Best local AI models for AMD RX 7600M 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 7600M XT is a mobile graphics processor equipped with 8 GB of GDDR6 dedicated video memory. When running artificial intelligence models locally, this hardware boundary is the most critical factor. The entire active model must fit inside this video memory to run at maximum speed. If a model exceeds this limit, the system must transfer data back and forth to your system memory, which slows down generation speeds significantly.

To fit larger models into the 8 GB limit, developers use quantization. This process compresses the mathematical weights of the neural network. The quantization column shows the optimal format for each model. For example, a Q4_K_M quantization uses four bit precision, while a Q6_K quantization uses six bit precision. Higher quantization numbers preserve more of the original model intelligence but require more video memory.

With 8 GB of video memory, you can run many capable models entirely on your graphics card. Gemma 2 9B fits at the Q4_K_M quantization level by using exactly 8 GB of memory. You can also run Llama 3.1 8B at the Q5_K_M quantization level using 7.4 GB of memory. Models like Mistral 7B and Qwen2.5 7B run comfortably at the Q6_K quantization level, using 7.4 GB and 6.9 GB of video memory respectively.

If you want to run larger models, you must use CPU offloading. This technique shares the workload between your graphics card and your system RAM. For example, running Mistral NeMo 12B at Q4_K_M requires 8.8 GB of video memory and 10.8 GB of system RAM. Running the FLUX.1 dev model at FP8 requires 14.4 GB of video memory and 16.4 GB of system RAM. Offloading allows you to run these larger models, but it costs significant processing speed.

When planning your local deployments, remember that context length affects memory usage. The memory figures listed here assume a standard 4k context window. If you increase the context length to process longer documents or chat histories, the system will require more memory. You may need to select a lower quantization level or a smaller model to prevent your graphics card from running out of memory during long conversations.