Best local AI models for AMD FirePro M4100

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 M4100 is an entry level mobile 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 load a model entirely on this GPU, the model files and active memory must fit within this 2 GB boundary. Running models locally ensures complete data privacy and eliminates reliance on external cloud services.

To fit larger models into the limited video memory, developers use quantization. Quantization reduces the precision of model weights to shrink the overall file size. The quant column shows the best compromise between model intelligence and memory usage. For example, a Q4_K_M quant uses four bit quantization to compress models like Allegro 2.8B or Open-Sora Plan 2.7B down to exactly 2 GB of memory. Higher quants like Q6_K or Q8_0 offer better output quality but require smaller base models to fit the same space.

Models like SmolVLM 2B or Stable Diffusion 3 Medium can run at Q6_K quantization while using exactly 2 GB of video memory. If you choose a model with a Q8_0 quant, the base model size must be even smaller. Whisper Large v3 at 1.55B parameters uses 2 GB of video memory at Q8_0 quantization. TinyLlama 1.1B at Q8_0 quantization uses only 1.4 GB of video memory, leaving some safety margin for your system display.

When a model exceeds the 2 GB video memory limit, you must use CPU offload. This technique splits the model layers between your video memory and your system RAM. We assume your system has 32 GB of system RAM for these calculations. CPU offload allows you to run larger models like SmolLM3 3B or Replit Code v1.5 3B. These models need 2.2 GB of memory at Q4_K_M quantization, which requires offloading 4.2 GB of data to your system RAM.

Using CPU offload comes with a significant performance cost. System RAM is much slower than the GDDR5 memory on your graphics card. While offloading allows you to run larger tools like Stable Diffusion XL or SDXL Turbo, the generation speed will be much slower. You will experience longer wait times for text generation or image creation when the system must constantly transfer data between the CPU and GPU.

You must also consider the active context window when running local text models. The memory figures listed here are calculated using a baseline 4k context window. If you increase the context window to process longer documents or chat histories, the memory usage will rise quickly. Keeping your context window at or below 4k is necessary to prevent out of memory errors on this 2 GB hardware.