Best local AI models for AMD RX 6600 LE

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 6600 LE graphics card features 8 GB of GDDR6 video memory. This onboard memory determines the maximum size of the artificial intelligence models you can run entirely on your hardware. When a model fits completely within this limit, it processes tokens at maximum speed. If a model exceeds this capacity, you must utilize system memory to handle the overflow.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to shrink the overall file size. For example, a Q4_K_M quantization represents a medium four bit compression. A Q5_K_M or Q6_K quantization offers higher precision and better output quality but requires more of your video memory. Choosing the correct quantization allows you to balance model accuracy against the available space.

Several high quality models fit directly into the video memory of your card. The Mochi 1 10B model fits at Q4_K_M quantization while using 7.3 GB of memory. Gemma 2 9B utilizes exactly 8 GB of memory at the Q4_K_M quantization level. You can also run models like Llama 3.1 8B at Q5_K_M quantization using 7.4 GB of memory, or Mistral 7B at Q6_K quantization using 7.4 GB of memory.

To run larger models like Gemma 3 12B or Mistral NeMo 12B, you must offload portions of the workload to your system RAM. This offloading process allows you to run models that exceed 8 GB, but it introduces a speed penalty. For instance, running Gemma 3 12B at Q4_K_M requires 8.8 GB of video memory and 10.8 GB of system RAM. Running FLUX.1 dev at FP8 requires 14.4 GB of video memory and 16.4 GB of system RAM.

When planning your local deployments, you must account for the context window. The memory figures listed here assume a standard 4k context window. If you increase the context window to process longer documents or extended conversations, the memory requirements will rise. You may need to select a smaller model or a lower quantization level to prevent out of memory errors during long sessions.