Best local AI models for AMD R9 M265X

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 M265X is a mobile graphics card equipped with 2 GB of GDDR5 dedicated video memory. This memory size is the absolute limit for loading artificial intelligence models directly onto the hardware. To run local models on this GPU you must select small architectures and use optimized quantization formats to prevent out of memory errors.

The quantization column indicates the compression level applied to each model. For this hardware the best quants range from Q4_K_M up to Q8_0. Lower quantization levels like Q4_K_M compress the model parameters to four bits which allows larger models like Allegro 2.8B or Open-Sora Plan 2.7B to fit into the 2 GB limit. Higher quantization levels like Q8_0 preserve more model accuracy but require more memory per parameter which limits you to smaller models like TinyLlama 1.1B.

When a model exceeds the 2 GB video memory limit you must use CPU offload. This technique splits the model layers between your GPU and your system RAM. For example running SmolLM3 3B or Kandinsky 3.1 requires 2.2 GB of video memory and 4.2 GB of system RAM. Running Stable Diffusion XL requires 4.1 GB of video memory and 6.1 GB of system RAM which forces heavy offloading.

CPU offload comes with a significant performance cost. Transferring data between the AMD R9 M265X video memory and the system RAM over the system bus is much slower than processing data directly on the GPU. While offloading allows you to run larger models like SDXL Turbo or MusicGen it will greatly reduce the generation speed.

You must also consider the 4k context window caveat when running text models. The memory figures listed for models like LFM2 2.6B or Qwen3 1.7B only account for the base model weights. As your conversation history grows toward a 4k context window the active memory usage increases. You may need to use a lower quantization level or offload more layers to prevent crashes during long sessions.