Best local AI models for AMD FirePro W2100

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 FirePro W2100 is an entry level workstation graphics card equipped with 2 GB of DDR3 video memory. This onboard memory capacity determines which artificial intelligence models can run directly on the hardware. To run a model entirely on the graphics processor, the total size of the model must not exceed the available 2 GB limit. DDR3 memory is slower than modern GDDR memory, so keeping the model within the onboard capacity is critical for maintaining basic execution speeds.

Quantization is a method that compresses model files to fit into smaller memory footprints. The quant column shows the best quantization level that fits within your hardware limits. For example, the 2.8B Allegro model and the 2.7B Open-Sora Plan model both fit into 2 GB of memory when compressed to the Q4_K_M quantization level. Smaller models like the 1.7B Qwen3 and the 1.7B SmolLM2 can run at a higher quality Q6_K quantization level while using 1.7 GB of video memory.

Very small models can run at the highest quality Q8_0 quantization level. The 1.1B TinyLlama and the 1.1B SantaCoder models use 1.4 GB of video memory at Q8_0. Audio and speech models also fit this category. The 1.55B Whisper Large v3 uses 2 GB of video memory at Q8_0, while the 1.5B Whisper Large v2 / turbo uses 1.9 GB of video memory at the same quantization level. Other options include the 1.5B ControlNet / T2I-Adapter / IP-Adapter and the 1.5B AudioGen which both use 1.9 GB of video memory.

When a model is too large for the 2 GB video memory, you must use CPU offload. This process splits the model between the graphics card and your system RAM. We assume a standard system setup with 32 GB of system RAM. For example, running the 3B SmolLM3 or the 3B Kandinsky 3.1 requires 2.2 GB of video memory at Q4_K_M and an additional 4.2 GB of system RAM. The 3.5B SDXL Turbo requires 2.6 GB of video memory at Q4_K_M and 4.6 GB of system RAM.

CPU offload allows you to run larger models like the 3.417B Stable Diffusion XL which needs 4.1 GB of video memory at FP8 / optimized and 6.1 GB of system RAM. However, offloading comes with a performance cost. Moving data between the DDR3 video memory and the system RAM over the system bus slows down processing speeds significantly compared to running models entirely on the graphics card.

Users must also consider the memory cost of context length. Running text models with a standard 4k context window requires additional memory to store active conversation history. This context memory is not included in the base model file sizes listed above. If you run a model near the 2 GB limit of the AMD FirePro W2100, you may need to reduce the context window to prevent out of memory errors.