Best local AI models for NVIDIA Quadro K1000M

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 K1000M is an older mobile workstation graphics card equipped with 2 GB of DDR3 video memory. This dedicated VRAM is the primary bottleneck for running artificial intelligence models locally. To run a model entirely on this GPU, the model files and the active memory space must fit within this 2 GB limit. If a model exceeds this capacity, it cannot run solely on the graphics hardware.

Quantization is a compression method that reduces the size of AI models so they fit into smaller memory spaces. The quant column shows the best quantization level for each model on this hardware. For example, Allegro 2.8B and Open-Sora Plan 2.7B can run on this GPU using the Q4_K_M quantization, which uses exactly 2 GB of VRAM. Smaller models like TinyLlama 1.1B and SantaCoder 1.1B can use the higher quality Q8_0 quantization because they only require 1.4 GB of VRAM.

When a model is too large for the 2 GB VRAM, you must use CPU offloading. This process splits the workload between your GPU and your system RAM. Assuming your computer has 32 GB of system RAM, you can run larger models like SmolLM3 3B or Kandinsky 3.1. These 3B models need 2.2 GB of VRAM at Q4_K_M quantization and require an additional 4.2 GB of system RAM. Offloading allows you to run these models but it significantly reduces processing speed.

Larger image generation models also require CPU offloading on this hardware. Stable Diffusion XL has 3.417B parameters and needs 4.1 GB of VRAM at FP8 or optimized settings, which requires 6.1 GB of system RAM. Similarly, SDXL Turbo and SDXL Lightning are 3.5B parameter models that need 2.6 GB of VRAM at Q4_K_M quantization, requiring 4.6 GB of system RAM to function.

You must also consider the memory cost of context length. Running text models with a standard 4k context window increases the memory footprint during inference. The listed VRAM usage figures represent the base model requirements. If you generate long responses or input large prompts, the system may run out of memory or force more data onto the slower system RAM.