Best local AI models for AMD Pro 450

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 450 is a legacy mobile graphics processor equipped with 2 GB of GDDR5 video memory. This dedicated memory size dictates the maximum scale of artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 2 GB boundary, it executes with the highest possible speed because the system does not need to swap data to your computer system memory.

To fit modern models into this compact footprint, developers use quantization. The quantization column shows the specific compression level required to run each model. For example, the Allegro 2.8B model fits by using the Q4_K_M quantization which reduces the footprint to exactly 2 GB. Smaller models like SmolLM2 1.7B can run at a higher quality Q6_K quantization while using 1.7 GB of video memory. TinyLlama 1.1B runs at the premium Q8_0 quantization and uses only 1.4 GB of video memory.

When you want to run larger models like the SmolLM3 3B or Stable Diffusion XL, you must use CPU offload. This technique splits the workload between your graphics card and your computer system RAM. Running the 3.5B SDXL Turbo model requires 2.6 GB of video memory at Q4_K_M quantization and an additional 4.6 GB of system RAM. While CPU offload allows you to run these larger architectures, it comes with a severe performance cost because system RAM is much slower than dedicated GDDR5 video memory.

You must also consider the memory cost of context length when running text models. The listed memory usage figures assume a standard base context. If you increase your active context window to 4k tokens, the system must allocate extra memory to store the attention history. This additional allocation can easily push a model that sits right at the 2 GB limit over the threshold, forcing the system into slow CPU offload mode.

For the best local experience on this hardware, choose models that naturally fit under the 2 GB threshold at high quantization levels. Running models like the Canary 1B or Qwen-2.5B at Q4_K_M quantization uses 1.8 GB of video memory, leaving a safe margin for your display and basic system tasks without triggering slow memory swapping.