hanzo-ml 0.11.61

Fast multi-backend tensor & ML framework for Rust (CPU/CUDA/Metal/Vulkan/ROCm) with quantization — the compute core of the Hanzo stack.
Documentation

Hanzo ML

One tensor API. Every accelerator. The full quant zoo. Rust-native. The compute substrate behind Hanzo inference.

crates.io docs.rs license

use hanzo_ml::{Device, Tensor};

let device = Device::new_cuda(0)?; // or Cpu, new_metal(0)?, Vulkan, ROCm — same code
let a = Tensor::randn(0f32, 1., (2, 3), &device)?;
let b = Tensor::randn(0f32, 1., (3, 4), &device)?;
println!("{}", a.matmul(&b)?); // Tensor[[2, 4], f32]

Broad support, measured — not a wishlist

Six backends, one API. CPU (SIMD / Intel MKL / Apple Accelerate) · CUDA (incl. unified/managed memory for NVIDIA GB10 / DGX Spark, sm_121 Blackwell) · Metal (Apple Silicon) · Vulkan (any GPU) · ROCm (AMD RDNA3.5 APUs) · WebAssembly (models in the browser).

At llama.cpp decode parity on every GPU backend (measured, single-stream, same weights): AMD 0.98×, NVIDIA GB10 0.99–1.04×, Apple M4 Max 0.96× — decode streams the memory roofline everywhere. Prefill trails 0.76–0.91× (the tensor-core GEMM frontier) — honest engineering, not a rounding error.

The whole GGUF/GGML quant zoo — 22 formats, GPU-resident and bit-exact: Q4_0/1, Q5_0/1, Q8_0/1, K-quants (Q2_K–Q6_K), the i-quant codebooks (IQ1_S/M, IQ2_XXS/XS/S, IQ3_XXS/S, IQ4_NL/XS), ternary (TQ1_0/TQ2_0) — plus GPTQ · AWQ · AFQ · MXFP4. Every type decodes through one unified dp4a / tensor-core core, gated bit-exact against a CPU oracle.

Multimodal: text · vision · audio · 3D.

The stack

Crate What
hanzo-ml this crate — the multi-backend tensor + ML framework
hanzo-kernel the first-party GPU kernel DSL: write a kernel once, lower it to CUDA/ROCm/Vulkan/Metal/CPU
hanzo-flash-attn flash-attention-2 CUDA kernels
hanzo-kernels the hand-tuned CUDA quant kernels
Hanzo Engine the serving engine on top: OpenAI + Anthropic + MCP APIs

Install

[dependencies]
hanzo-ml = "0.11"

Enable a backend with a feature: cuda (+ cudnn, nccl, flash-attn), metal, vulkan, rocm, mkl, accelerate.

Quick start

use hanzo_ml::{Device, Tensor};

let device = Device::Cpu;
let x = Tensor::randn(0f32, 1., (1, 4096), &device)?;
let w = Tensor::randn(0f32, 1., (4096, 4096), &device)?;
let y = x.matmul(&w)?.gelu()?;

See the repository for examples, the guide book, and the model zoo.

Derived from candle and rebuilt for the Hanzo stack. Dual-licensed MIT / Apache-2.0. Models and weights: huggingface.co/hanzoai · huggingface.co/zenlm.