diff --git a/tests/kernels/attention/test_flash_attn.py b/tests/kernels/attention/test_flash_attn.py
index 18995545552e..6e5468969bf2 100644
--- a/tests/kernels/attention/test_flash_attn.py
+++ b/tests/kernels/attention/test_flash_attn.py
@@ -9,7 +9,6 @@
 from vllm.vllm_flash_attn import (
     fa_version_unsupported_reason,
     flash_attn_varlen_func,
-    flash_attn_with_kvcache,
     is_fa_version_supported,
 )
 
@@ -83,124 +82,6 @@ def ref_paged_attn(
     return torch.cat(outputs, dim=0)
 
 
-@pytest.mark.parametrize("use_out", [True, False])
-@pytest.mark.parametrize("kv_lens", [[1328, 18, 463], [1, 54, 293, 70]])
-@pytest.mark.parametrize("num_heads", NUM_HEADS)
-@pytest.mark.parametrize("head_size", HEAD_SIZES)
-@pytest.mark.parametrize("block_size", BLOCK_SIZES)
-@pytest.mark.parametrize("dtype", DTYPES)
-@pytest.mark.parametrize("soft_cap", SOFT_CAPS)
-@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
-@pytest.mark.parametrize("sliding_window", SLIDING_WINDOWS)
-@pytest.mark.parametrize("fa_version", [2, 3])
-@pytest.mark.parametrize("q_dtype", QDTYPES)
-@torch.inference_mode()
-def test_flash_attn_with_paged_kv(
-    use_out: bool,
-    kv_lens: list[int],
-    num_heads: tuple[int, int],
-    head_size: int,
-    dtype: torch.dtype,
-    block_size: int,
-    soft_cap: float | None,
-    num_blocks: int,
-    sliding_window: int | None,
-    fa_version: int,
-    q_dtype: torch.dtype | None,
-) -> None:
-    torch.set_default_device("cuda")
-    if not is_fa_version_supported(fa_version):
-        pytest.skip(
-            f"Flash attention version {fa_version} not supported due "
-            f'to: "{fa_version_unsupported_reason(fa_version)}"'
-        )
-    if q_dtype is not None and (dtype != torch.bfloat16 or fa_version == 2):
-        pytest.skip(
-            "Flash attention with quantized inputs is only "
-            "supported on version 3 with bfloat16 base type"
-        )
-
-    current_platform.seed_everything(0)
-    num_seqs = len(kv_lens)
-    num_query_heads = num_heads[0]
-    num_kv_heads = num_heads[1]
-    assert num_query_heads % num_kv_heads == 0
-    max_kv_len = max(kv_lens)
-    scale = head_size**-0.5
-    window_size = (sliding_window - 1, 0) if sliding_window is not None else (-1, -1)
-
-    query = torch.randn(num_seqs, num_query_heads, head_size, dtype=dtype)
-    key_cache = torch.randn(
-        num_blocks, block_size, num_kv_heads, head_size, dtype=dtype
-    )
-    value_cache = torch.randn_like(key_cache)
-    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int32)
-
-    max_num_blocks_per_seq = (max_kv_len + block_size - 1) // block_size
-    block_tables = torch.randint(
-        0, num_blocks, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
-    )
-
-    q = query.unsqueeze(1)
-    out = torch.empty_like(q) if use_out else None
-
-    maybe_quantized_query = q
-    maybe_quantized_key_cache = key_cache
-    maybe_quantized_value_cache = value_cache
-    q_descale = None
-    k_descale = None
-    v_descale = None
-    if q_dtype is not None:
-        # QKV are drawn from N(0, 1): no need for a fp8 scaling factor
-        maybe_quantized_query = q.to(q_dtype)
-        maybe_quantized_key_cache = key_cache.to(q_dtype)
-        maybe_quantized_value_cache = value_cache.to(q_dtype)
-
-        scale_shape = (num_seqs, num_kv_heads)
-        q_descale = torch.ones(scale_shape, dtype=torch.float32)
-        k_descale = torch.ones(scale_shape, dtype=torch.float32)
-        v_descale = torch.ones(scale_shape, dtype=torch.float32)
-
-    output = flash_attn_with_kvcache(
-        q=maybe_quantized_query,
-        k_cache=maybe_quantized_key_cache,
-        v_cache=maybe_quantized_value_cache,
-        out=out,
-        softmax_scale=scale,
-        causal=True,
-        block_table=block_tables,
-        cache_seqlens=kv_lens_tensor,
-        softcap=soft_cap if soft_cap is not None else 0,
-        window_size=window_size,
-        fa_version=fa_version,
-        q_descale=q_descale,
-        k_descale=k_descale,
-        v_descale=v_descale,
-    )
-    output = output if not use_out else out
-    output = output.squeeze(1)
-
-    atol, rtol = 1.5e-2, 1e-2
-    if q_dtype is not None:
-        atol, rtol = 1.5e-1, 1.5e-1
-
-    ref_output = ref_paged_attn(
-        query=query,
-        key_cache=key_cache,
-        value_cache=value_cache,
-        query_lens=[1] * num_seqs,
-        kv_lens=kv_lens,
-        block_tables=block_tables,
-        scale=scale,
-        soft_cap=soft_cap,
-        sliding_window=sliding_window,
-    )
-    (
-        torch.testing.assert_close(output, ref_output, atol=atol, rtol=rtol),
-        f"{torch.max(torch.abs(output - ref_output))}",
-    )
-
-
 @pytest.mark.parametrize("use_out", [True, False])
 @pytest.mark.parametrize(
     "seq_lens", [[(1, 1328), (5, 18), (129, 463)], [(1, 523), (1, 37), (1, 2011)]]