Example: Share KV cache across multiple LLMs#

LMCache should be able to reduce the generation time of the second and following calls.

We have examples for the following types of across-instance KV cache sharing:

  • KV cache sharing through a centralized cache server: centralized_sharing

  • KV cache sharing through p2p cache transfer: p2p_sharing

Prerequisites#

Your server should have at least 2 GPUs.

For Centralized sharing, this will use the port 8000 and 8001 (for vLLM) and port 65432 (for LMCache).

For P2P sharing:

  • NIXL installed on the host.

  • Port 8010 and 8011 for 2 vllms servers.

  • Port 8200 and 8202 for 2 p2p initialization connections.

  • Port 8201 and 8203 for 2 p2p lookup connections.

  • Port 8300 for controller pull requests.

  • Port 8400 for controller reply requests.

  • Port 8500 and 8501 for 2 LMCache workers.

  • Port 9000 for controller main port (arbitrary and can be changed) to start the controller.

Centralized KV cache sharing#

This section demonstrates how to share KV cache across multiple vLLM instances using a centralized LMCache server.

Setup centralized sharing#

First, create a configuration file named lmcache_config.yaml with the following content:

chunk_size: 256
local_cpu: true
remote_url: "lm://localhost:65432"
remote_serde: "cachegen"

Run centralized sharing example#

  1. Start the LMCache centralized server,

lmcache_server localhost 65432

  1. In a different terminal,

LMCACHE_CONFIG_FILE=lmcache_config.yaml \
CUDA_VISIBLE_DEVICES=0 \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --gpu-memory-utilization 0.8 \
    --port 8000 --kv-transfer-config \
    '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

In another terminal,

LMCACHE_CONFIG_FILE=lmcache_config.yaml \
CUDA_VISIBLE_DEVICES=1 \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --gpu-memory-utilization 0.8 \
    --port 8001 \
    --kv-transfer-config \
    '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

Wait until both engines are ready.

  1. Send one request to the engine at port 8000,

curl -X POST http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
        "prompt": "Explain the significance of KV cache in language models.",
        "max_tokens": 10
    }'

  1. Send the same request to the engine at port 8001,

curl -X POST http://localhost:8001/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
        "prompt": "Explain the significance of KV cache in language models.",
        "max_tokens": 10
    }'

The second request will automatically retrieve and reuse the KV cache from the first instance, significantly reducing generation time.

P2P KV cache sharing#

This section demonstrates how to share KV cache across multiple vLLM instances using peer-to-peer transfer.

Configure LMCache instances#

Create two configuration files for the P2P sharing setup. The values that differ between the files are the lmcache_instance_id and the P2P/controller port assignments.

Instance 1 configuration (p2p_example1.yaml):

chunk_size: 256
local_cpu: true
max_local_cpu_size: 5
enable_async_loading: True

# P2P configurations
enable_p2p: true
p2p_host: "localhost"
p2p_init_ports: 8200
p2p_lookup_ports: 8201
transfer_channel: "nixl"

# Controller configurations
enable_controller: true
lmcache_instance_id: "lmcache_instance_1"
controller_pull_url: "localhost:8300"
controller_reply_url: "localhost:8400"
lmcache_worker_ports: 8500

extra_config:
  lookup_backoff_time: 0.001

Instance 2 configuration (p2p_example2.yaml):

chunk_size: 256
local_cpu: true
max_local_cpu_size: 5
enable_async_loading: True

# P2P configurations
enable_p2p: true
p2p_host: "localhost"
p2p_init_ports: 8202
p2p_lookup_ports: 8203
transfer_channel: "nixl"

# Controller configurations
enable_controller: true
lmcache_instance_id: "lmcache_instance_2"
controller_pull_url: "localhost:8300"
controller_reply_url: "localhost:8400"
lmcache_worker_ports: 8501

extra_config:
  lookup_backoff_time: 0.001

Save both files in the directory that you will mount into the container (referenced later as $YAML_FILES).

