Best local AI models for AMD R9 M395X

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 R9 M395X graphics card features 8 GB of GDDR5 memory. This dedicated hardware memory determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on your graphics processor, the model files and working memory must fit within this 8 GB limit. Keeping the model inside the graphics memory ensures the fastest possible processing speeds.

Quantization is a method that compresses model files to save space. The quant column shows the best compression level for each model. For example, Gemma 2 9B fits exactly at the Q4_K_M quantization which uses 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. Granite 3.3 8B and Mistral 7B can run at the even sharper Q6_K quantization using 7.9 GB and 7.4 GB of memory.

When a model exceeds your 8 GB of graphics memory, you can offload parts of it to your system RAM. This process requires a computer with at least 32 GB of system RAM to work smoothly. Offloading allows you to run larger models like Gemma 3 12B or Mistral NeMo 12B which need 8.8 GB of graphics memory and 10.8 GB of system RAM. You can also run FLUX.1 dev which requires 14.4 GB at FP8 or optimized settings along with 16.4 GB of system RAM.

Offloading comes with a performance cost. Moving data between your graphics card and your system RAM is much slower than keeping everything on the GDDR5 memory. Models like Vicuna 13B require 9.5 GB of graphics memory and 11.5 GB of system RAM which will slow down generation speeds. For the fastest response times, you should select models that fit completely within your 8 GB limit such as Qwen2.5 7B at Q6_K using 6.9 GB of memory.

You must also consider the 4k context caveat when planning your memory usage. The memory figures listed for these models assume a standard context window of 4000 tokens. If you process longer documents or have longer conversations, the model will require more memory. Running a model close to your 8 GB limit like Mochi 1 10B at Q4_K_M using 7.3 GB of memory leaves very little room for expanding this context window.