Best local AI models for AMD VII

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 VII graphics card features 16 GB of high speed HBM2 memory. This onboard memory determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on the graphics card, the model files and the active working memory must fit within this 16 GB limit. Keeping the entire model inside the HBM2 memory ensures the fastest possible generation 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, the 21B gpt-oss-20b and Reka Flash 3 models fit in 15.4 GB of memory when compressed to the Q4_K_M quantization. Other models like Qwen-Image and Qwen-Image-Edit fit in 14.6 GB using the same Q4_K_M quantization. Larger quantizations like Q6_K or Q8_0 preserve more original model quality but require more memory.

Models with 14B to 17B parameters can use higher quality quantizations on this hardware. The HunyuanImage 2.1 / 3.0 model uses 14.5 GB of memory at the Q5_K_M quantization. The DeepSeek-Coder-V2 16B / 236B and Kimi-VL A3B models both fit in 15.7 GB of memory using the Q6_K quantization. Similarly, the Phi-4 and Phi-4-reasoning / -plus models fit in 13.8 GB of memory at the Q6_K quantization level.

For 12B models, you can run the highest quality Q8_0 quantization. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B all use 15.3 GB of memory at Q8_0. The FLUX.1 dev model can run in 14.4 GB of memory using the FP8 / optimized quantization. Running these models at Q8_0 or FP8 ensures excellent output quality while staying within the hardware limits.

If a model is too large for the 16 GB graphics memory, you can offload parts of it to your system RAM. This offloading process requires a system with at least 32 GB of system RAM. For example, Solar Pro and Codestral 22B need 16.1 GB of memory at Q4_K_M, which requires 18.1 GB of system RAM. The Gemma 3 27B model needs 19.8 GB of memory at Q4_K_M, which requires 21.8 GB of system RAM. Offloading allows you to run larger models but slows down the generation speed significantly.

All memory calculations are based on a standard 4k context window. The context window is the amount of text the model can remember during a conversation. If you increase the context window beyond 4k tokens, the model will require more memory. This extra memory usage might force you to use a lower quantization level or offload parts of the model to your system RAM.