Best local AI models for AMD Pro WX 5100

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 5100 is a workstation graphics card equipped with 8 GB of GDDR5 memory. This dedicated video memory determines the size of the artificial intelligence models you can run locally. To fit a model entirely on this hardware, the total memory footprint of the model and its active context must remain under the 8 GB limit.

Quantization is a method that compresses model files to save space. The quant column shows the optimal compression level for each model on this hardware. For example, Gemma 2 9B fits at the Q4_K_M quant and uses exactly 8 GB of memory. Smaller models can run at higher quality levels. Llama 3.1 8B runs at the Q5_K_M quant using 7.4 GB of memory, while Mistral 7B runs at the Q6_K quant using 7.4 GB of memory.

Other models can also run at high quality levels within the hardware limits. Granite 3.3 8B, Ministral 8B, InternLM 3 8B, and OpenCoder 8B all run at the Q6_K quant using 7.9 GB of memory. Vision and media models like MiniCPM-V 2.6, Idefics 3 8B, and Stable Diffusion 3.5 Large also run at the Q6_K quant using 7.9 GB of memory. For 7B models like Qwen2.5 7B, OLMo 2 7B, and Falcon 3 7B, the Q6_K quant uses 6.9 GB of memory.

When a model exceeds the 8 GB video memory limit, you must use CPU offloading. This process splits the model between your graphics card and your system RAM. CPU offloading allows you to run larger models, but it reduces processing speed because system RAM is slower than graphics memory. These calculations assume your computer has 32 GB of system RAM available.

For example, Gemma 3 12B and Mistral NeMo 12B require 8.8 GB of memory at the Q4_K_M quant, which uses 10.8 GB of system RAM. FLUX.1 dev requires 14.4 GB of memory at the FP8 or optimized level, which uses 16.4 GB of system RAM. Vicuna 13B requires 9.5 GB of memory at the Q4_K_M quant, which uses 11.5 GB of system RAM.

Memory usage calculations are based on a standard 4k context window. The context window is the active memory used for your current conversation history. If you increase the context window beyond 4k tokens, the model will require more memory. This extra memory usage may force you to use a lower quant level or rely on CPU offloading to prevent out of memory errors.