Best local AI models for AMD FirePro M5100

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 M5100 is an older mobile workstation graphics card equipped with 2 GB of GDDR5 dedicated video memory. This memory capacity dictates the size of the artificial intelligence models you can run locally. To load a model entirely onto this hardware, the total memory footprint of the model must remain under the 2 GB limit of your graphics card.

Model quantization is a method that compresses the weights of neural networks to save space. The quant column indicates the optimal compression level for each model on this hardware. For example, the 2.8B Allegro model fits within 2 GB of video memory when compressed to the Q4_K_M quantization level. Smaller models like the 1.7B SmolLM2 can run at a higher quality Q6_K quantization level while using 1.7 GB of video memory.

When a model exceeds the 2 GB video memory limit, you must use CPU offload. This technique splits the model weights between your graphics card and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like the 3.5B SDXL Turbo. This model requires 2.6 GB of video memory at Q4_K_M quantization and offloads the remaining data to use 4.6 GB of system RAM.

Using CPU offload comes with a performance cost. Transferring data between your system RAM and the graphics card over the system bus is much slower than reading directly from GDDR5 video memory. While offloading allows you to run larger models like the 3.417B Stable Diffusion XL or the 3.3B MusicGen, your generation speeds will decrease significantly compared to models that fit entirely on your graphics card.

You must also consider the memory cost of context length when running text models. The memory figures listed are for the base model weights only. Running a model with a standard 4k context window requires additional video memory to store the active conversation history. If you run a model that already uses the maximum 2 GB of video memory, you will experience slowdowns as the context window grows and forces data into system RAM.