Best local AI models for AMD 550X

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 550X is an entry level graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory determines which artificial intelligence models can run entirely on your hardware. When a model fits completely within this 2 GB limit, it executes much faster because the system does not need to transfer data back and forth across the slow system bus.

To fit larger models into this memory limit, developers use quantization. The quantization column shows the optimal compression level for each model. For example, the Allegro 2.8B model fits into 2 GB of memory when compressed to the Q4_K_M quantization. Smaller models like the SmolLM2 1.7B can run at a higher quality Q6_K quantization while using 1.7 GB of memory. TinyLlama 1.1B runs at the high quality Q8_0 quantization using only 1.4 GB of memory.

If you want to run models that exceed the 2 GB limit, you must use CPU offloading. This process splits the model between your graphics card and your system RAM. A typical setup with 32 GB of system RAM can run the 3B SmolLM3 or Replit Code v1.5 3B. These models need 2.2 GB of video memory at Q4_K_M quantization and require an additional 4.2 GB of system RAM. Offloading allows you to run larger models like the 3.5B SDXL Turbo, but the processing speed will drop significantly.

Running models at their maximum capacity leaves very little room for context. If you load a 2 GB model like the 2B SmolVLM or the 2B Stable Diffusion 3 Medium, your video memory is completely full. This leaves no space for the active memory required during generation. For text models, this limitation means you cannot use long chat histories or large prompt contexts without causing memory errors or severe slowdowns.

For the best balance of speed and quality on the AMD 550X, you should target models that leave some free video memory. Running the 1.6B Sana or the 1.5B Whisper Large v2 at Q8_0 quantization utilizes 2 GB and 1.9 GB of memory. If you experience performance issues, dropping down to smaller models like the 1.1B SantaCoder will free up valuable memory space and ensure smoother operation.