Best local AI models for AMD RX 6600M

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 RX 6600M is a mobile graphics card equipped with 8 GB of GDDR6 memory. This onboard memory size dictates which artificial intelligence models you can run entirely on your hardware. For local execution, the model weights must fit inside this video memory to maintain fast processing speeds. If a model exceeds this limit, your system must rely on system memory, which slows down performance.

To fit larger architectures into the 8 GB limit, we use quantized models. Quantization reduces the precision of model weights to save space. The best quant column shows the optimal compromise between size and accuracy. For example, Gemma 2 9B fits at the Q4_K_M quantization level using exactly 8 GB of video memory. Similarly, Llama 3.1 8B runs efficiently at the Q5_K_M quantization level while using 7.4 GB of memory.

Smaller models can run at higher precision levels because they have fewer parameters. Granite 3.3 8B, Ministral 8B, and InternLM 3 8B can all run at the Q6_K quantization level, which uses 7.9 GB of memory. You can also run popular 7B models like Mistral 7B at Q6_K using 7.4 GB, or Qwen2.5 7B at Q6_K using 6.9 GB. These configurations keep the entire model inside your graphics memory for maximum speed.

When you want to run models that exceed 8 GB, you must use CPU offloading. This process splits the model between your graphics card and your system RAM. For this setup, we assume you have 32 GB of system RAM. Offloading allows you to run Gemma 3 12B or Mistral NeMo 12B at Q4_K_M, which requires 8.8 GB of video memory and 10.8 GB of system RAM. This method makes larger models accessible but reduces generation speed.

You can also run heavy image generation models through offloading. FLUX.1 dev requires 14.4 GB of video memory at FP8 or optimized settings, which needs 16.4 GB of system RAM to function. FLUX.1 schnell requires 8.8 GB of video memory at Q4_K_M and needs 10.8 GB of system RAM. While offloading enables these advanced tools, the data transfer between system RAM and video memory creates a performance bottleneck.

All memory calculations are based on a standard 4k context window. As you type longer prompts or generate longer answers, the context window consumes additional video memory. If your model already uses close to 8 GB of memory, a long conversation might exceed the limit and trigger slow system RAM usage. Keep your context windows short to maintain the best speed on your hardware.