vllm/docs/features/nixl_connector_usage.md

NixlConnector Usage Guide

NixlConnector is a high-performance KV cache transfer connector for vLLM's disaggregated prefilling feature. It provides fully asynchronous send/receive operations using the NIXL library for efficient cross-process KV cache transfer.

Prerequisites

Installation

Install the NIXL library: uv pip install nixl, as a quick start.

• Refer to NIXL official repository for more installation instructions
• The specified required NIXL version can be found in requirements/kv_connectors.txt and other relevant config files

Transport Configuration

NixlConnector uses NIXL library for underlying communication, which supports multiple transport backends. UCX (Unified Communication X) is the primary default transport library used by NIXL. Configure transport environment variables:

# Example UCX configuration, adjust according to your enviroment
export UCX_TLS=all  # or specify specific transports like "rc,ud,sm,^cuda_ipc" ..etc
export UCX_NET_DEVICES=all  # or specify network devices like "mlx5_0:1,mlx5_1:1"

!!! tip When using UCX as the transport backend, NCCL environment variables (like NCCL_IB_HCA, NCCL_SOCKET_IFNAME) are not applicable to NixlConnector, so configure UCX-specific environment variables instead of NCCL variables.

Basic Usage (on the same host)

Producer (Prefiller) Configuration

Start a prefiller instance that produces KV caches

# 1st GPU as prefiller
CUDA_VISIBLE_DEVICES=0 \
UCX_NET_DEVICES=all \
VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
vllm serve Qwen/Qwen3-0.6B \
  --port 8100 \
  --enforce-eager \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'

Consumer (Decoder) Configuration

Start a decoder instance that consumes KV caches:

# 2nd GPU as decoder
CUDA_VISIBLE_DEVICES=1 \
UCX_NET_DEVICES=all \
VLLM_NIXL_SIDE_CHANNEL_PORT=5601 \
vllm serve Qwen/Qwen3-0.6B \
  --port 8200 \
  --enforce-eager \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'

Proxy Server

Use a proxy server to route requests between prefiller and decoder:

python tests/v1/kv_connector/nixl_integration/toy_proxy_server.py \
  --port 8192 \
  --prefiller-hosts localhost \
  --prefiller-ports 8100 \
  --decoder-hosts localhost \
  --decoder-ports 8200

Environment Variables

• VLLM_NIXL_SIDE_CHANNEL_PORT: Port for NIXL handshake communication

  • Default: 5600
  • Required for both prefiller and decoder instances
  • Each vLLM worker needs a unique port on its host; using the same port number across different hosts is fine
  • For TP/DP deployments, each worker's port on a node is computed as: base_port + dp_rank * tp_size + tp_rank (e.g., with --tensor-parallel-size=4 and base_port=5600, tp_rank 0..3 use ports 5600, 5601, 5602, 5603 on that node).
  • Used for the initial NIXL handshake between the prefiller and the decoder

• VLLM_NIXL_SIDE_CHANNEL_HOST: Host for side channel communication

  • Default: "localhost"
  • Set when prefiller and decoder are on different machines
  • Connection info is passed via KVTransferParams from prefiller to decoder for handshake

• VLLM_NIXL_ABORT_REQUEST_TIMEOUT: Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request. (Optional)

  • Default: 120
  • If a request is aborted and the decoder has not yet read the KV-cache blocks through the nixl channel, the prefill instance will release its KV-cache blocks after this timeout to avoid holding them indefinitely.

Multi-Instance Setup

Multiple Prefiller Instances on Different Machines

# Prefiller 1 on Machine A (example IP: ${IP1})
VLLM_NIXL_SIDE_CHANNEL_HOST=${IP1} \
VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
UCX_NET_DEVICES=all \
vllm serve Qwen/Qwen3-0.6B --port 8000 \
  --tensor-parallel-size 8 \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'

# Prefiller 2 on Machine B (example IP: ${IP2})
VLLM_NIXL_SIDE_CHANNEL_HOST=${IP2} \
VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
UCX_NET_DEVICES=all \
vllm serve Qwen/Qwen3-0.6B --port 8000 \
  --tensor-parallel-size 8 \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'

Multiple Decoder Instances on Different Machines

# Decoder 1 on Machine C (example IP: ${IP3})
VLLM_NIXL_SIDE_CHANNEL_HOST=${IP3} \
VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
UCX_NET_DEVICES=all \
vllm serve Qwen/Qwen3-0.6B --port 8000 \
  --tensor-parallel-size 8 \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'

# Decoder 2 on Machine D (example IP: ${IP4})
VLLM_NIXL_SIDE_CHANNEL_HOST=${IP4} \
VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
UCX_NET_DEVICES=all \
vllm serve Qwen/Qwen3-0.6B --port 8000 \
  --tensor-parallel-size 8 \
  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'

Proxy for Multiple Instances

python tests/v1/kv_connector/nixl_integration/toy_proxy_server.py \
  --port 8192 \
  --prefiller-hosts ${IP1} ${IP2} \
  --prefiller-ports 8000 8000 \
  --decoder-hosts ${IP3} ${IP4} \
  --decoder-ports 8000 8000

KV Role Options

• kv_producer: For prefiller instances that generate KV caches
• kv_consumer: For decoder instances that consume KV caches from prefiller
• kv_both: Enables symmetric functionality where the connector can act as both producer and consumer. This provides flexibility for experimental setups and scenarios where the role distinction is not predetermined.

!!! tip NixlConnector currently does not distinguish kv_role; the actual prefiller/decoder roles are determined by the upper-level proxy (e.g., toy_proxy_server.py using --prefiller-hosts and --decoder-hosts). Therefore, kv_role in --kv-transfer-config is effectively a placeholder and does not affect NixlConnector's behavior.

Example Scripts/Code

Refer to these example scripts in the vLLM repository:

• run_accuracy_test.sh
• toy_proxy_server.py
• test_accuracy.py