feat: FlexKV integration in Dynamo

ATTRIBUTIONS-Python.md

@@ -10979,6 +10979,194 @@ MIT License
 ### URLs
   - `Homepage`: https://github.com/flashinfer-ai/flashinfer
 
+## flexkv (1.2.1)
+
+### Licenses
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+
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+
+### URLs
+  - `Homepage`: https://github.com/taco-project/FlexKV
+  - `Source`: https://github.com/taco-project/FlexKV
 
 ## fonttools (4.61.1)
 

components/src/dynamo/vllm/args.py

@@ -375,6 +375,10 @@ def _connector_to_kv_transfer_json(connectors: list[str]) -> str:
             multi_connectors.append(
                 {"kv_connector": "LMCacheConnectorV1", "kv_role": "kv_both"}
             )
+        elif c == "flexkv":
+            multi_connectors.append(
+                {"kv_connector": "FlexKVConnectorV1", "kv_role": "kv_both"}
+            )
         elif c == "nixl":
             multi_connectors.append(
                 {"kv_connector": "NixlConnector", "kv_role": "kv_both"}

components/src/dynamo/vllm/tests/test_vllm_unit.py

@@ -283,6 +283,7 @@ def test_uses_nixl_connector_direct_and_nested():
     assert _uses_nixl_connector(_make_engine_cfg("NixlConnector")) is True
     assert _uses_nixl_connector(_make_engine_cfg("PdConnector", _PD_KVBM_NIXL)) is True
     assert _uses_nixl_connector(_make_engine_cfg("LMCacheConnectorV1")) is False
+    assert _uses_nixl_connector(_make_engine_cfg("FlexKVConnectorV1")) is False
     assert _uses_nixl_connector(_make_engine_cfg()) is False
 
 

docs/backends/vllm/README.md

@@ -51,6 +51,7 @@ docker build -f container/rendered.Dockerfile -t dynamo:latest-vllm .
 | [**SLA-Based Planner**](../../components/planner/planner-guide.md) | ✅ | |
 | [**KVBM**](../../components/kvbm/README.md) | ✅ | |
 | [**LMCache**](../../integrations/lmcache-integration.md) | ✅ | |
+| [**FlexKV**](../../integrations/flexkv-integration.md) | ✅ | |
 | [**Multimodal Support**](vllm-omni.md) | ✅ | Via vLLM-Omni integration |
 | [**Observability**](vllm-observability.md) | ✅ | Metrics and monitoring |
 | **WideEP** | ✅ | Support for DeepEP |

docs/backends/vllm/flexkv_integration.md

@@ -0,0 +1,31 @@
+# FlexKV Integration in Dynamo
+
+## Introduction
+
+[FlexKV](https://github.com/taco-project/FlexKV) is a scalable, distributed runtime for KV cache offloading. It acts as a unified KV caching layer for inference engines like vLLM, TensorRT-LLM and SGLang.
+
+## Usage
+
+Enable FlexKV by setting the `DYNAMO_USE_FLEXKV` environment variable:
+
+```bash
+export DYNAMO_USE_FLEXKV=1
+```
+
+### Aggregated Serving
+
+Use FlexKV with the `--kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'` flag:
+
+```bash
+python -m dynamo.vllm --model $YOUR_MODEL --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'
+```
+
+Refer to [`agg_flexkv.sh`](../../../examples/backends/vllm/launch/agg_flexkv.sh) for quick setup.
+
+### Aggregated Serving with Peer Node KV Cache Reuse
+
+Refer to our project [README](https://github.com/taco-project/FlexKV/blob/main/docs/dist_reuse/README_en.md) for instructions on setting up peer KV cache reuse.
+
+### Disaggregated Serving
+
+Refer to [`disagg_flexkv.sh`](../../../examples/backends/vllm/launch/disagg_flexkv.sh) for quick setup.

docs/design-docs/architecture.md

@@ -26,7 +26,7 @@ There are multi-faceted challenges:
 
 - *Expensive KV cache re-computation*: When requests aren't efficiently routed, KV caches (intermediate states of transformer model) often get flushed and recomputed, leading to wasted computation cycles and increased latency. KV-aware request routing eliminates redundant KV cache regeneration, significantly boosting efficiency.([DeepSeek](https://arxiv.org/abs/2501.12948))
 
-- *Memory bottlenecks*: Large-scale inference workloads demand extensive KV cache storage, which can quickly overwhelm GPU memory capacity. KV cache offloading across memory hierarchies (HBM, DDR, NVMe or remote storage) enables models to scale beyond GPU memory limits and speeds up latency. ([Mooncake](https://kvcache-ai.github.io/Mooncake/design/mooncake-store.html), [AIBrix](https://blog.vllm.ai/2025/02/21/aibrix-release.html), [LMCache](https://lmcache.ai/))
+- *Memory bottlenecks*: Large-scale inference workloads demand extensive KV cache storage, which can quickly overwhelm GPU memory capacity. KV cache offloading across memory hierarchies (HBM, DDR, NVMe or remote storage) enables models to scale beyond GPU memory limits and speeds up latency. ([Mooncake](https://kvcache-ai.github.io/Mooncake/design/mooncake-store.html), [AIBrix](https://blog.vllm.ai/2025/02/21/aibrix-release.html), [FlexKV](https://github.com/taco-project/FlexKV), [LMCache](https://lmcache.ai/))
 
