Best local AI models for AMD Pro 5500M

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 5500M is a mobile graphics processor equipped with 8 GB of GDDR6 video memory. This dedicated memory pool determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 8 GB limit, execution is fast because the graphics processor can access the model weights directly without waiting for the slower system memory.

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. A Q4_K_M quant represents a four bit medium quantization, while Q5_K_M and Q6_K represent five bit and six bit configurations. Higher quantization levels like Q6_K preserve more of the original model quality but require more video memory. For example, Mistral 7B fits at Q6_K using 7.4 GB of memory, while Gemma 2 9B requires a Q4_K_M quant to fit within 8 GB of memory.

If a model exceeds the 8 GB video memory limit, you must use CPU offloading. This process splits the model layers between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these scenarios. Offloading allows you to run larger models like Gemma 3 12B or Mistral NeMo 12B, which need 8.8 GB of memory at Q4_K_M and 10.8 GB of system RAM. However, offloading introduces a speed penalty because data must travel over the system bus.

Some very large models require significant system memory resources. Running FLUX.1 dev at FP8 requires 14.4 GB of memory and 16.4 GB of system RAM. Similarly, Vicuna 13B requires 9.5 GB of memory at Q4_K_M and 11.5 GB of system RAM. While CPU offloading makes these models accessible on your hardware, the generation speed will be noticeably slower than running smaller models that fit entirely within the 8 GB video memory.

You must also consider the memory required for the context window. The memory figures listed for these models are calculated using a standard 4k context window. If you increase the context length to process longer documents or extended conversations, the memory usage will rise. This extra memory demand might force you to use a lower quantization level or rely on CPU offloading to prevent out of memory errors.