---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: FlexKV
---

## Introduction

[FlexKV](https://github.com/taco-project/FlexKV) is a scalable, distributed runtime for KV cache offloading developed by Tencent Cloud's TACO team in collaboration with the community. It acts as a unified KV caching layer for inference engines like SGLang, TensorRT-LLM, and vLLM.

### Key Features

- **Multi-level caching**: CPU memory, local SSD, and scalable storage (cloud storage) for KV cache offloading
- **Distributed KV cache reuse**: Share KV cache across multiple nodes using distributed RadixTree
- **High-performance I/O**: Supports io_uring and GPU Direct Storage (GDS) for accelerated data transfer
- **Asynchronous operations**: Get and put operations can overlap with computation through prefetching


## Prerequisites

1. **Dynamo installed** with vLLM support
2. **Infrastructure services running**:
   ```bash
   docker compose -f deploy/docker-compose.yml up -d
   ```
3. **FlexKV dependencies** (for SSD offloading):
   ```bash
   apt install liburing-dev libxxhash-dev
   ```

## Quick Start

### Enable FlexKV

Set the `DYNAMO_USE_FLEXKV` environment variable and use the `--kv-transfer-config` flag:

```bash
export DYNAMO_USE_FLEXKV=1
python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
```

## Aggregated Serving

### Basic Setup

```bash
# Terminal 1: Start frontend
python -m dynamo.frontend &

# Terminal 2: Start vLLM worker with FlexKV
DYNAMO_USE_FLEXKV=1 \
FLEXKV_CPU_CACHE_GB=32 \
  python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
```

### With KV-Aware Routing

For multi-worker deployments with KV-aware routing to maximize cache reuse:

```bash
# Terminal 1: Start frontend with KV router
python -m dynamo.frontend \
    --router-mode kv \
    --router-reset-states &

# Terminal 2: Worker 1
DYNAMO_USE_FLEXKV=1 \
FLEXKV_CPU_CACHE_GB=32 \
FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_0" \
CUDA_VISIBLE_DEVICES=0 \
python -m dynamo.vllm \
    --model Qwen/Qwen3-0.6B \
    --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}' \
    --gpu-memory-utilization 0.2 \
    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20080","enable_kv_cache_events":true}' &

# Terminal 3: Worker 2
DYNAMO_USE_FLEXKV=1 \
FLEXKV_CPU_CACHE_GB=32 \
FLEXKV_SERVER_RECV_PORT="ipc:///tmp/flexkv_server_1" \
CUDA_VISIBLE_DEVICES=1 \
python -m dynamo.vllm \
    --model Qwen/Qwen3-0.6B \
    --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}' \
    --gpu-memory-utilization 0.2 \
    --kv-events-config '{"publisher":"zmq","topic":"kv-events","endpoint":"tcp://*:20081","enable_kv_cache_events":true}'
```

## Disaggregated Serving

FlexKV can be used with disaggregated prefill/decode serving. The prefill worker uses FlexKV for KV cache offloading, while NIXL handles KV transfer between prefill and decode workers.

```bash
# Terminal 1: Start frontend
python -m dynamo.frontend &

# Terminal 2: Decode worker (without FlexKV)
CUDA_VISIBLE_DEVICES=0 python -m dynamo.vllm --model Qwen/Qwen3-0.6B --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}' &

# Terminal 3: Prefill worker (with FlexKV)
DYN_VLLM_KV_EVENT_PORT=20081 \
VLLM_NIXL_SIDE_CHANNEL_PORT=20097 \
DYNAMO_USE_FLEXKV=1 \
FLEXKV_CPU_CACHE_GB=32 \
CUDA_VISIBLE_DEVICES=1 \
  python -m dynamo.vllm \
  --model Qwen/Qwen3-0.6B \
  --disaggregation-mode prefill \
  --kv-transfer-config '{"kv_connector":"FlexKVConnector","kv_role":"kv_both"}'
```

## Configuration

### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `DYNAMO_USE_FLEXKV` | Enable FlexKV integration | `0` (disabled) |
| `FLEXKV_CPU_CACHE_GB` | CPU memory cache size in GB | Required |
| `FLEXKV_CONFIG_PATH` | Path to FlexKV YAML config file | Not set |
| `FLEXKV_SERVER_RECV_PORT` | IPC port for FlexKV server | Auto |

### CPU-Only Offloading

For simple CPU memory offloading:

```bash
unset FLEXKV_CONFIG_PATH
export FLEXKV_CPU_CACHE_GB=32
```

### CPU + SSD Tiered Offloading

For multi-tier offloading with SSD storage, create a configuration file:

```bash
cat > ./flexkv_config.yml <<EOF
cpu_cache_gb: 32
ssd_cache_gb: 1024
ssd_cache_dir: /data0/flexkv_ssd/;/data1/flexkv_ssd/
enable_gds: false
EOF

export FLEXKV_CONFIG_PATH="./flexkv_config.yml"
```

### Configuration Options

| Option | Description |
|--------|-------------|
| `cpu_cache_gb` | CPU memory cache size in GB |
| `ssd_cache_gb` | SSD cache size in GB |
| `ssd_cache_dir` | SSD cache directories (semicolon-separated for multiple SSDs) |
| `enable_gds` | Enable GPU Direct Storage for SSD I/O |

> **Note:** For full configuration options, see the [FlexKV Configuration Reference](https://github.com/taco-project/FlexKV/blob/main/docs/flexkv_config_reference/README_en.md).

## Distributed KV Cache Reuse

FlexKV supports distributed KV cache reuse to share cache across multiple nodes. This enables:

- **Distributed RadixTree**: Each node maintains a local snapshot of the global index
- **Lease Mechanism**: Ensures data validity during cross-node transfers
- **RDMA-based Transfer**: Uses Mooncake Transfer Engine for high-performance KV cache transfer

For setup instructions, see the [FlexKV Distributed Reuse Guide](https://github.com/taco-project/FlexKV/blob/main/docs/dist_reuse/README_en.md).

## Architecture

FlexKV consists of three core modules:

### StorageEngine

Initializes the three-level cache (GPU → CPU → SSD/Cloud). It groups multiple tokens into blocks and stores KV cache at the block level, maintaining the same KV shape as in GPU memory.

### GlobalCacheEngine

The control plane that determines data transfer direction and identifies source/destination block IDs. Includes:
- RadixTree for prefix matching
- Memory pool to track space usage and trigger eviction

### TransferEngine

The data plane that executes data transfers:
- Multi-threading for parallel transfers
- High-performance I/O (io_uring, GDS)
- Asynchronous operations overlapping with computation

## Verify Deployment

```bash
curl localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "Hello!"}],
    "stream": false,
    "max_tokens": 30
  }'
```

## See Also

- [FlexKV GitHub Repository](https://github.com/taco-project/FlexKV)
- [FlexKV vLLM Adapter Documentation](https://github.com/taco-project/FlexKV/blob/main/docs/vllm_adapter/README_en.md)

