Can an RX 580 Run Gemma 4? I Tested Every Model Locally

I tested every Gemma 4 model I could run on an 8GB AMD RX 580, using Vulkan and Q4 quantized models. The goal was simple: find out how far this older budget GPU could go with modern local AI. In this video, I test: Gemma 4 E2B Gemma 4 E4B Gemma 4 12B Gemma 4 26B-A4B Gemma 4 31B The smaller models ran surprisingly well, while the larger models quickly exceeded the RX 580’s VRAM and began relying heavily on system memory. The 12B model was still usable for shorter responses, but the 26B-A4B and 31B tests became more of an experiment than a practical setup. The biggest surprise was that the RX 580 actually loaded the full Gemma 4 31B model—even though generating a normal response could take hours. This video covers model loading, GPU offloading, VRAM usage, prompt-processing speed, generation speed, and which Gemma 4 model is realistically usable on an RX 580. Hardware: AMD Radeon RX 580 8GB Backend: Vulkan Quantization: Q4 Testing: Local AI inference and benchmark prompts Subscribe for more local AI model tests, budget GPU benchmarks, and real-world open-source AI experiments. #Gemma4 #RX580 #LocalAI #AMD #Vulkan #AIModels #LLM #OpenSourceAI #LocalLLM #AIBenchmark #GPU #MachineLearning #Gemma #BudgetGPU #OfflineAI