ai-agent

Run Fast LLM Inference on AMD GPUs with hipfire

Name: Run Fast LLM Inference on AMD GPUs with hipfire
Author: Kaden-Schutt

A high-performance Rust-based inference engine for founders building AI on RDNA hardware. Optimize your machine learning stack without NVIDIA dependencies.

413 stars45 forksRustQuality 9/10Updated 6/2/2026100% free · open source

What it does

Hipfire is a high-performance inference engine that allows founders to run machine learning models on RDNA hardware without relying on NVIDIA dependencies.

When to use it

•You're building an AI-powered startup and want to reduce costs by avoiding NVIDIA hardware
•Your machine learning model requires high-performance inference on AMD RDNA hardware
•You need to deploy AI models in environments where NVIDIA hardware is not available or feasible

Quick start

1Clone the Hipfire repository from GitHub and build the Rust project using Cargo
2Prepare your machine learning model for inference by converting it to a compatible format
3Integrate Hipfire into your application using the provided Rust API or command-line interface
4Test and optimize your model's performance on RDNA hardware using Hipfire's benchmarking tools

Ready-to-paste prompt

cargo run --release --example inference --model-path /path/to/your/model.onnx --input-path /path/to/your/input.data

Saves to your device

Topics

amd-gpu

gpu-computing

hip

llm-inference

machine-learning

quantization

rdna

rocm

rust

What's inside — free to inspect

No purchase needed

Read the entire source before you build — unlike paid marketplaces that hide it behind a buy button.

top-level files

folders

64.1M

repo size

Other

license

Key files

AGENTS.md

README.md

File tree

.agents/

.githooks/

.github/

bench/

benchmarks/

cli/

crates/

docker/

docs/

experiments/

findings/

kernels/

nix/

registry/

scripts/

tests/

third_party/

.gitignore

.gitmodules

AGENTS.md

Cargo.lock

Cargo.toml

CHANGELOG.md

CITATION.cff

Quick Actions

Details

Creator

Kaden-Schutt

Language

Rust

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