Best local AI models for AMD Pro WX 7100

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 Pro WX 7100 is equipped with 8 GB of GDDR5 graphics memory. This dedicated VRAM determines the maximum size of the artificial intelligence models you can run entirely on the GPU. When a model fits completely within this 8 GB limit, it benefits from the fastest processing speeds the hardware can offer. If a model exceeds this capacity, it cannot be loaded entirely into the graphics memory.

To fit larger models into the 8 GB limit, files are compressed using quantization. The quant column indicates the specific level of compression applied to the model weights. For example, a Q4_K_M quant represents a four bit quantization level, while a Q6_K quant represents a six bit level. Higher quant numbers preserve more original model accuracy but require more memory. Lower quants reduce the memory footprint at the cost of some precision.

Several capable models fit directly within the VRAM of your card. The Mochi 1 10B model fits at a Q4_K_M quant using 7.3 GB of memory. Gemma 2 9B fits at Q4_K_M using exactly 8 GB of VRAM. You can also run Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B at a Q5_K_M quant using 7.7 GB of memory. Chroma 8.9B fits at Q5_K_M using 7.6 GB, while Llama 3.1 8B fits at Q5_K_M using 7.4 GB.

For higher precision, you can run Granite 3.3 8B, Ministral 8B, InternLM 3 8B, OpenCoder 8B, Seed-Coder 8B, MiniCPM-V 2.6, Idefics 3 8B, Fuyu-8B, Emu3, and Stable Diffusion 3.5 Large at a Q6_K quant using 7.9 GB of VRAM. EXAONE 3.5 7.8B fits at Q6_K using 7.7 GB. Mistral 7B fits at Q6_K using 7.4 GB. Models like 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 fit at Q6_K using 6.9 GB.

When a model is too large for the 8 GB VRAM, you can offload parts of it to your system RAM. This process requires a system with at least 32 GB of system RAM. Offloading allows you to run Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, Pixtral 12B, FLUX.1 schnell, FLUX.1 Kontext dev, and FLUX.1 Krea dev at Q4_K_M, which requires 8.8 GB of memory and 10.8 GB of system RAM. Open-Sora 2.0 requires 8.1 GB at Q4_K_M and 10.1 GB of system RAM. FLUX.1 dev requires 14.4 GB at FP8 and 16.4 GB of system RAM. Vicuna 13B requires 9.5 GB at Q4_K_M and 11.5 GB of system RAM. Offloading makes these models run much slower because system RAM is slower than GDDR5 VRAM.

All memory calculations are based on a standard context window of 4k tokens. If you increase the context window to process longer documents or conversations, the memory usage will rise. This extra memory demand can push a model over the 8 GB VRAM limit, which will force the system to offload data to your system RAM and reduce performance.