Best local AI models for NVIDIA Quadro M3000M

The largest models that fit fully

The 30 largest of the 81 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 M3000M is a professional graphics card equipped with 4 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To run a model smoothly without system slowdowns, the model files and active memory must fit within this 4 GB limit.

Quantization is a compression method that shrinks model files so they fit into smaller memory spaces. The quant column shows the best quality level your hardware can support for each model. For example, the 5B Lumina Next or Lumina Image 2.0 fits in 3.7 GB of memory using a Q4_K_M quantization. Other models like the 4B Qwen3 or Gemma 3 4B run at a higher Q6_K quantization using 3.9 GB of memory.

Smaller models can run at the highest quality levels on this hardware. The 3B SmolLM3 and Replit Code v1.5 3B can use the Q8_0 quantization which uses 3.8 GB of memory. Image generation models like Stable Diffusion 3.5 Medium also fit within the limits. This model uses 3.2 GB of memory at the Q8_0 quantization level.

If a model is too large for the 4 GB video memory, you must use CPU offload. This process splits the workload between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these setups. Running Mistral 7B requires 5.7 GB of video memory at Q4_K_M quantization and needs 7.7 GB of system RAM to function.

CPU offload allows you to run larger models like the 7B Qwen2.5 or Falcon 3 7B. These models need 5.1 GB of video memory at Q4_K_M quantization and 7.1 GB of system RAM. Offloading parts of the model to your system RAM prevents out of memory errors. However, this process will reduce your generation speed because system RAM is much slower than GDDR5 video memory.

You must also consider the context window size when loading these models. The memory figures listed here are measured with a basic 4k context window. If you increase the context window to process longer documents, the memory usage will rise quickly. Keeping your context window at 4k or lower ensures the models do not exceed your available memory.