README

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Latest

• v0.8.2 CUDA performance: CUDA graphs, FlashInfer paged kernels, and MoE optimizations deliver strong results on GB10, B200, and H100 SXM. Benchmarks
• Agentic runtime: web search, local Python code execution with model feedback, session management, and custom tool hooks. Guide
• Gemma 4: full multimodal: text, image, video, and audio input. Guide | Video setup
• MXFP4 ISQ quantization: MXFP4 with optimized decode kernels for faster, smaller models. Quantization docs

Benchmarks

Mean tokens per second across prompt lengths and decode depths from 128 to 16384 tokens. Decode uses 256 generated tokens. See the full v0.8.2 report for commands, model revisions, host metadata, and appendix tables.

Q8 prefill TPS: mistral.rs UQFF q8 vs llama.cpp GGUF Q8_0

Q8 decode TPS: mistral.rs UQFF q8 vs llama.cpp GGUF Q8_0

BF16 prefill TPS: mistral.rs BF16 vs vLLM BF16

BF16 decode TPS: mistral.rs BF16 vs vLLM BF16

Why mistral.rs?

• Any Hugging Face model, zero config: Just mistralrs run -m user/model. Architecture, quantization format, and chat template are auto-detected.
• True multimodality: Text, vision, video, and audio, speech generation, image generation, and embeddings in one engine.
• Smart quantization: --quant automatically selects the best quantization format at that level: using a prebuilt UQFF if one is published, otherwise applying ISQ. Docs
• Built-in web UI: Served at /ui by default. Shows reasoning, code execution, plots, and files inline. Edit any message and the new branch runs with its own Python state. Pass --no-ui to disable.
• Hardware-aware: mistralrs tune benchmarks your system and picks optimal quantization + device mapping.
• Flexible SDKs: Python package and Rust crate to build your projects.
• Native agentic support: built-in agentic loop with web search, local Python code execution with model feedback, session management, and custom tool hooks.

Quick Start

Install

Linux/macOS:

curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/EricLBuehler/mistral.rs/master/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/EricLBuehler/mistral.rs/master/install.ps1 | iex

Manual installation & other platforms

Run Your First Model

# Interactive chat
mistralrs run -m Qwen/Qwen3-4B

# One-shot prompt (no interactive session)
mistralrs run -m Qwen/Qwen3-4B -i "What is the capital of France?"

# One-shot with an image
mistralrs run -m google/gemma-4-E4B-it --image photo.jpg -i "Describe this image"

# Agentic REPL: search + code execution from the terminal
mistralrs run --agent -m Qwen/Qwen3-4B

# Start an API server with the built-in web UI
mistralrs serve -m google/gemma-4-E4B-it

For the server command, visit http://localhost:1234/ui for the web chat interface.

The mistralrs CLI

The CLI is designed to be zero-config: just point it at a model and go.

• Auto-detection: Automatically detects model architecture, quantization format, and chat template
• All-in-one: Single binary for chat, server, benchmarks, and web UI (run, serve, bench)
• Hardware tuning: Run mistralrs tune to automatically benchmark and configure optimal settings for your hardware
• Format-agnostic: Works with Hugging Face models, GGUF files, and UQFF quantizations seamlessly

# Auto-tune for your hardware and emit a config file
mistralrs tune -m Qwen/Qwen3-4B --emit-config config.toml

# Run using the generated config
mistralrs from-config -f config.toml

# Diagnose system issues (CUDA, Metal, HuggingFace connectivity)
mistralrs doctor

Full CLI documentation

What Makes It Fast

Performance

• Continuous batching support by default on all devices.
• CUDA with FlashAttention V2/V3, Metal, multi-GPU tensor parallelism
• PagedAttention for high throughput continuous batching on CUDA or Apple Silicon, prefix caching (including multimodal)

Quantization (full docs)

• In-situ quantization (ISQ) of any Hugging Face model
• GGUF (2-8 bit), GPTQ, AWQ, HQQ, FP8, BNB support
• ⭐ Per-layer topology: Fine-tune quantization per layer for optimal quality/speed
• ⭐ Auto-select fastest quant method for your hardware

