run_kvbm_in_vllm.md

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# Running KVBM in vLLM

This guide explains how to leverage KVBM (KV Block Manager) to mange KV cache and do KV offloading in vLLM.

To learn what KVBM is, please check [here](https://docs.nvidia.com/dynamo/latest/architecture/kvbm_intro.html)

## Quick Start

To use KVBM in vLLM, you can follow the steps below:

### Docker Setup
```bash
# start up etcd for KVBM leader/worker registration and discovery
docker compose -f deploy/docker-compose.yml up -d

# build a container containing vllm and kvbm
./container/build.sh --framework vllm --enable-kvbm

# launch the container
./container/run.sh --framework vllm -it --mount-workspace --use-nixl-gds
```

### Aggregated Serving with KVBM
```bash
cd $DYNAMO_HOME/components/backends/vllm
./launch/agg_kvbm.sh
```

### Disaggregated Serving with KVBM (1P1D)
```bash
# NOTE: need at least 2 GPUs
cd $DYNAMO_HOME/components/backends/vllm
./launch/disagg_kvbm.sh
```
> [!NOTE]
> To tune the size of CPU or disk cache, set `DYN_KVBM_CPU_CACHE_GB` and `DYN_KVBM_DISK_CACHE_GB` accordingly. We only set `DYN_KVBM_CPU_CACHE_GB=20` in both scripts above.

> [!NOTE]
> `DYN_KVBM_CPU_CACHE_GB` must be set and `DYN_KVBM_DISK_CACHE_GB` is optional.

### Sample Request
```bash
# make a request to verify vLLM with KVBM is started up correctly
# NOTE: change the model name if served with a different one
curl localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [
    {
        "role": "user",
        "content": "In the heart of Eldoria, an ancient land of boundless magic and mysterious creatures, lies the long-forgotten city of Aeloria. Once a beacon of knowledge and power, Aeloria was buried beneath the shifting sands of time, lost to the world for centuries. You are an intrepid explorer, known for your unparalleled curiosity and courage, who has stumbled upon an ancient map hinting at ests that Aeloria holds a secret so profound that it has the potential to reshape the very fabric of reality. Your journey will take you through treacherous deserts, enchanted forests, and across perilous mountain ranges. Your Task: Character Background: Develop a detailed background for your character. Describe their motivations for seeking out Aeloria, their skills and weaknesses, and any personal connections to the ancient city or its legends. Are they driven by a quest for knowledge, a search for lost familt clue is hidden."
    }
    ],
    "stream":false,
    "max_tokens": 10
  }'
```

Alternatively, can use `vllm serve` directly to use KVBM for aggregated serving:
```bash
vllm serve --kv-transfer-config '{"kv_connector":"DynamoConnector","kv_role":"kv_both", "kv_connector_module_path": "dynamo.llm.vllm_integration.connector"}' deepseek-ai/DeepSeek-R1-Distill-Llama-8B
```

## Enable and View KVBM Metrics

Follow below steps to enable metrics collection and view via Grafana dashboard:
```bash
# Start the basic services (etcd & natsd), along with Prometheus and Grafana
docker compose -f deploy/docker-compose.yml --profile metrics up -d

# set env var DYN_KVBM_METRICS to true, when launch via dynamo
# Optionally set DYN_KVBM_METRICS_PORT to choose the /metrics port (default: 6880).
DYN_KVBM_METRICS=true \
python -m dynamo.vllm \
    --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
    --connector kvbm &

# optional if firewall blocks KVBM metrics ports to send prometheus metrics
sudo ufw allow 6880/tcp
```

View grafana metrics via http://localhost:3001 (default login: dynamo/dynamo) and look for KVBM Dashboard

## Benchmark KVBM

Once the model is loaded ready, follow below steps to use LMBenchmark to benchmark KVBM performance:
```bash
git clone https://github.com/LMCache/LMBenchmark.git

# show case of running the synthetic multi-turn chat dataset.
# we are passing model, endpoint, output file prefix and qps to the sh script.
cd LMBenchmark/synthetic-multi-round-qa
./long_input_short_output_run.sh \
    "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" \
    "http://localhost:8000" \
    "benchmark_kvbm" \
    1

# Average TTFT and other perf numbers would be in the output from above cmd
```
More details about how to use LMBenchmark could be found [here](https://github.com/LMCache/LMBenchmark).

`NOTE`: if metrics are enabled as mentioned in the above section, you can observe KV offloading, and KV onboarding in the grafana dashboard.

To compare, you can run `vllm serve deepseek-ai/DeepSeek-R1-Distill-Llama-8B` to turn KVBM off as the baseline.