Best local AI models for AMD FirePro W4100

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 W4100 is an entry level workstation graphics card equipped with 2 GB of GDDR5 video memory. This hardware memory limit dictates the size of the artificial intelligence models you can run locally. To run a model entirely on this GPU, the active weights and system overhead must fit within the 2 GB physical boundary. If a model exceeds this capacity, execution will fail or slow down significantly unless you configure your system to offload some processing to your system memory.

Quantization is a compression method that reduces the memory footprint of local models. The quant column shows the best quantization level that fits within the hardware limits of the card. For example, the Allegro 2.8B model fits into 2 GB of video memory using the Q4_K_M quantization. Other models like SmolVLM 2B or Stable Diffusion 3 Medium can run at a higher quality Q6_K quantization while using exactly 2 GB of video memory. Smaller models like TinyLlama 1.1B can run at the high quality Q8_0 quantization while using only 1.4 GB of video memory.

When a model is slightly too large for the 2 GB video memory, you can use CPU offloading if your computer has enough system RAM. Assuming your computer has 32 GB of system RAM, you can split the workload. For example, running the SmolLM3 3B model requires 2.2 GB of video memory at Q4_K_M quantization and needs an additional 4.2 GB of system RAM. Similarly, running Stable Diffusion XL requires 4.1 GB of video memory at FP8 or optimized settings along with 6.1 GB of system RAM.

Offloading models to system RAM comes with a performance cost. System RAM is much slower than the GDDR5 memory on your graphics card. When you run models like MusicGen or SDXL Turbo using CPU offloading, the processing speed drops because data must travel between the system RAM and the GPU. For the best generation speeds, you should select models that fit entirely within the native 2 GB video memory of your card.

You must also consider the context window size when running local text models. The memory figures listed are calculated using a baseline 4k context window. If you increase the context window to process longer documents or conversations, the memory usage will increase. This extra memory usage might push a model that normally fits in your 2 GB video memory into a state where it requires CPU offloading.