Unsloth Ships AMD GPU Support, Bringing 2x Faster Fine-Tuning to Radeon

Unsloth, the popular open-source LLM fine-tuning toolkit with 66.5k GitHub stars, just shipped official AMD GPU support. Built in collaboration with AMD's engineering team, the release brings Unsloth's signature speed and memory tricks to Radeon, Instinct, and Ryzen AI hardware, covering everything from consumer RX 9000 cards to MI350 data center GPUs. The pitch: up to 2x faster training and 70% less VRAM usage, with no accuracy loss.

The problem this actually solves

AMD GPU owners have been second-class citizens in the LLM fine-tuning world. The ROCm software stack has matured significantly in 2026, but the tooling gap remained painful. For years, running large language models locally meant buying an NVIDIA GPU, not because AMD hardware was bad, but because the software ecosystem lagged far behind CUDA in compatibility and stability. Unsloth's AMD release directly attacks that gap by porting its custom Triton kernels and memory-efficient training algorithms to ROCm.

The VRAM story is particularly compelling. Unsloth collaborated with AMD to enable up to 2x faster and 70% less VRAM training for all models with no accuracy loss. That means you can now train Gemma 4 models in 8GB VRAM or Qwen3.5 in 3GB VRAM on AMD hardware, numbers that were previously only achievable on NVIDIA.

What hardware is supported

Unsloth now officially supports AMD hardware, making it easy to train, fine-tune, run reinforcement learning, and deploy models locally on AMD GPUs, with support for AMD's Radeon RX 9000, 7000 series, Instinct MI350 and MI300 data center GPUs, Strix Halo powered Ryzen AI Max systems, and more.

The full breakdown by architecture:

• RDNA 4 (RX 9000 series): Full support on Windows, WSL, and Linux
• RDNA 3.5 (Ryzen AI 300/MAX, Strix Halo): Full support on all platforms
• RDNA 3 (RX 7000 series): Full support on all platforms
• CDNA 4/3/2 (Instinct MI350, MI300, MI200): Full support on Linux