Best local AI models for AMD RX 7900M

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 Radeon RX 7900M is a high performance laptop graphics processor equipped with 16 GB of GDDR6 memory. This dedicated memory determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on your graphics hardware, the model files and the active workspace must fit within this 16 GB limit. Running models locally ensures complete data privacy and eliminates subscription fees.

The quantization column indicates the compression level applied to each model. Raw models are often too large for consumer hardware, so they are compressed into smaller formats like Q4_K_M, Q5_K_M, Q6_K, or Q8_0. A higher quantization number like Q8_0 preserves more original model accuracy but requires more memory. For example, the 12B models like Gemma 3 12B, Gemma 4 12B, and Mistral NeMo 12B run at Q8_0 quantization using 15.3 GB of memory.

If you want to run larger models, you can use CPU offloading by sharing the workload with your system RAM. Assuming your laptop has 32 GB of system RAM, you can run models that exceed the 16 GB graphics limit. For instance, Solar Pro 22B and Codestral 22B require 16.1 GB of memory at Q4_K_M quantization, which uses 18.1 GB of system RAM. Larger models like Gemma 3 27B require 19.8 GB of memory at Q4_K_M quantization and use 21.8 GB of system RAM.

CPU offloading comes with a performance cost. While your graphics processor handles its portion of the model at high speed, transferring data back and forth to your system RAM slows down the generation speed. Models that fit entirely within the 16 GB GDDR6 memory of your RX 7900M will generate text and images much faster than models that require offloading.

You must also consider the memory required for context. The memory figures listed for these models are calculated using a standard 4k context window. If you increase the context window to process longer documents or extended conversations, the model will require more memory. To avoid running out of memory, you may need to select a smaller model size or a lower quantization level.