Unsloth now supports AMD!

Hey r/LocalLLaMA folks! Unsloth now officially supports AMD hardware for local inference, fine-tuning, reinforcement learning, and deployment! It's been in the works for quite some time, but it works on Windows, Linux & WSL devices (+ technically Mac) with AMD GPUs!

Unsloth Studio is fully open source and free, and supports:

• Radeon RX 9000 and 7000 series
• Instinct MI350 and MI300 GPUs
• Strix Halo / Ryzen AI Max systems
• AMD CPUs for GPU-free inference

You can train models with up to 70% less VRAM, run reinforcement learning with up to 80% less VRAM, and use optimized ROCm, Triton, bitsandbytes, PyTorch, and llama.cpp builds - all installed automatically.

Linux, WSL, and macOS:

curl -fsSL https://unsloth.ai/install.sh | sh 

Windows PowerShell:

irm https://unsloth.ai/install.ps1 | iex 

Unsloth supports inference and training for nearly all models, including Qwen, Gemma, DeepSeek, GLM, Kimi, MiniMax, and DiffusionGemma.

You can also:

• Export models as GGUF, safetensors, or LoRA adapters
• Connect local models to Claude Code, Codex, Hermes Agent, OpenClaw, Pi, OpenCode!
• Track RAM and VRAM usage during training - remotely and locally
• Access Unsloth remotely through secure Cloudflare HTTPS tunneling - like a "LM Link"!
• Update with daily AMD-optimized llama.cpp ROCm prebuilts to reduce compilation time!

For plain pip installation:

uv pip install "unsloth[amd]" 

Huge thanks to the AMD team for collaborating with us on this release! Let us know what AMD hardware you’re using and share any feedback - we'll try to make AMD much better!

More details on the release blog: https://unsloth.ai/docs/basics/amd