Best local AI models for NVIDIA Quadro M500M

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 NVIDIA Quadro M500M is an entry level professional graphics card equipped with 2 GB of DDR3 dedicated video memory. This hardware configuration places strict limits on the size of local AI models you can run entirely on the GPU. To run a model without system slowdowns, the model files and active memory must fit within this 2 GB limit.

To make models fit on this hardware, developers use quantization. The quant column shows the specific compression level used to shrink the model. For example, the Allegro 2.8B model fits in 2 GB of video memory when using the Q4_K_M quantization. Smaller models like Moondream 2 at 1.9B can run with the higher quality Q6_K quantization while using 1.9 GB of video memory. TinyLlama 1.1B can run at the high quality Q8_0 quantization while using only 1.4 GB of video memory.

When a model exceeds the 2 GB video memory limit, you must use CPU offload. This process splits the model between your graphics card and your system RAM. We assume a standard system configuration with 32 GB of system RAM for these scenarios. Offloading allows you to run larger models, but it comes with a performance cost. Your processing speed will drop because system RAM is much slower than dedicated video memory.

For example, running the SmolLM3 3B model requires 2.2 GB of video memory at Q4_K_M quantization and needs 4.2 GB of system RAM. Similarly, the Stable Diffusion XL model at 3.417B parameters requires 4.1 GB of video memory at FP8 or optimized settings, which forces 6.1 GB of data into your system RAM. Other models like SDXL Turbo at 3.5B parameters require 2.6 GB of video memory at Q4_K_M quantization and 4.6 GB of system RAM.

You must also consider the memory cost of context length. The memory figures listed for these models assume a standard 4k context window. If you increase the context window to process longer documents or longer conversations, the memory requirements will rise. On a card with 2 GB of video memory, this extra demand can quickly exceed your limit and force the system to use slow CPU offloading.