@diy_smart_code: Unsloth now trains LLMs on AMD GPUs — fine-tune local AI models on a Radeon, Ryzen AI laptop, or Instinct card. 500+ models, 3GB VRAM minimum, 2x faster training, 70% less VRAM, and it runs on Windows, WSL, and Linux. ---- 🚀 DYNAMOUS AI COMMUNITY Want to learn agentic coding with live daily events and workshops? Check out Dynamous AI: https://dynamous.ai/?code=646a60 Get 10% off here 👉 https://shorturl.smartcode.diy/dynamous_ai_10_percent_discount ⚡ HOSTINGER — RELIABLE HOSTING FOR YOUR PROJECTS (10% OFF) Whether you're shipping a portfolio, a side project, n8n flows, or AI agents — I use Hostinger for fast, affordable VPS + web hosting. Get 10% off here 👉 https://hostinger.com/DIYSMARTCODE (Affiliate link — costs you nothing, supports the channel.) ---- What you will see in this 80-second breakdown: → Unsloth adds AMD support — train and fine-tune LLMs without an NVIDIA card → Which hardware works: Radeon RX 9000 / 7000 / 6000, Ryzen AI laptops, Instinct MI300 / MI350 → As little as 3GB of VRAM to fine-tune a small model on a card you already own → 500+ models, up to 2x faster training, 70% less VRAM, no accuracy loss → Runs on Windows, WSL, and Linux — export to GGUF, safetensors, or a LoRA adapter, then wire it into Claude Code → One-line install: curl the script and start fine-tuning on your own machine Unsloth AMD docs: https://unsloth.ai/docs/basics/amd Unsloth GitHub: https://github.com/unslothai/unsloth For years, fine-tuning a model meant one thing: an NVIDIA card. Unsloth just knocked that wall down — so here's the real question. Does training on your own AMD hardware finally break NVIDIA's grip on AI, or is CUDA still too far ahead? Radeon or GeForce — which one are you training on? Drop it in the comments. #Unsloth #AMD #AMDGPU #Radeon #LLM #LocalAI #FineTuning #MachineLearning #AI #OpenSourceAI #ROCm #RyzenAI #AMDInstinct #ArtificialIntelligence #LLMTraining #AICoding #DeepLearning #Gemma #ClaudeCode #DevTools #LocalLLM #AMDdeveloper

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Tuesday 21 July 2026 15:16:08 GMT