Best local AI models for AMD Pro WX 3200

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 3200 is an entry level workstation graphics card equipped with 4 GB of GDDR5 video memory. This onboard memory capacity determines which artificial intelligence models you can run entirely on the hardware. To execute a model without performance bottlenecks, the active weights must fit within this 4 GB limit while leaving a small amount of headroom for the operating system.

The quantization column indicates the compression level applied to each model. Quantization reduces the precision of model weights to save space. For this hardware, models like Lumina-Next or CogVideoX 2B / 5B can run at a Q4_K_M quantization using 3.7 GB of memory. Models with smaller parameter counts like Qwen3 4B, Gemma 3 4B, and Phi-4-mini-instruct can run at a higher quality Q6_K quantization. The smallest models like SmolLM3 3B and Kandinsky 3.1 can run at Q8_0 quantization using 3.8 GB of memory.

When a model exceeds the 4 GB video memory limit, you must use CPU offload. This technique splits the model layers between your graphics card and your system memory. For example, running Mistral 7B requires 5.7 GB of memory at Q4_K_M quantization, which uses 7.7 GB of system RAM when offloaded. Similarly, running Stable Diffusion XL at FP8 requires 4.1 GB of memory, which uses 6.1 GB of system RAM.

CPU offloading allows you to run larger architectures like Falcon 3 7B or Command R7B on your system. However, transferring data between the system RAM and the graphics card over the system bus introduces latency. This transfer process significantly slows down the generation speed compared to running models entirely inside the GDDR5 memory.

You must also consider the memory cost of context length. Running a model with a standard 4k context window requires additional video memory to store the active conversation history. If you use the maximum quantization size shown for a model, you may need to reduce the context window to prevent out of memory errors during long generations.