Best local AI models for AMD RX 480

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 480 graphics card has 8 GB GDDR5 memory. This onboard memory determines which artificial intelligence models you can run locally. To run a model entirely on your graphics hardware, the model size and its working memory must fit inside this 8 GB limit. Keeping the model on the card ensures the fastest processing speeds.

The quantization column shows the compression level used for each model. Quantization reduces the size of a model so it uses less memory. For example, a Q4_K_M quantization uses a four bit format to compress the weights. A Q6_K quantization uses a six bit format. Higher quantization numbers like Q6_K preserve more original model quality but require more memory space.

Several large models fit completely within the 8 GB memory of the AMD RX 480. Mochi 1 10B fits at the Q4_K_M quantization using 7.3 GB of memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB of memory. Models like Llama 3.1 8B fit at Q5_K_M using 7.4 GB of memory. You can also run Mistral 7B at Q6_K using 7.4 GB of memory.

When a model exceeds 8 GB, you must use CPU offload. This process splits the model between your graphics card and your system RAM. We assume you have 32 GB of system RAM for these cases. Offloading allows you to run larger models but it slows down processing speed because system RAM is slower than GDDR5 graphics memory.

Many excellent models require CPU offload on this hardware. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B all need 8.8 GB at Q4_K_M and require 10.8 GB of system RAM. FLUX.1 dev 12B needs 14.4 GB at FP8 or optimized settings and requires 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and requires 11.5 GB of system RAM.

You must consider the context limit when running these models. The memory numbers listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory usage will increase. This extra memory demand might force a fitting model to require CPU offload.