vllm-setup.md

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# Running KVBM in vLLM

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

To learn what KVBM is, please check [here](kvbm_architecture.md)

## 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 dynamo vLLM container (KVBM is built in by default)
./container/build.sh --framework vllm

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

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

### Disaggregated Serving with KVBM
```bash
# 1P1D - one prefill worker and one decode worker
# NOTE: need at least 2 GPUs
cd $DYNAMO_HOME/examples/backends/vllm
./launch/disagg_kvbm.sh

# 2P2D - two prefill workers and two decode workers
# NOTE: need at least 4 GPUs
cd $DYNAMO_HOME/examples/backends/vllm
./launch/disagg_kvbm_2p2d.sh
```

> [!NOTE]
> Configure or tune KVBM cache tiers (choose one of the following options):
> ```bash
> # Option 1: CPU cache only (GPU -> CPU offloading)
> # 4 means 4GB of pinned CPU memory would be used
> export DYN_KVBM_CPU_CACHE_GB=4
>
> # Option 2: Both CPU and Disk cache (GPU -> CPU -> Disk tiered offloading)
> export DYN_KVBM_CPU_CACHE_GB=4
> # 8 means 8GB of disk would be used
> export DYN_KVBM_DISK_CACHE_GB=8
>
> # [Experimental] Option 3: Disk cache only (GPU -> Disk direct offloading, bypassing CPU)
> # NOTE: this option is only experimental and it might not give out the best performance.
> # NOTE: disk offload filtering is not support when using this option.
> export DYN_KVBM_DISK_CACHE_GB=8
> ```
>
> You can also use "DYN_KVBM_CPU_CACHE_OVERRIDE_NUM_BLOCKS" or
> "DYN_KVBM_DISK_CACHE_OVERRIDE_NUM_BLOCKS" to specify exact block counts instead of GB

> [!NOTE]
> When disk offloading is enabled, to extend SSD lifespan, disk offload filtering would be enabled by default. The current policy is only offloading KV blocks from CPU to disk if the blocks have frequency equal or more than `2`. Frequency is determined via doubling on cache hit (init with 1) and decrement by 1 on each time decay step.
>
> To disable disk offload filtering, set `DYN_KVBM_DISABLE_DISK_OFFLOAD_FILTER` to true or 1.

### 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": "kvbm.vllm_integration.connector"}' Qwen/Qwen3-0.6B
```

## 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).
# NOTE: update launch/disagg_kvbm.sh or launch/disagg_kvbm_2p2d.sh as needed
DYN_KVBM_METRICS=true \
python -m dynamo.vllm \
    --model Qwen/Qwen3-0.6B \
    --enforce-eager \
    --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 \
    "Qwen/Qwen3-0.6B" \
    "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 Qwen/Qwen3-0.6B` to turn KVBM off as the baseline.