Best local AI models for AMD FirePro W8100

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 FirePro W8100 workstation graphics card is equipped with 8 GB of GDDR5 onboard memory. This memory size determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this VRAM limit, it executes at the highest possible speed supported by the processor architecture.

To fit larger models into the 8 GB limit, you must use quantized versions. The quant column indicates the specific level of compression applied to the model weights. For example, the Q4_K_M quantization allows the Mochi 1 10B model to occupy 7.3 GB of memory. Similarly, the Q6_K quantization allows the Mistral 7B model to run using 7.4 GB of memory. Higher quantization levels like Q6_K preserve more model accuracy but require more space than Q4_K_M or Q5_K_M variants.

If a model exceeds the 8 GB onboard memory, you must use CPU offload. This process splits the model layers between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like the FLUX.1 dev 12B model which needs 14.4 GB at FP8 or optimized settings and uses 16.4 GB of system RAM. You can also run the Gemma 3 12B model which needs 8.8 GB at Q4_K_M and uses 10.8 GB of system RAM.

CPU offload comes with a significant performance cost. Moving data between the graphics card and the system RAM over the system bus is much slower than using the onboard GDDR5 memory. While offloading allows you to run models like Vicuna 13B which needs 9.5 GB at Q4_K_M and uses 11.5 GB of system RAM, the generation speed will drop noticeably compared to fully loaded models.

Memory consumption calculations must also account for the context window. The listed memory figures assume a standard 4k context window. If you increase the context length to process longer documents or chat histories, the memory usage will rise. This extra memory demand might force you to use a lower quantization level or trigger CPU offload to avoid running out of memory.