Best local AI models for AMD PRO W6600

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 W6600 is a professional workstation graphics card equipped with 8 GB GDDR6 of dedicated video memory. This onboard memory capacity determines the size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this limit, it executes with the fastest possible processing speeds because the graphics processor has direct high speed access to all the model parameters.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of the model weights to make the file size smaller. For example, a Q4_K_M quant uses a four bit medium quantization method, while a Q6_K quant uses a six bit method. Higher quantization levels like Q6_K preserve more of the original model accuracy but require more memory. Lower levels like Q4_K_M allow larger models to fit inside your hardware limits.

For models that fit entirely on the card, you can run Gemma 2 9B at the Q4_K_M quant using exactly 8 GB of video memory. The Mochi 1 10B model fits at Q4_K_M using 7.3 GB. You can also run Llama 3.1 8B at Q5_K_M using 7.4 GB. Models like Granite 3.3 8B, Ministral 8B, and InternLM 3 8B run at Q6_K using 7.9 GB. Popular 7B models such as Mistral 7B use 7.4 GB at Q6_K, while Qwen2.5 7B and Command R7B use 6.9 GB at Q6_K.

When a model exceeds the 8 GB video memory limit, you must use CPU offload. This technique splits the model between your graphics card and your system memory. Offloading allows you to run larger models but introduces a speed penalty because data must travel across the slower system bus. For these cases, we assume your workstation has 32 GB of system RAM to handle the shared workload.

With CPU offload, you can run the FLUX.1 dev 12B model at FP8 which needs 14.4 GB of memory and 16.4 GB of system RAM. The Gemma 3 12B, Gemma 4 12B, and Mistral NeMo 12B models need 8.8 GB at Q4_K_M and 10.8 GB of system RAM. The Vicuna 13B model needs 9.5 GB at Q4_K_M and 11.5 GB of system RAM. These larger models will run slower than the fully on card models.

You must also consider the context window size when planning your memory usage. The memory figures 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 memory usage will increase. This extra memory demand might push a model that normally fits on the card into CPU offload territory.