForCausalLM  .forward = PeftModel_fast_forward
         fix_prepare_inputs_for_generation(MistralForCausalLM)
-        
+
         # Solves https://github.com/unslothai/unsloth/issues/168
         # Static KV Cache was introduced in 4.38.0, causing training to be much slower.
         # Inferene can now be CUDAGraphed, but we shall retain the old rotary embeddings.
diff --git a/unsloth/models/qwen3.py b/unsloth/models/qwen3.py
index 80bd6ee7c..b20f22dab 100644
--- a/unsloth/models/qwen3.py
+++ b/unsloth/models/qwen3.py
@@ -66,7 +66,7 @@ def Qwen3Attention_fast_forward(
     position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
     *args, **kwargs,
 ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
-    
+
     # Clear inference
     if hasattr(self, "paged_attention"):
         del self.paged_attention_K
@@ -111,12 +111,13 @@ def Qwen3Attention_fast_forward(
         # Extend RoPE dynamically to fit in VRA
         rotary_emb = self.rotary_emb
         rotary_emb.extend_rope_embedding(V, seq_len = kv_seq_len)
+        device_index = Q.device.index
 
         if position_ids is None:
             # Useful for LongRoPE
-            cos, sin = rotary_emb.get_cached(kv_seq_len)
+            cos, sin = rotary_emb.get_cached(kv_seq_len, device_index)
         else:
-            cos, sin = rotary_emb(V, seq_len = kv_seq_len)
+            cos, sin = rotary_emb.get_cached(kv_seq_len, device_index)
     Q, K = fast_rope_embedding(Q, K, cos, sin)
 
     if past_key_value is not None:
@@ -260,14 +261,14 @@ def Qwen3Attention_fast_forward_inference(
         self.temp_QA = torch.empty((2, bsz, 1, attention_size), dtype = dtype, device = device)
         self.temp_KV = torch.empty((2, bsz, 1, n_kv_heads*head_dim), dtype = dtype, device = device)
         self.RH_Q = torch.empty((bsz, n_heads, 1, head_dim), dtype = dtype, device = device)
-        
+
         # Mistral Nemo 12b has weird dimensions
         if attention_size != hidden_size:
             self.temp_O = torch.empty((1, bsz, hidden_size), dtype = dtype, device = device)
         else:
             self.temp_O = self.temp_QA[1][:,:,:hidden_size]
         pass
-        
+
         self.attention = torch.empty((bsz, n_heads, 1, KV_CACHE_INCREMENT+seq_len), dtype = dtype, device = device)
         self.scalar = 1.0 / math_sqrt(self.head_dim)
         self.half_head_dim = head_dim // 2
@@ -297,7 +298,7 @@ def Qwen3Attention_fast_forward_inference(
     # Need to do it prior 2 steps before hitting full on short KV cache
     # or else error
     self.rotary_emb.extend_rope_embedding(Vn, seq_len + 2)
-    cos, sin = self.rotary_emb.get_cached(kv_seq_len)
+    cos, sin = self.rotary_emb.get_cached(kv_seq_len, Qn.device.index)
     cos = cos[position_ids].unsqueeze(1)
     sin = sin[position_ids].unsqueeze(1)
     h = self.half_head_dim
@@ -315,7 +316,7 @@ def Qwen3Attention_fast_forward_inference(
     RH_K[:,:,:,:h].neg_() #torch.neg(RH_K[:,:,:,:h], out = RH_K[:,:,:,:h])
     Kn *= cos
     Kn.addcmul_(RH_K, sin)
-    
+
     # New KV cache
     # Kn = torch.cat([K1, Kn], dim = 2)
     # Vn = torch.cat([V1, Vn], dim = 2)
diff --git a/unsloth/models/vision.py b/unsloth/models/vision.py
index 53c549742..a358594d8 100644
--- a/unsloth/models/vision.py
+++ b/unsloth/models/vision.py
@@ -61,7 +61,7 @@
     # Old HF Hub versions <= 0.0.25
     from huggingface_hub.utils._token import get_token
 pass
-from unsloth import DEVICE_TYPE
+from unsloth import DEVICE_TYPE, DEVICE_COUNT
 
 __all__ = [
     "FastBaseModel",
@@ -281,7 +281,6 @@ def from_pretrained(
             gpu_stats = torch.cuda.get_device_properties(0)
             gpu_version = torch.version.cuda
             gpu_stats_snippet = f"CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {gpu_version}."
-            num_gpus = torch.cuda.device_count()
 
             from importlib.metadata import version as importlib_version
             try:    vllm_version = f" vLLM: {importlib_version('vllm')}."
@@ -289,7 +288,6 @@ def from_pretrained(
         elif DEVICE_TYPE == "xpu":
             gpu_stats = torch.xpu.get_device_properties(0)
             gpu_version = torch.version.xpu
-            num_gpus = torch.xpu.device_count()
             gpu_stats_snippet = f"Intel Toolkit: {gpu_version}."
 
             # TODO: After adding vLLM support for XPU, changed this
@@ -306,11 +304,11 @@ def from_pretrained(
 
         statistics = \
         f"==((====))==  Unsloth {__version__}: Fast {model_type_arch.title()} patching. Transformers: {transformers_version}.{vllm_version}\n"\
-        f"   {chr(92)}{chr(92)}   /|    {gpu_stats.name}. Num GPUs = {num_gpus}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
+        f"   {chr(92)}{chr(92)}   /|    {gpu_stats.name}. Num GPUs = {DEVICE_COUNT}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
         f"O^O/ {chr(92)}_/ {chr(92)}    Torch: {torch.__version__}. {gpu_stats_snippet} Triton: {triton_version}\n"\
         f"{chr(92)}        /    Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"\
         f' "-____-"     Free license: http://github.com/unslothai/unsloth'
-        
+
         print(statistics)
 
         # Warn about fast transfers
@@ -325,7 +323,7 @@ def from_pretrained(
         pass
         if old_hf_transfer != "0": os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
-        get_statistics() # For debugging - we use a download counter to see if environments are not breaking 
+        get_statistics() # For debugging - we use a download counter to see if environments are not breaking
 
         if dtype is None:
             dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
@@ -604,7 +602,7 @@ def get_peft_model(
         else:
             assert(type(target_modules) in (list, tuple,))
         pass
-        
+
         # Clear deleted GPU items
         for _ in range(3):
             gc.collect()
@@ -678,7 +676,7 @@ def post_patch_model(
             float32_mixed_precision    = float32_mixed_precision,
         )
 
-        from transformers.trainer import Trainer 
+        from transformers.trainer import Trainer
         if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop" and trust_remote_code == False:
             raise RuntimeError('Unsloth: Unsuccessfully patched inner_training_loop')
         pass