Best local AI models for AMD RX 6600 XT

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 6600 XT graphics card features 8 GB of GDDR6 video memory. This memory size determines which artificial intelligence models can run directly on your hardware. To run a model entirely on the graphics card, the model files and its working memory must fit within this 8 GB limit. If a model exceeds this capacity, your system must use alternative execution methods.

The quantization column indicates the compression level applied to each model. Quantization reduces the size of model weights to save video memory. For example, a Q4_K_M quantization uses a four bit format to compress the model. A Q6_K quantization uses a six bit format which preserves more accuracy but requires more memory. Choosing the correct quantization allows you to run larger models on your hardware.

Several high quality models fit completely within the 8 GB limit of the RX 6600 XT. Mochi 1 is a 10B model that fits at the Q4_K_M quantization using 7.3 GB of video memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB of video memory. You can also run Llama 3.1 8B at Q5_K_M using 7.4 GB of video memory. Popular 7B models like Mistral 7B and Qwen2.5 7B run comfortably at Q6_K using 7.4 GB and 6.9 GB of video memory respectively.

When a model is too large for the 8 GB video memory, you can use CPU offloading. This process splits the model between your graphics card and your system RAM. Offloading allows you to run larger models but it reduces generation speed because system RAM is much slower than GDDR6 video memory. This approach assumes your computer has at least 32 GB of system RAM.

Using CPU offloading you can run models like Gemma 3 12B or Mistral NeMo 12B which require 8.8 GB at Q4_K_M and 10.8 GB of system RAM. Image generation models like FLUX.1 dev require 14.4 GB at FP8 or optimized settings along with 16.4 GB of system RAM. Larger models like Vicuna 13B require 9.5 GB at Q4_K_M and 11.5 GB of system RAM.

You must also consider the context window size when loading these models. The memory numbers listed here assume a standard 4k context window. If you increase the context window to process longer documents or chat histories, the model will require significantly more video memory. This extra memory usage can cause an out of memory error or force the system to slow down.