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We worked with @AMD to add RL, inference, notebooks, 2x faster, 70% less VRAM training on over 500 models + more to Unsloth & Unsloth Studio! > RDNA 3-4, Strix Halo, MI300, 325 + more support > Windows, Linux, WSL support > RL vLLM weight sharing + faster > Free remote HTTPS!
Introducing Unsloth for AMD 🚀 You can now train & run LLMs on your AMD hardware • We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs • Works on Windows, WSL, Linux • Train Qwen, Gemma on 3GB VRAM GitHub: https://github.com/unslothai/unsloth Works on Radeon, Instinct, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models. 🔗Blog + Guide: https://unsloth.ai/docs/basics/amd
Also unsloth start can launch your favorite harness powered by llama.cpp prebuilts + builtin @UnslothAI tool call healing to increase tool call accuracy by 50%! All runs are isolated + support for Claude, Codex, Hermes Agent, OpenClaw, OpenCode and Pi! --resume, tmux all work!
You can now fine-tune models on your personal laptops, and run the latest @GoogleGemma 4 models with as little as 3GB VRAM (🤯). 👇Stellar work, as always, from the @UnslothAI team!
Introducing Unsloth for AMD 🚀 You can now train & run LLMs on your AMD hardware • We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs • Works on Windows, WSL, Linux • Train Qwen, Gemma on 3GB VRAM GitHub: https://github.com/unslothai/unsloth Works on Radeon, Instinct, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models. 🔗Blog + Guide: https://unsloth.ai/docs/basics/amd
Introducing Unsloth for AMD 🚀 You can now train & run LLMs on your AMD hardware • We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs • Works on Windows, WSL, Linux • Train Qwen, Gemma on 3GB VRAM GitHub: https://github.com/unslothai/unsloth Works on Radeon, Instinct, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models. 🔗Blog + Guide: https://unsloth.ai/docs/basics/amd
unsloth is one of the most underrated teams in ai and it's not close. while the timeline fights about the frontier, they quietly write the kernels that let you fine-tune on 3gb of vram, 2x faster with 70% less memory, no accuracy lost. and now they've brought real training support to amd hardware that was effectively cuda only until this week. radeon, ryzen, instinct, a massive install base that couldn't touch local training just got a first class path in. the labs making the models get the headlines, the people making the models run on your actual hardware get a quote tweet. that ratio is broken. go give unsloth their due.
Introducing Unsloth for AMD 🚀 You can now train & run LLMs on your AMD hardware • We collaborated with AMD to enable you to train & run 500+ models on AMD GPUs • Works on Windows, WSL, Linux • Train Qwen, Gemma on 3GB VRAM GitHub: https://github.com/unslothai/unsloth Works on Radeon, Instinct, Ryzen and data center GPUs with up to 2× faster with 70% less VRAM and no accuracy loss via our custom Triton kernels and math algorithms. We also support optimized ROCm builds for GGUF & Safetensors inference. Unsloth is an open-source local UI for faster LLM training and inference, with tool-call healing, code execution, secure web search, remote APIs, and HTTPS deployment. Connect local models to Claude Code, Codex agents and run the latest Kimi, GLM, DeepSeek, Qwen3.6, and Gemma 4 models. 🔗Blog + Guide: https://unsloth.ai/docs/basics/amd