Best local AI models for AMD RX 7600 XT

The largest models that fit fully

The 30 largest of the 155 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 7600 XT graphics card features 16 GB of GDDR6 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on your hardware at full speed, the model files and the active context data must fit completely within this 16 GB limit.

The quantization level indicates how much the model weights are compressed. A higher quantization level like Q8_0 or Q6_K preserves more original model quality but requires more memory. A lower quantization level like Q4_K_M reduces the memory footprint to let larger models fit on your card but it slightly decreases output precision.

For maximum performance on your card, you can run models up to 21B parameters. The gpt-oss-20b and Reka Flash 3 models fit at Q4_K_M quantization using 15.4 GB of memory. Vision models like Qwen-Image and Qwen-Image-Edit fit at Q4_K_M using 14.6 GB of memory. CogVLM2 fits at Q4_K_M using 13.9 GB of memory.

You can run 14B models with higher precision. The Phi-4 model, Phi-4-reasoning / -plus, Wan 2.2 T2I, Wan 2.1 (1.3B / 14B), and SkyReels V2 fit at Q6_K quantization using 13.8 GB of memory. For 12B models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B, you can use the high quality Q8_0 quantization which uses 15.3 GB of memory.

If a model exceeds your 16 GB video memory, you must offload some layers to your system RAM. This offloading allows you to run larger models but it significantly reduces generation speed. For example, running Gemma 3 27B at Q4_K_M requires 19.8 GB of memory and needs 21.8 GB of system RAM. Mistral Small 3.2 at Q4_K_M requires 17.6 GB of memory and needs 19.6 GB of system RAM.

All memory calculations assume a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory requirement will grow. You may need to select a smaller model or a lower quantization level to prevent out of memory errors during long conversations.