Best local AI models for AMD RX 9060

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 9060 graphics card comes equipped with 8 GB of GDDR6 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. For the best performance, a model must fit entirely within this 8 GB space. When a model fits inside the graphics memory, the processor can access the data quickly to generate fast responses.

The quantization column shows the compression level used to shrink these models. Raw models are often too large for consumer hardware. Quantization techniques like Q4_K_M, Q5_K_M, and Q6_K reduce the precision of the model weights. This process lowers the memory footprint while keeping most of the original intelligence. For example, Gemma 2 9B fits in 8 GB of memory using the Q4_K_M quantization, while Llama 3.1 8B uses 7.4 GB at Q5_K_M.

Many capable models fit directly inside the local memory of this card. You can run Granite 3.3 8B, Ministral 8B, or InternLM 3 8B at Q6_K quantization using 7.9 GB of memory. Other options like Mistral 7B and Qwen2.5 7B run comfortably at Q6_K quantization, consuming 7.4 GB and 6.9 GB respectively. Even specialized tools like the Mochi 1 10B model can run at Q4_K_M quantization by using 7.3 GB of memory.

If you want to run larger models, you must use CPU offloading. This technique splits the model between your 8 GB graphics card and your system memory. For these calculations, we assume a standard system with 32 GB of system RAM. Offloading allows you to load larger files, but it comes with a speed cost. Moving data between the system RAM and the graphics card is much slower than keeping everything on the card.

With CPU offloading, you can run models like Gemma 3 12B or Mistral NeMo 12B. These models require 8.8 GB of memory at Q4_K_M quantization and need 10.8 GB of system RAM to function. You can also run FLUX.1 dev at FP8 or optimized settings, which requires 14.4 GB of memory and 16.4 GB of system RAM. Vicuna 13B is also accessible, needing 9.5 GB at Q4_K_M quantization and 11.5 GB of system RAM.

You must also consider the context window when planning your memory usage. The memory figures listed here assume a standard 4k context window. If you process very long documents or have long conversations, the memory usage will increase. Running close to the 8 GB limit of your AMD RX 9060 means that large context demands might force the system to slow down or fail.