 - *Fluctuating demand and inefficient GPU allocation*: Inference workloads are use-case specific and dynamic—demand surges inherently cause unpredictably, yet traditional serving stacks allocate GPUs statically. Dynamic GPU scheduling ensures that resources are allocated based on real-time demand, preventing over-provisioning and improving utilization ([AzureTrace](https://github.com/Azure/AzurePublicDataset))
 

docs/integrations/flexkv-integration.md

@@ -6,7 +6,7 @@ title: FlexKV
 
 ## Introduction
 
-[FlexKV](https://github.com/taco-project/FlexKV) is a scalable, distributed runtime for KV cache offloading developed by Tencent Cloud's TACO team in collaboration with the community. It acts as a unified KV caching layer for inference engines like SGLang, TensorRT-LLM, and vLLM.
+[FlexKV](https://github.com/taco-project/FlexKV) is a scalable, distributed runtime for KV cache offloading developed by Tencent Cloud's TACO team and NVIDIA in collaboration with the community. It acts as a unified KV caching layer for inference engines like SGLang, TensorRT-LLM, and vllm.
 
 ### Key Features
 
@@ -36,7 +36,7 @@ Set the `DYNAMO_USE_FLEXKV` environment variable and use the `--kv-transfer-conf
 
 ```bash
 export DYNAMO_USE_FLEXKV=1
-python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
+python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'
 ```
 
 ## Aggregated Serving
@@ -50,7 +50,7 @@ python -m dynamo.frontend &
 # Terminal 2: Start vLLM worker with FlexKV
 DYNAMO_USE_FLEXKV=1 \
 FLEXKV_CPU_CACHE_GB=32 \
-  python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
+  python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'
 ```
 
 ### With KV-Aware Routing
@@ -70,7 +70,7 @@ FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_0" \
 CUDA_VISIBLE_DEVICES=0 \
 python -m dynamo.vllm \
     --model Qwen/Qwen3-0.6B \
-    --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}' \
+    --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}' \
     --gpu-memory-utilization 0.2 \
     --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20080","enable_kv_cache_events":true}' &
 
@@ -81,7 +81,7 @@ FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_1" \
 CUDA_VISIBLE_DEVICES=1 \
 python -m dynamo.vllm \
     --model Qwen/Qwen3-0.6B \
-    --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}' \
+    --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}' \
     --gpu-memory-utilization 0.2 \
     --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20081","enable_kv_cache_events":true}'
 ```
@@ -106,7 +106,7 @@ CUDA_VISIBLE_DEVICES=1 \
   python -m dynamo.vllm \
   --model Qwen/Qwen3-0.6B \
   --disaggregation-mode prefill \
-  --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
+  --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'
 ```
 
 ## Configuration

examples/backends/vllm/launch/agg_flexkv.sh

@@ -0,0 +1,13 @@
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+# Run frontend
+python -m dynamo.frontend &
+
+# Run worker with FlexKV
+DYNAMO_USE_FLEXKV=1 \
+FLEXKV_CPU_CACHE_GB=32 \
+  python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}'

examples/backends/vllm/launch/agg_flexkv_router.sh

@@ -0,0 +1,33 @@
+#!/bin/bash
+# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+set -e
+trap 'echo Cleaning up...; kill 0' EXIT
+
+MODEL="Qwen/Qwen3-0.6B"
+
+# Run frontend and KV router
+python -m dynamo.frontend \
+    --router-mode kv \
+    --router-reset-states &
+
+# Run worker with FlexKV
+DYNAMO_USE_FLEXKV=1 \
+FLEXKV_CPU_CACHE_GB=32 \
+FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_0" \
+CUDA_VISIBLE_DEVICES=0 \
+python -m dynamo.vllm \
+    --model $MODEL \
+    --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}' \
+    --gpu-memory-utilization 0.2 \
+    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20080","enable_kv_cache_events":true}' &
+
+DYNAMO_USE_FLEXKV=1 \
+FLEXKV_CPU_CACHE_GB=32 \
+FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_1" \
+CUDA_VISIBLE_DEVICES=1 \
+python -m dynamo.vllm \
+    --model $MODEL \
+    --kv-transfer-config '{"kv_connector":"FlexKVConnectorV1","kv_role":"kv_both"}' \
+    --gpu-memory-utilization 0.2 \
+    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20081","enable_kv_cache_events":true}'