Best local AI models for AMD 550

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 550 graphics card features 2 GB of GDDR5 memory. This onboard memory size dictates the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this limit, it runs at the maximum speed the hardware can deliver. If a model exceeds this limit, you must use alternative execution strategies.

The quantization column indicates the specific compression level applied to each model. Quantization reduces the precision of model weights to save memory. For example, the Allegro 2.8B model fits in 2 GB of memory when using the Q4_K_M quantization. Smaller models like the SmolLM2 1.7B can run at the higher quality Q6_K quantization while using 1.7 GB of memory. TinyLlama 1.1B can run at the Q8_0 quantization level using 1.4 GB of memory.

For models that exceed the onboard memory, you can offload parts of the workload to your system RAM. This approach assumes your computer has 32 GB of system RAM. Offloading allows you to run larger models like the 3B SmolLM3 or the 3.5B SDXL Turbo. The SDXL Turbo model needs 2.6 GB of memory at Q4_K_M quantization and requires 4.6 GB of system RAM to function.

Offloading comes with a performance cost. System RAM is significantly slower than the GDDR5 memory on your graphics card. While offloading makes it possible to run models like Stable Diffusion XL which needs 4.1 GB at FP8 and 6.1 GB of system RAM, the generation speed will be much slower than running fully on the graphics card.

You must also consider the memory cost of context length. Running text models with a standard 4k context window requires additional memory beyond the base model weights. When running models near the 2 GB limit, such as the LFM2 2.6B model using 1.9 GB of memory, you may need to reduce the context window to avoid running out of memory.