Best local AI models for AMD Pro WX 2100

The largest models that fit fully

The 30 largest of the 56 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 2100 is an entry level workstation graphics card equipped with 2 GB of GDDR5 video memory. This hardware memory size is the absolute limit for running local AI models entirely on the graphics processor. When a model fits inside this 2 GB boundary, the GPU handles all computations at its maximum speed. If a model exceeds this limit, it cannot run on the graphics card alone.

To make larger models fit into this 2 GB space, we use quantized versions. The quant column shows the best quantization level for each model. Quantization compresses the model weights to use less memory. For example, the 2.8B Allegro and 2.7B Open-Sora Plan models can run in 2 GB of space when compressed to the Q4_K_M quantization level. Smaller models like the 1.7B SmolLM2 or 1.7B Qwen3 can use the higher quality Q6_K quantization and fit within 1.7 GB of memory.

When a model is too large for the 2 GB video memory, you must use CPU offload. This technique splits the model between your graphics card and your system RAM. For this setup, we assume your computer has 32 GB of system RAM. Running models this way allows you to use larger options like the 3.417B Stable Diffusion XL which needs 4.1 GB at FP8 or optimized settings and requires 6.1 GB of system RAM.

CPU offload comes with a significant performance cost. System RAM is much slower than the GDDR5 memory on your graphics card. While offloading lets you run the 3.5B SDXL Turbo or 3.5B SDXL Lightning with 2.6 GB at Q4_K_M and 4.6 GB of system RAM, the generation speed will be much slower than running a smaller model entirely on the GPU.

You can also run audio and text models using offload. The 3.3B MusicGen and 3B Higgs Audio v2 can run by offloading some work to your system RAM. MusicGen needs 2.4 GB at Q4_K_M and 4.4 GB of system RAM. Higgs Audio v2 needs 2.2 GB at Q4_K_M and 4.2 GB of system RAM. This allows your system to process larger files at the cost of processing speed.

There is an important caveat regarding context window size for text models. The memory numbers listed are for the model weights only. Running a model with a long text history requires extra memory for the context. If you use a large 4k context window, the active memory usage will increase. This extra demand can easily push a model over the 2 GB limit of your AMD Pro WX 2100 and force your system into slow offload mode.