kv-cache-transfer.md

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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:

## Using NIXL for KV Cache Transfer

Start the disaggregated service: See [Disaggregated Serving](./README.md#disaggregated) to learn how to start the deployment.

## 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.

### Specify Backends for NIXL

TODO: Add instructions for how to specify different backends for NIXL.

## Alternative Method: UCX

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:

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.

This flexibility allows users to choose the most suitable method for their deployment and compatibility requirements.