Best local AI models for AMD PRO W7600

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 W7600 is a professional workstation graphics card equipped with 8 GB of GDDR6 memory. This dedicated video memory determines the size of the artificial intelligence models you can run entirely on the hardware. To run a model smoothly without system slowdowns, the model files and the active processing data must fit within this 8 GB limit.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save memory. For example, the Mochi 1 10B model fits in 7.3 GB of video memory using the Q4_K_M quantization. Other models like Gemma 2 9B require exactly 8 GB of memory at the Q4_K_M quantization level. Models like Llama 3.1 8B fit comfortably in 7.4 GB of memory using the higher quality Q5_K_M quantization.

Many popular models can run at the Q6_K quantization level on this hardware. Mistral 7B uses 7.4 GB of video memory at Q6_K. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large all use 7.9 GB of video memory at the Q6_K level. EXAONE 3.5 7.8B uses 7.7 GB of video memory at Q6_K. Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all require 6.9 GB of video memory at Q6_K.

When a model exceeds the 8 GB video memory limit, you must offload parts of the model to your system RAM. This offloading process allows you to run larger models but reduces processing speed because system RAM is slower than GDDR6 memory. For these offload cases, we assume your computer has 32 GB of system RAM. Under this setup, Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev require 8.8 GB of memory at Q4_K_M and need 10.8 GB of system RAM.

Other offload options include Open-Sora 2.0 which requires 8.1 GB at Q4_K_M and 10.1 GB of system RAM. FLUX.1 dev requires 14.4 GB at FP8 or optimized settings and needs 16.4 GB of system RAM. Vicuna 13B requires 9.5 GB at Q4_K_M and needs 11.5 GB of system RAM. These configurations let you run advanced architectures at the cost of generation speed.

You must also consider the context window size when planning your memory usage. The memory numbers listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or larger chat histories, the system will require additional video memory. This extra memory demand may force you to use a lower quantization level or rely on CPU offloading.