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 unsloth.ai/install.sh | sh Windows PowerShell: irm 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: unsloth.ai/docs/basics/amd