Best local AI models for AMD Pro W5500

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 W5500 is a professional workstation graphics card equipped with 8 GB of GDDR6 memory. This onboard memory capacity determines the maximum size of the artificial intelligence models you can run entirely on the hardware. Keeping model files within this limit ensures fast processing speeds because the graphics processor can access the data directly.

The quantization column indicates the compression level used to fit these models into the available memory. Quantization reduces the precision of model weights to save space. For example, the Gemma 2 9B model fits at the Q4_K_M quantization level which uses exactly 8 GB of memory. Other models like the Granite 3.3 8B or Ministral 8B can run at a higher quality Q6_K quantization level which uses 7.9 GB of memory.

When a model exceeds the 8 GB limit of the graphics card you must use CPU offloading. This process splits the workload between your graphics card and your system memory. If you have 32 GB of system RAM you can run larger models like Mistral NeMo 12B or Gemma 3 12B. These models require 8.8 GB of video memory at Q4_K_M quantization and need an additional 10.8 GB of system RAM to function.

CPU offloading allows you to run advanced models but it comes with a performance cost. Transferring data between the system RAM and the graphics card is much slower than using onboard GDDR6 memory. Models like FLUX.1 dev require 14.4 GB of memory at FP8 or optimized settings and need 16.4 GB of system RAM which will result in slower generation speeds.

You must also consider the memory required for context. The listed memory usage figures represent the base model size. Running a model with a standard 4k context window requires additional memory to store the active conversation history. If your model already uses close to the 8 GB limit like Gemma 2 9B at 8 GB you may experience slowdowns or errors as the context window fills up.