Best local AI models for AMD Pro 555

The largest models that fit fully

The 30 largest of the 56 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 555 is an entry level graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. To load a model entirely onto the graphics processor, the model files and the active memory space must not exceed this 2 GB limit. Running models directly on your hardware ensures private data processing and eliminates subscription fees.

The quantization column indicates the compression level used to fit these models into your hardware. Quantization reduces the precision of model weights to save space. For example, the 2.8B Allegro and 2.7B Open-Sora Plan models fit into 2 GB of memory using a Q4_K_M quantization. Models like the 2.2B Parler-TTS and 2.2B Kimi K3 DSpark use a Q5_K_M quantization. Smaller models like the 2B SmolVLM, 2B Stable Diffusion 3 Medium, and 2B Moondream can run at a higher Q6_K quantization.

Very small models can run at maximum precision. The 1.6B StableLM 2, 1.6B Sana, 1.6B Zonos 0.1, and 1.55B Whisper Large v3 fit within the 2 GB memory limit using Q8_0 quantization. This high precision level is also available for the 1.5B ControlNet, 1.5B Hunyuan-DiT, and 1.5B AudioGen models. Using Q8_0 quantization preserves the original output quality of these smaller architectures.

When a model exceeds the 2 GB limit, you must use CPU offloading. This process splits the model layers between your graphics card and your system memory. For example, running the 3B SmolLM3, 3B Kandinsky 3.1, or 3B Orpheus TTS requires 2.2 GB of video memory at Q4_K_M and 4.2 GB of system RAM. The 3.5B SDXL Turbo and 3.5B SDXL Lightning require 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM. CPU offloading allows you to run larger models but reduces processing speed.

You must also consider the memory cost of context length. The memory figures listed for these models assume a standard 4k context window. If you increase the context window to process longer documents or extended conversations, the system will require additional video memory. This extra memory demand can cause a model that normally fits within 2 GB to overflow into system RAM, which slows down response times.