Best local AI models for AMD R9 M485X

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 M485X graphics card features 8 GB of GDDR5 video memory. This dedicated memory size determines which artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 8 GB limit, it executes with the best possible speed and efficiency.

To fit larger models into the available video memory, you must use quantized versions. The quantization column indicates the compression level applied to the model weights. For example, a Q4_K_M quant uses fewer bits per parameter than a Q6_K quant. This compression reduces the memory footprint so that models like Gemma 2 9B or Llama 3.1 8B can load on your hardware.

Running a model at its limit requires careful attention to memory usage. Mochi 1 at 10B uses 7.3 GB of memory with the Q4_K_M quant. Gemma 2 9B uses exactly 8 GB with the Q4_K_M quant. Other models like GLM-4 9B, Yi-Coder 9B, and Nemotron Nano 9B fit within 7.7 GB using the Q5_K_M quant. These configurations leave very little room for other system tasks.

For models that exceed the 8 GB video memory limit, you must use CPU offload. This process splits the model layers between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like Gemma 3 12B or Mistral NeMo 12B. These models need 8.8 GB of video memory at Q4_K_M and require 10.8 GB of system RAM. Offloading allows you to run these larger architectures, but it costs performance because system RAM is much slower than GDDR5 video memory.

You must also consider the context window when loading models. The memory usage figures listed for these models assume a standard 4k context window. If you increase the context length to process longer documents or chat histories, the memory requirements will rise. This extra memory demand can push a model past the 8 GB limit and force slow CPU offloading.