Best local AI models for AMD RX 6650M

The largest models that fit fully

The 30 largest of the 123 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 RX 6650M is a mobile graphics processor equipped with 8 GB of GDDR6 video memory. This dedicated memory determines the maximum size of the artificial intelligence models you can run entirely on the graphics card. To run a model smoothly at native speeds, the model files and the active processing data must fit completely within this 8 GB limit.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, Gemma 2 9B fits in 8 GB of video memory using the Q4_K_M quantization. Models like Llama 3.1 8B fit comfortably at Q5_K_M quantization using 7.4 GB of memory. Smaller models like Mistral 7B can run at a higher quality Q6_K quantization while consuming only 7.4 GB of video memory.

When a model exceeds the 8 GB video memory limit, you must offload some processing to your system RAM. This offloading process allows you to run larger models but reduces processing speed. For a system with 32 GB of system RAM, you can run FLUX.1 dev at FP8 which needs 14.4 GB of memory and uses 16.4 GB of system RAM. Similarly, Gemma 3 12B at Q4_K_M needs 8.8 GB of memory and uses 10.8 GB of system RAM.

Other offload options include Mistral NeMo 12B and Pixtral 12B which both require 8.8 GB at Q4_K_M and use 10.8 GB of system RAM. You can also run Vicuna 13B at Q4_K_M which needs 9.5 GB of memory and uses 11.5 GB of system RAM. While offloading makes these larger models accessible, the data transfer between the graphics card and system memory slows down the generation speed.

Memory consumption calculations assume a standard 4k context window. As you send longer prompts or generate longer responses, the context window expands and consumes additional video memory. If you run a model close to the 8 GB limit, such as Granite 3.3 8B at Q6_K using 7.9 GB, a long conversation may exceed the remaining space and trigger slow system RAM offloading.