Best local AI models for AMD Pro WX 3100

The largest models that fit fully

The 30 largest of the 81 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 3100 is an entry level professional graphics card equipped with 4 GB of GDDR5 video memory. This dedicated memory size determines which artificial intelligence models can run entirely on the hardware. When a model fits completely within this 4 GB frame buffer, it achieves the fastest possible processing speeds. If a model exceeds this limit, the system must split the workload between the graphics card and system memory.

To fit larger models into the 4 GB limit, developers use quantization. The quantization column shows the compression level applied to the model weights. For example, the 5B parameter Lumina-Next or Lumina-Image 2.0 and CogVideoX 2B or 5B can run at a Q4_K_M quantization which uses 3.7 GB of video memory. Similarly, DeepSeek-VL2 at 4.5B parameters fits using a Q5_K_M quantization which consumes 3.8 GB. Models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 utilize a Q6_K quantization to fit within 3.9 GB.

Smaller models can run with less compression for better output quality. The 3B parameter SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 can all run at a high quality Q8_0 quantization using 3.8 GB of video memory. Other models like Stable Diffusion 3.5 Medium at 2.5B parameters run at Q8_0 quantization using 3.2 GB of video memory. This allows the card to maintain high precision on smaller architectures.

When a model is too large for the 4 GB video memory, you must use CPU offloading. This process shares the workload with your system RAM, assuming you have a standard 32 GB system RAM setup. Offloading allows you to run larger models but it costs processing speed because system RAM is much slower than GDDR5 video memory. For example, running Mistral 7B at Q4_K_M requires 5.7 GB of memory, which uses all video memory and needs 7.7 GB of system RAM.

Other popular models require similar offloading setups. The Qwen2.5 0.5B or 1.5B or 3B or 7B, OLMo 2 1B or 7B, Falcon 3 1B or 3B or 7B, Command R7B, and OpenHermes 2.5 all need 5.1 GB at Q4_K_M quantization, which requires 7.1 GB of system RAM. Stable Diffusion XL needs 4.1 GB at FP8 or optimized settings, which requires 6.1 GB of system RAM. These configurations make execution possible but slower.

You must also consider the 4k context caveat when running text models. The memory usage figures listed for these models are calculated at a standard base context. If you increase the context window to 4000 tokens or higher, the active memory requirements will rise. This extra memory demand can push a model that fits at base context over the 4 GB limit, which triggers CPU offloading and slows down generation.