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, this will use the port 8000 and 8001 for 2 vllms, And will use port 8200 and 8201 for 2 distributed cache servers, And will use port 8100 for lookup server.

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 mistralai/Mistral-7B-Instruct-v0.2 \
    --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 mistralai/Mistral-7B-Instruct-v0.2 \
    --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.

Setup P2P sharing#

Create two configuration files for the P2P sharing setup:

Instance 1 configuration (lmcache_config1.yaml):

chunk_size: 256
local_cpu: true
max_local_cpu_size: 5

# P2P configuration
enable_p2p: true
lookup_url: "localhost:8100"
distributed_url: "localhost:8200"

Instance 2 configuration (lmcache_config2.yaml):

chunk_size: 256
local_cpu: true
max_local_cpu_size: 5

# P2P configuration
enable_p2p: true
lookup_url: "localhost:8100"
distributed_url: "localhost:8201"

Run P2P sharing example#

  1. Pull redis docker and start lookup server at port 8100:

docker pull redis
docker run --name lmcache-redis -d -p 8100:6379 redis
  1. Start two vllm engines:

Start vllm engine 1 at port 8000:

CUDA_VISIBLE_DEVICES=0 \
LMCACHE_CONFIG_FILE=lmcache_config1.yaml \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.8 \
    --port 8000 \
    --kv-transfer-config \
    '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

Start vllm engine 2 at port 8001:

CUDA_VISIBLE_DEVICES=1 \
LMCACHE_CONFIG_FILE=lmcache_config2.yaml \
vllm serve meta-llama/Meta-Llama-3.1-8B-Instruct \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.8 \
    --port 8001 \
    --kv-transfer-config \
    '{"kv_connector":"LMCacheConnectorV1", "kv_role":"kv_both"}'

Note that the two distributed cache servers will start at port 8200 and 8201.

  1. Send request to vllm engine 1:

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": 100
    }'
  1. Send request to vllm engine 2:

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": 100
    }'

The cache will be automatically retrieved from vllm engine 1.