Flexibility

• LoRA & X-LoRA with weight merging
• AnyMoE: Create mixture-of-experts on any base model
• Multiple models: Load/unload at runtime

Agentic Features

• Integrated tool calling with grammar enforcement and strict schema mode
• ⭐ Server-side agentic loop: auto-execute tools and feed results back
• ⭐ Python code execution: persistent Jupyter-like sessions with matplotlib capture and multimodal feedback
• ⭐ Web search integration with embedding-based ranking
• ⭐ Tool dispatch URL: POST tool calls to your own endpoint
• ⭐ MCP client: Connect to external tools via Process, HTTP, or WebSocket
• Python/Rust tool callbacks for custom execution

Full feature documentation

Supported Models

• Granite 4.0
• SmolLM 3
• DeepSeek V3
• GPT-OSS
• DeepSeek V2
• Qwen 3 Next
• Qwen 3 MoE
• Phi 3.5 MoE
• Qwen 3
• GLM 4
• GLM-4.7-Flash
• GLM-4.7 (MoE)
• Gemma 2
• Qwen 2
• Starcoder 2
• Phi 3
• Mixtral
• Phi 2
• Gemma
• Llama
• Mistral

• Qwen 3.5
• Qwen 3.5 MoE
• Qwen 3-VL
• Qwen 3-VL MoE
• Gemma 3n
• Llama 4
• Gemma 3
• Mistral 3
• Phi 4 multimodal
• Qwen 2.5-VL
• MiniCPM-O
• Llama 3.2 Vision
• Qwen 2-VL
• Idefics 3
• Idefics 2
• LLaVA Next
• LLaVA
• Phi 3V

• Voxtral (ASR/speech-to-text)
• Dia

• FLUX

• Embedding Gemma
• Qwen 3 Embedding

Request a new model | Full compatibility tables

Python SDK

pip install mistralrs  # or mistralrs-cuda, mistralrs-metal, mistralrs-mkl, mistralrs-accelerate

from mistralrs import Runner, Which, ChatCompletionRequest

runner = Runner(
    which=Which.Plain(model_id="Qwen/Qwen3-4B"),
    in_situ_quant="4",
)

res = runner.send_chat_completion_request(
    ChatCompletionRequest(
        model="default",
        messages=[{"role": "user", "content": "Hello!"}],
        max_tokens=256,
    )
)
print(res.choices[0].message.content)

Python SDK | Installation | Examples | Cookbook

Rust SDK

cargo add mistralrs

use anyhow::Result;
use mistralrs::{IsqType, TextMessageRole, TextMessages, MultimodalModelBuilder};

#[tokio::main]
async fn main() -> Result<()> {
    let model = MultimodalModelBuilder::new("google/gemma-4-E4B-it")
        .with_isq(IsqType::Q4K)
        .with_logging()
        .build()
        .await?;

    let messages = TextMessages::new().add_message(
        TextMessageRole::User,
        "Hello!",
    );

    let response = model.send_chat_request(messages).await?;

    println!("{:?}", response.choices[0].message.content);

    Ok(())
}

API Docs | Crate | Examples

Docker

For quick containerized deployment:

docker pull ghcr.io/ericlbuehler/mistral.rs:latest
docker run --gpus all -p 1234:1234 ghcr.io/ericlbuehler/mistral.rs:latest \
  serve -m Qwen/Qwen3-4B

Docker images

For production use, we recommend installing the CLI directly for maximum flexibility.

Documentation

For complete documentation, see the Documentation.

Quick Links:

• CLI Reference - All commands and options
• HTTP API - OpenAI-compatible endpoints
• Quantization - ISQ, GGUF, GPTQ, and more
• Device Mapping - Multi-GPU and CPU offloading
• MCP Integration - MCP integration documentation
• Troubleshooting - Common issues and solutions
• Configuration - Environment variables for configuration

Contributing

Contributions welcome! Please open an issue to discuss new features or report bugs. If you want to add a new model, please contact us via an issue and we can coordinate.

Credits

This project would not be possible without the excellent work at Candle. Thank you to all contributors!

mistral.rs is not affiliated with Mistral AI.