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ai-agent

Fast LLM Inference with hipfire

Get fast AI inference for startups with hipfire, a Rust-based LLM engine, ideal for machine learning founders
intermediateโฑ 30 minutes๐Ÿ’ต Free (self-hosted)
482 stars49 forksRustQuality 8/10Updated 7/15/2026100% free ยท open source
What it is

Use this skill to run fast Large Language Model (LLM) inference on your AMD GPUs.

What you can make with it

Automations like integrating AI-driven product recommendations into your e-commerce website by pulling data from Amazon and applying machine learning models.

How it helps

You can optimize your machine learning stack without relying on NVIDIA dependencies, which saves cost and time.

Real use case example

"A founder, Alex, wants to personalize product recommendations on her e-commerce website. She uses this skill to run LLM inference on her AMD GPU, which processes user data and product information, and then integrates the optimized recommendations into her website. As a result, her website sees a significant increase in sales conversion rates."

If you're new

Novice developers should pick this up when they want to start building simple machine learning projects or integrations.

If you're senior

Senior engineers and professionals should reach for this skill when they need to optimize their large-scale machine learning infrastructure without the need for NVIDIA hardware.

Common confusion cleared up

This skill is specifically designed for running LLM inference on AMD GPUs, but it can be used for other types of machine learning tasks too.

Best inside these AI tools
Claude DesktopGeminiGemini Code AssistSelf-hosted
Pairs with
Claude APIStripe webhookNotion database
Why we list it on WorkflowStacks: This skill is included here because it's a free and open-source solution for running LLM inference on AMD GPUs, offering a cost-effective alternative.
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
  • โ€ขYou're building an AI-powered startup and want to reduce costs by avoiding NVIDIA hardware
  • โ€ขYour machine learning model requires high-performance inference on AMD RDNA hardware
  • โ€ขYou need to deploy AI models in environments where NVIDIA hardware is not available or feasible
Quick start
  1. 1Clone the Hipfire repository from GitHub and build the Rust project using Cargo
  2. 2Prepare your machine learning model for inference by converting it to a compatible format
  3. 3Integrate Hipfire into your application using the provided Rust API or command-line interface
  4. 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 โ€” free to inspect
No purchase needed

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

23
top-level files
19
folders
81.3M
repo size
Other
license
Key files
AGENTS.md
README.md
File tree
.agent-memory/
.agents/
.githooks/
.github/
autoresearch/
bench/
benchmarks/
cli/
crates/
docker/
docs/
experiments/
findings/
kernels/
nix/
registry/
scripts/
tests/
third_party/
.gitignore
.gitmodules
AGENTS.md
Cargo.lock
Cargo.toml
Quick Actions
Details
Creator
Kaden-Schutt
Language
Rust
Category
ai-agent
Published
3/21/2026

Are you the creator of this tool? Claim your listing โ†’ and earn 85% of every sale.

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