Best local AI models for AMD Pro W5500M

The largest models that fit fully

The 30 largest of the 81 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 W5500M is a mobile workstation graphics card equipped with 4 GB of GDDR6 dedicated memory. This hardware memory limit dictates the size of the artificial intelligence models you can run locally. To run a model entirely on the graphics processor, the model files and the active working memory must fit within this 4 GB boundary.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, Q4_K_M represents a medium four bit quantization, while Q6_K and Q8_0 represent higher quality six bit and eight bit formats. Higher quantization levels preserve more original model intelligence but require more memory.

The largest models that fit completely within the 4 GB memory limit of your graphics card include Lumina-Next or Lumina-Image 2.0 at 5B parameters using Q4_K_M quantization which takes 3.7 GB. Other options are DeepSeek-VL2 at 4.5B parameters using Q5_K_M quantization at 3.8 GB and Qwen3 4B using Q6_K quantization at 3.9 GB. These models run entirely on your graphics hardware for maximum speed.

When a model exceeds the 4 GB graphics memory limit, you can use CPU offload if your computer has at least 32 GB of system RAM. This process splits the workload between your graphics card and your system memory. Offloading allows you to run larger models like Mistral 7B using Q4_K_M quantization which needs 5.7 GB of total space and 7.7 GB of system RAM. However, offloading introduces a performance cost because transferring data between system RAM and graphics memory is much slower.

You must also consider the context window when planning your memory usage. The memory figures listed here are calculated using a baseline 4k context window. If you increase the context length to process longer documents or chat histories, the memory requirement will rise. This extra memory demand may force you to use a lower quantization level or rely more heavily on CPU offloading.