Best local AI models for NVIDIA Quadro K2100M

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 K2100M is an older mobile workstation graphics card equipped with 2 GB of GDDR5 memory. This dedicated memory size determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 2 GB limit, it executes with the fastest possible processing speeds because the graphics processor does not have to wait for slower system memory.

To fit larger models into this restricted memory space, developers use quantization. The quant column indicates the specific level of compression applied to the model weights. For example, a Q4_K_M quantization uses approximately four bits per weight to reduce the footprint of the Allegro 2.8B model to 2 GB of used space. Higher quantization levels like Q8_0 provide better output quality but require more memory, which limits you to smaller models like the TinyLlama 1.1B at 1.4 GB used.

If you want to run models that exceed the 2 GB limit, you must use CPU offloading. This technique splits the model layers between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these scenarios. For instance, running the SmolLM3 3B model requires 2.2 GB of graphics memory at Q4_K_M quantization and an additional 4.2 GB of system RAM. Offloading allows you to run larger tools like the MusicGen medium or SDXL Turbo, but the transfer of data between the system RAM and the graphics card reduces processing speed.

When running large language models, you must also consider the 4k context window caveat. The memory figures listed for these models only account for the base weights. As you type longer prompts and the model generates longer responses, the active memory usage increases. Running a model near the 2 GB limit of your hardware might cause out of memory errors if your conversation history grows too large.

For pure local execution without offloading, you can choose from several optimized models. The Open-Sora Plan 2.7B and LFM2 2.6B models fit well at Q4_K_M quantization. If you prefer higher precision, you can run the SmolVLM 2B or Stable Diffusion 3 Medium at Q6_K quantization. Smaller models like the Sana 1.6B and Whisper Large v3 can run at Q8_0 quantization while staying within the hardware limits.