Best local AI models for AMD RX 7600

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 7600 graphics card features 8 GB of GDDR6 memory. This onboard memory determines the maximum size of the artificial intelligence models you can run locally. For optimal performance, the model files and their activation states must fit entirely within this hardware limit. If a model exceeds this capacity, your system must use slower memory pools, which decreases generation speeds.

To fit larger models into the 8 GB limit, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, Gemma 2 9B fits at the Q4_K_M quantization level, which uses exactly 8 GB of memory. Smaller models like Llama 3.1 8B can run at a higher quality Q5_K_M quantization using 7.4 GB of memory. Models like Mistral 7B or Qwen2.5 7B can run at the even higher quality Q6_K quantization, using 7.4 GB and 6.9 GB respectively.

When selecting a model, you must account for the context window. The memory figures listed represent the model at its base state. Running a model with a long history or a 4k context window requires additional memory for the key value cache. If you run a model that uses almost all 8 GB of your hardware memory, like Gemma 2 9B at Q4_K_M, you will have very little room left for processing long conversations.

You can run models that exceed 8 GB by using CPU offload. This technique splits the model weights between your graphics card and your system RAM. For this setup, we assume your computer has 32 GB of system RAM. Offloading allows you to run larger architectures, but sharing the workload across the system bus reduces your processing speed.

Using CPU offload, you can run models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, or Pixtral 12B. These models require 8.8 GB of memory at the Q4_K_M quantization and need 10.8 GB of system RAM. You can also run FLUX.1 dev at FP8, which requires 14.4 GB of memory and 16.4 GB of system RAM. Vicuna 13B is also accessible with offloading, requiring 9.5 GB at Q4_K_M and 11.5 GB of system RAM.