Best local AI models for AMD RX 7600M

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 is a mobile graphics processor equipped with 8 GB of GDDR6 dedicated video memory. When running artificial intelligence models locally, this hardware limit dictates the maximum size of the model you can load. To run at optimal speed, the model weights and active memory must fit entirely within this 8 GB boundary. If a model exceeds this limit, the execution will slow down or fail.

To make larger models fit into the 8 GB video memory, developers use quantization. The quantization column shows the specific compressed format recommended for each model. For example, the Q4_K_M quant uses approximately four bits per weight, while the Q5_K_M and Q6_K quants use five and six bits. Higher quants preserve more model accuracy but require more memory. A model like Mochi 1 at 10B parameters fits within 7.3 GB of video memory when using the Q4_K_M quant.

For models that slightly exceed the local video memory, you can use CPU offloading. This technique splits the workload between your graphics card and your system memory. If you have 32 GB of system RAM, you can run larger models like Gemma 3 12B or Mistral NeMo 12B. These models require 8.8 GB of memory at the Q4_K_M quant, which uses 10.8 GB of system RAM to handle the overflow. CPU offload allows you to run models up to Vicuna 13B, but it comes with a performance cost because system RAM is much slower than GDDR6 video memory.

When selecting your model, you must also consider the context window. The memory figures listed are calculated at a standard 4k context window. If you increase the context length to process longer documents or chat histories, the memory usage will rise. This extra memory consumption might push a model that normally fits, such as Gemma 2 9B at 8 GB used, over the hardware limit.

For pure on-card execution without offloading, you have many options. The Llama 3.1 8B model fits comfortably at the Q5_K_M quant using 7.4 GB of video memory. You can also run the Granite 3.3 8B or Ministral 8B models at the higher quality Q6_K quant, which uses 7.9 GB of video memory. If you need maximum speed and headroom, 7B models like Mistral 7B or Qwen2.5 7B use between 6.9 GB and 7.4 GB of video memory at the Q6_K quant.