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# KV Cache Transfer in Disaggregated Serving

In disaggregated serving architectures, KV cache must be transferred between prefill and decode workers. TensorRT-LLM supports two methods for this transfer:

## Default Method: UCX
By default, TensorRT-LLM uses UCX (Unified Communication X) for KV cache transfer between prefill and decode workers. UCX provides high-performance communication optimized for GPU-to-GPU transfers.

## Beta Method: NIXL
TensorRT-LLM also supports using **NIXL** (NVIDIA Inference Xfer Library) for KV cache transfer. [NIXL](https://github.com/ai-dynamo/nixl) is NVIDIA's high-performance communication library designed for efficient data transfer in distributed GPU environments.

**Note:** NIXL support in TensorRT-LLM is currently beta and may have some sharp edges.

## Using NIXL for KV Cache Transfer

**Note:** NIXL backend for TensorRT-LLM is currently only supported on AMD64 (x86_64) architecture. If you're running on ARM64, you'll need to use the default UCX method for KV cache transfer.

To enable NIXL for KV cache transfer in disaggregated serving:

1. **Build the container with NIXL support:**
   The TensorRT-LLM wheel must be built from source with NIXL support. The `./container/build.sh` script caches previously built TensorRT-LLM wheels to reduce build time. If you have previously built a TensorRT-LLM wheel without NIXL support, you must delete the cached wheel to force a rebuild with NIXL support.

   **Remove cached TensorRT-LLM wheel (only if previously built without NIXL support):**
   ```bash
   rm -rf /tmp/trtllm_wheel
   ```

   **Build the container with NIXL support:**
   ```bash
   ./container/build.sh --framework trtllm \
     --use-default-experimental-tensorrtllm-commit \
     --trtllm-use-nixl-kvcache-experimental
   ```

   **Note:** Both `--use-default-experimental-tensorrtllm-commit` and `--trtllm-use-nixl-kvcache-experimental` flags are required to enable NIXL support.

2. **Run the containerized environment:**
   See [run container](./README.md#run-container) section to learn how to start the container image built in previous step.

3. **Start the disaggregated service:**
   See [disaggregated serving](./README.md#disaggregated-serving) to see how to start the deployment.

4. **Send the request:**
   See [client](./README.md#client) section to learn how to send the request to deployment.

**Important:** Ensure that ETCD and NATS services are running before starting the service.
