Best local AI models for AMD RX 6600

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 6600 graphics card features 8 GB of GDDR6 video memory. This onboard memory determines the size of the artificial intelligence models you can run locally. When a model fits entirely within this 8 GB space, it executes quickly because the graphics processor has direct access to the data. If a model exceeds this limit, the system must split the workload between your graphics card and system memory.

To fit larger models into the 8 GB limit, developers use quantization. The quantization column shows the optimal compression level for each model. For example, Mochi 1 is a 10B model that fits into 7.3 GB of video memory using the Q4_K_M quantization. Gemma 2 9B fits into exactly 8 GB of video memory using the same Q4_K_M quantization. Models like Llama 3.1 8B use the Q5_K_M quantization to fit within 7.4 GB of video memory.

Other models can run at higher precision levels. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, and OpenCoder 8B all fit into 7.9 GB of video memory using the Q6_K quantization. Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large also use the Q6_K quantization to fit within 7.9 GB of video memory. EXAONE 3.5 7.8B fits into 7.7 GB of video memory using the Q6_K quantization.

Smaller 7B models run comfortably on this hardware. Mistral 7B uses 7.4 GB of video memory at Q6_K quantization. Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, OpenHermes 2.5, Zephyr 7B Beta, OpenChat 3.5, Starling LM 7B, and Codestral Mamba 7B all use 6.9 GB of video memory at the Q6_K quantization level. These memory calculations assume a standard 4k context window. Increasing the context window size will require more video memory and may exceed the 8 GB limit.

When a model is too large for the 8 GB video memory, you must use CPU offloading. This process shares the workload with your system RAM but reduces processing speed. For example, FLUX.1 dev requires 14.4 GB at FP8 and needs 16.4 GB of system RAM. Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev all require 8.8 GB at Q4_K_M and need 10.8 GB of system RAM.

Other offloading options include Open-Sora 2.0, which requires 8.1 GB at Q4_K_M and needs 10.1 GB of system RAM. Vicuna 13B requires 9.5 GB at Q4_K_M and needs 11.5 GB of system RAM. These offload configurations assume your computer has at least 32 GB of system RAM installed to handle the shared memory load.