Best local AI models for AMD Pro W5500X

The largest models that fit fully

The 30 largest of the 123 models that fit; every smaller model in the catalog fits too. Best quant means the highest quality compression whose weights and 4k context both sit inside the memory.

Close, but only with CPU offload

These need more than the card holds at their smallest practical quant, so part of the model runs from system memory (figures assume 32 GB of it). They work, several times slower.

How to read this

The AMD Radeon Pro W5500X is equipped with 8 GB of GDDR6 memory. This onboard VRAM determines the size of the artificial intelligence models you can run locally. When a model fits entirely within this 8 GB limit, the GPU processes tokens at maximum speed. If a model exceeds this limit, you must offload some layers to your system memory, which slows down performance.

To fit larger models into the 8 GB VRAM, you must use quantized versions. Quantization reduces the precision of model weights to save space. The best quantization level for your hardware depends on the parameter size of the model. For example, the 10B Mochi 1 fits at Q4_K_M quantization using 7.3 GB of memory. Gemma 2 9B fits at Q4_K_M quantization using exactly 8 GB of memory.

Several 9B models run comfortably on this hardware at Q5_K_M quantization, utilizing 7.7 GB of memory. These include Nemotron Nano 4B / 9B, GLM-4 9B / GLM-4.5-Air, Yi-Coder 1.5B / 9B, GLM-4-9B-Chat / CodeGeeX4, and GLM-4V-9B / GLM-4.1V-Thinking. Chroma is an 8.9B model that fits at Q5_K_M quantization using 7.6 GB of memory. Llama 3.1 8B also fits at Q5_K_M quantization using 7.4 GB of memory.

Many 8B models can run at a higher quality Q6_K quantization, requiring 7.9 GB of memory. This group includes Granite 3.3 2B / 8B, Ministral 3B / 8B, InternLM 3 8B, OpenCoder 1.5B / 8B, Seed-Coder 8B, MiniCPM-V 2.6 / MiniCPM-o 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large / Turbo. EXAONE 3.5 2.4B / 7.8B fits at Q6_K quantization using 7.7 GB of memory.

Smaller 7B models run efficiently at Q6_K quantization using 6.9 GB of memory. This includes Mistral 7B at 7.4 GB used, alongside Qwen2.5 0.5B / 1.5B / 3B / 7B, OLMo 2 1B / 7B, Falcon 3 1B / 3B / 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B. These models leave a small safety margin in your VRAM.

When running models that exceed 8 GB, you can offload layers to your system RAM if you have 32 GB of system memory. Open-Sora 2.0 is an 11B model that needs 8.1 GB at Q4_K_M and 10.1 GB of system RAM. FLUX.1 dev is a 12B model that needs 14.4 GB at FP8 / optimized and 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM.

Other 12B models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev need 8.8 GB at Q4_K_M and 10.8 GB of system RAM. Note that memory calculations assume a standard 4k context window. Increasing the context window size requires more memory and might force you to use lower quantization levels.