@@ -21,39 +21,18 @@ limitations under the License.
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.
## Default Method: NIXL
By default, TensorRT-LLM uses **NIXL** (NVIDIA Inference Xfer Library) with UCX (Unified Communication X) as backend for KV cache transfer between prefill and decode workers. [NIXL](https://github.com/ai-dynamo/nixl) is NVIDIA's high-performance communication library designed for efficient data transfer in distributed GPU environments.
## 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.
### Specify Backends for NIXL
**Note:** NIXL support in TensorRT-LLM is currently beta and may have some sharp edges.
TODO: Add instructions for how to specify different backends for NIXL.
## Using NIXL for KV Cache Transfer
## Alternative Method: UCX
**Note:** NIXL version shipped with current dynamo is not supported by tensorrt-llm<=1.2.0rc2.InordertouseNIXLbackendforKVcachetransfer,usersarerequiredtobuildcontainerimagewithtensorrt-llm>=1.2.0rc3.
TensorRT-LLM can also leverage **UCX** (Unified Communication X) directly for KV cache transfer between prefill and decode workers. There are two ways to enable UCX as the KV cache transfer backend:
To enable NIXL for KV cache transfer in disaggregated serving:
1.**Recommended:** Set `cache_transceiver_config.backend: UCX` in your engine configuration YAML file.
2. Alternatively, set the environment variable `TRTLLM_USE_UCX_KV_CACHE=1` and configure `cache_transceiver_config.backend: DEFAULT` in the engine configuration YAML.
1.**Build the container with NIXL support(tensorrt-llm==1.2.0rc3):**
```bash
./container/build.sh --framework trtllm \
--tensorrtllm-pip-wheel tensorrt-llm==1.2.0rc3
```
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.
Within container, unset `TRTLLM_USE_UCX_KVCACHE` variable so NIXL can be used instead of UCX.
```bash
unset TRTLLM_USE_UCX_KVCACHE
```
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.
This flexibility allows users to choose the most suitable method for their deployment and compatibility requirements.