Run the P2P sharing workflow#

  1. Configure the environment on the host and open a shell inside the container:

docker pull vllm/vllm-openai:latest
export WEIGHT_DIR="/models"          # model weights directory
export CONTAINER_NAME="lmcache_vllm" # container name
export YAML_FILES="/path/to/yaml"    # directory containing the YAML files
docker run --name "$CONTAINER_NAME" \
        --detach \
        --ipc=host \
        --network host \
        --gpus all \
        --volume "$WEIGHT_DIR:$WEIGHT_DIR" \
        --volume "$YAML_FILES:$YAML_FILES" \
        --entrypoint "/bin/bash" \
        vllm/vllm-openai:latest -c "time sleep 452d"
docker exec -it "$CONTAINER_NAME" /bin/bash
pip install -U lmcache # update lmcache to the latest version

  1. Start the LMCache controller and monitoring endpoints:

PYTHONHASHSEED=123 lmcache_controller --host localhost --port 9000 --monitor-ports '{"pull": 8300, "reply": 8400}'

  1. Launch two vLLM engines, each with its own LMCache worker configuration.

Start vLLM engine 1 on GPU 0:

PYTHONHASHSEED=123 UCX_TLS=rc CUDA_VISIBLE_DEVICES=0 LMCACHE_CONFIG_FILE=p2p_example1.yaml \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --gpu-memory-utilization 0.8 \
    --port 8010 \
    --kv-transfer-config '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

Start vLLM engine 2 on GPU 1:

PYTHONHASHSEED=123 UCX_TLS=rc CUDA_VISIBLE_DEVICES=1 LMCACHE_CONFIG_FILE=p2p_example2.yaml \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --gpu-memory-utilization 0.8 \
    --port 8011 \
    --kv-transfer-config '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

  1. Populate the KV cache by sending a request to the first engine:

curl -X POST http://localhost:8010/v1/completions \
    -H "Content-Type: application/json" \
    -d "{
        \"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",
        \"prompt\": \"$(printf 'Explain the significance of KV cache in language models.%.0s' {1..100})\",
        \"max_tokens\": 10
    }"

  1. Send the same request to the second engine to demonstrate cache retrieval:

curl -X POST http://localhost:8011/v1/completions \
    -H "Content-Type: application/json" \
    -d "{
        \"model\": \"meta-llama/Meta-Llama-3.1-8B-Instruct\",
        \"prompt\": \"$(printf 'Explain the significance of KV cache in language models.%.0s' {1..100})\",
        \"max_tokens\": 10
    }"

Expected output#

When the second request successfully retrieves the cache from the first instance, the logs should include entries similar to:

(EngineCore_DP0 pid=305) [2025-11-16 07:24:11,522] LMCache INFO: Got layout info from controller: ('lmcache_instance_2', 'LocalCPUBackend', 3, 'localhost:8202') (p2p_backend.py:196:lmcache.v1.storage_backend.p2p_backend)
(EngineCore_DP0 pid=305) [2025-11-16 07:24:11,607] LMCache INFO: Established connection to peer_init_url localhost:8202. The peer_lookup_url: localhost:8203 (p2p_backend.py:349:lmcache.v1.storage_backend.p2p_backend)
(EngineCore_DP0 pid=305) [2025-11-16 07:24:11,706] LMCache INFO: Responding to scheduler for lookup id cmpl-e9ec2875bf954bd298ca26d14e083b80-0 with retrieved length 768 (storage_manager.py:531:lmcache.v1.storage_backend.storage_manager)
(EngineCore_DP0 pid=305) [2025-11-16 07:24:11,708] LMCache INFO: Reqid: cmpl-e9ec2875bf954bd298ca26d14e083b80-0, Total tokens 1002, LMCache hit tokens: 768, need to load: 768 (vllm_v1_adapter.py:1330:lmcache.integration.vllm.vllm_v1_adapter)
(EngineCore_DP0 pid=305) [2025-11-16 07:24:11,724] LMCache INFO: Retrieved 768 out of 768 required tokens (from 768 total tokens). size: 0.0938 gb, cost 7.9816 ms, throughput: 11.7458 GB/s; (cache_engine.py:531:lmcache.v1.cache_engine)

These logs indicate that the peer connection was established and the cache was transferred successfully.