Best local AI models for NVIDIA Quadro K2000

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 K2000 is an entry level workstation graphics card equipped with 2 GB of GDDR5 memory. This onboard memory capacity dictates the maximum size of the artificial intelligence models you can run entirely on the hardware. To fit local models into this specific memory limit, you must use quantized versions of the model weights.

The quant column indicates the compression level applied to the model. For example, the Allegro 2.8B model fits within 2 GB of used memory when using the Q4_K_M quantization. Other models like SmolVLM 2B or Stable Diffusion 3 Medium can run at a higher quality Q6_K quantization while still staying within the 2 GB limit. Using a Q8_0 quantization on models like TinyLlama 1.1B uses 1.4 GB of memory and provides higher precision.

When a model size exceeds the onboard graphics memory, you must use CPU offload. This method splits the workload between your graphics card and your system RAM. Running SmolLM3 3B or Kandinsky 3.1 requires 2.2 GB of video memory at Q4_K_M quantization and needs an additional 4.2 GB of system RAM. Offloading allows you to run larger models like MusicGen at 3.3B or SDXL Turbo at 3.5B, but it reduces processing speed because system RAM is much slower than GDDR5 graphics memory.

Running text generation models requires extra memory space to store the active conversation history. This active memory is called the context window. While a model like Qwen3 1.7B uses 1.7 GB of memory at Q6_K quantization, this figure only covers the base model weights. Running a standard 4k context window requires additional memory, which will exceed the 2 GB limit of the card and force some data into system RAM.

For audio and image tasks, models like Whisper Large v3 use 2 GB of memory at Q8_0 quantization. Stable Video Diffusion and AudioGen use 1.9 GB of memory at Q8_0 quantization. These specialized models do not use a text context window, but they still operate at the absolute limit of the hardware. Keeping your operating system display tasks minimal will help free up the maximum amount of the 2 GB physical memory for your local installations.