Fast, flexible LLM inference.

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Latest

• Anthropic Messages API: hanzo serve now exposes an Anthropic-compatible POST /v1/messages endpoint (streaming, tool use, and Claude Code harness support) alongside the OpenAI-compatible /v1 API. Examples
• 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

Why hanzo?

• Any Hugging Face model, zero config: Just hanzo 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
• OpenAI + Anthropic compatible serving: The same hanzo serve process exposes OpenAI-compatible /v1 endpoints and an Anthropic-compatible Messages endpoint.
• 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: hanzo 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/hanzoai/engine/master/install.sh | sh

Windows (PowerShell):

irm https://raw.githubusercontent.com/hanzoai/engine/master/install.ps1 | iex

Manual installation & other platforms

Run Your First Model

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

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

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

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

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

For the server command, visit http://localhost:1234/ui for the web chat interface. OpenAI-compatible clients use http://localhost:1234/v1; Anthropic-compatible clients use http://localhost:1234.

The hanzo 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 hanzo 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
hanzo tune -m Qwen/Qwen3-4B --emit-config config.toml

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

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

Full CLI documentation

UI Demo

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

Text 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

Multimodal Models

• 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

Speech Models

• Voxtral (ASR/speech-to-text)
• Dia

Image Generation Models

• FLUX

Embedding Models

• Embedding Gemma
• Qwen 3 Embedding

Request a new model | Full compatibility tables

Python SDK

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

from hanzo 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 hanzo

use anyhow::Result;
use hanzo::{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/hanzoai/engine:latest
docker run --gpus all -p 1234:1234 ghcr.io/hanzoai/engine: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 Hanzo. Thank you to all contributors!

hanzo is not affiliated with Mistral AI.

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