Best local AI models for NVIDIA Quadro K2000D

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 K2000D is an entry level workstation graphics card equipped with 2 GB of GDDR5 memory. This onboard memory capacity dictates the size of the artificial intelligence models you can run locally. To fit within this 2 GB limit, models must either be highly compact or compressed using quantization techniques. Running models entirely within your video memory ensures the fastest possible processing speeds.

The quantization column indicates the specific compression level used to shrink each model. For example, the 2.8B Allegro model fits into 2 GB of video memory when using the Q4_K_M quantization. Smaller models like the 2B Stable Diffusion 3 Medium can run at a higher quality Q6_K quantization while still using 2 GB. Ultra compact models such as the 1.1B TinyLlama use the Q8_0 quantization and require only 1.4 GB of video memory.

When a model exceeds the 2 GB physical limit of your graphics card, you must offload a portion of the workload to your system RAM. For instance, running the 3B SmolLM3 requires 2.2 GB of video memory at Q4_K_M quantization and an additional 4.2 GB of system RAM. This offloading process allows you to run larger models like the 3.5B SDXL Turbo, but it significantly reduces processing speed because system RAM is much slower than GDDR5 video memory.

Hardware limitations also affect text generation length. The memory figures listed assume a standard 4k context window. If you increase the context length to process longer documents or extended conversations, the memory usage will rise. This extra memory demand can push a fitting model over the 2 GB threshold, forcing your system to slow down or fail to run the model.

You can choose from many model types depending on your needs. For image generation, Stable Diffusion 3.5 Medium fits in 1.8 GB of video memory using Q4_K_M. For audio and speech tasks, Whisper Large v3 fits in 2 GB of video memory using Q8_0. These options allow the NVIDIA Quadro K2000D to handle diverse local AI workloads within its strict hardware boundaries.