Best local AI models for AMD R9 M470

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 R9 M470 graphics card features 2 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run directly on the hardware. When running local models, the entire model weights must ideally fit inside this 2 GB limit to ensure acceptable processing speeds.

The quantization column indicates the compression level applied to each model. For example, the Allegro 2.8B model fits in 2 GB of video memory when using the Q4_K_M quantization. Smaller models like the SmolLM2 1.7B can use 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 video memory.

If a model exceeds the 2 GB limit, you must use CPU offloading. This process splits the model between your graphics card and your system memory. For instance, running the SmolLM3 3B model at Q4_K_M requires 2.2 GB of video memory and 4.2 GB of system RAM. This offload process allows you to run larger models like the Stable Diffusion XL model at FP8 which needs 4.1 GB of video memory and 6.1 GB of system RAM.

CPU offloading comes with a performance cost. Transferring data between the AMD R9 M470 and your system RAM is much slower than keeping the data inside the GDDR5 memory. While offloading allows you to run larger models like the SDXL Turbo 3.5B model, the generation speed will decrease significantly compared to models that fit entirely within the 2 GB limit.

You must also consider the memory cost of context length. Running a model with a 4k context window requires additional memory beyond the base model weights. When using models close to the memory limit, such as the Canary 1B or Qwen-2.5B model at Q4_K_M using 1.8 GB, a large context history might exceed the remaining space and trigger slow system memory usage.