# vllm-frontend-rs

This is a Rust drop-in alternative frontend for vLLM. The current goal is to rebuild the northbound serving layer in Rust while still talking to the core Python vLLM engine process(es) via ZMQ over the existing engine boundary.

It should still be considered experimental, and is not feature-complete. We are working to add more functionality from the python front-end.

See <https://github.com/Inferact/vllm-frontend-rs> for the original commit history before it was moved into the main vllm repo.

## Architecture

The component is organized as a Cargo workspace with several crates, layered bottom-up:

```text
┌─────────────────────────────────┐
│  vllm-cmd / vllm-rs             │  CLI entrypoint:
│                                 │  Python vLLM frontend subprocess
│                                 │  Rust managed-engine serve mode
│                                 │  Engine-free render mode
├─────────────────────────────────┤
│  vllm-server                    │  OpenAI-compatible HTTP API (axum)
├─────────────────────────────────┤
│  vllm-chat                      │  Chat completions: template rendering,
│                                 │  structured assistant events,
│                                 │  reasoning & tool parsing
├─────────────────────────────────┤
│  vllm-text                      │  Tokenizer & incremental detokenizer
├─────────────────────────────────┤
│  vllm-llm                       │  Thin token-in/token-out facade over
│                                 │  the engine client
├─────────────────────────────────┤
│  vllm-engine-core-client        │  ZMQ transport + MessagePack protocol
│                                 │  for the headless vLLM engine
└─────────────────────────────────┘
```

`vllm-rs` integrates into Python `vllm` as a Rust frontend subprocess.
Python owns process startup and launches the Rust API server as a Python-supervised worker, while
passing the inherited listening socket and transport addresses into `vllm-rs`.

For example:

```bash
VLLM_USE_RUST_FRONTEND=1 vllm serve Qwen/Qwen3-0.6B
```

### External Engine

`vllm-rs serve` can be run standalone with `--data-parallel-size-local 0` when the Python engines
are started elsewhere and this node should run only the Rust frontend. The frontend still uses
the global `--data-parallel-size` to determine how many engines it expects to join the shared handshake.

```bash
vllm serve Qwen/Qwen3-0.6B \
  --headless \
  --data-parallel-address 127.0.0.1 \
  --data-parallel-rpc-port 62100 \
  --data-parallel-size 1 \
  --data-parallel-size-local 1
```

Then start the Rust frontend-only server:

```bash
vllm-rs serve Qwen/Qwen3-0.6B \
  --data-parallel-address 127.0.0.1 \
  --data-parallel-rpc-port 62100 \
  --data-parallel-size 1 \
  --data-parallel-size-local 0
```

To build the `vllm-rs` in isolation:

```bash
# from the local checkout
./build_rust.sh
```

### Engine-free renderer

`vllm-rs render` serves text-only request preprocessing without starting or
connecting to a Python inference engine:

```bash
cargo run --manifest-path rust/Cargo.toml -p vllm-cmd --release -- \
  render Qwen/Qwen3-32B \
  --host 127.0.0.1 --max-model-len 32768
```

It exposes `/v1/chat/completions/render` and `/v1/completions/render`. Only
tokenizer and model configuration files are loaded; model weights, PyTorch,
and vLLM kernels are not required.

The render endpoints return the public token-in `GenerateRequest` consumed by
the Rust `/inference/v1/generate` endpoint. A chat render response, or one item
from a completion render response, can be submitted to that endpoint without
changing its fields.

The render and inference paths use the same `vllm-chat` and `vllm-text`
request-preparation logic; render mode stops before engine submission.
Tool-call and reasoning parsers use model-based auto-detection by default. Use
`--tool-call-parser` and `--reasoning-parser` to override either selection;
unified parsers require the same selection for both options.

```bash
curl http://127.0.0.1:8000/v1/chat/completions/render \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-32B",
    "messages": [{"role": "user", "content": "Hello"}],
    "max_completion_tokens": 16
  }'
```

### Example Request

After either full-frontend startup path, you can use any OpenAI-compatible
client against the inference endpoints:

```bash
curl http://127.0.0.1:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Qwen/Qwen3-0.6B",
    "messages": [{"role": "user", "content": "What is the capital of France?"}],
    "stream": true
  }'
```
