Best local AI models for NVIDIA Quadro P400

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 P400 is an entry level professional graphics card equipped with 2 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When running local models, the entire neural network weights and the active working memory must fit within this physical limit to achieve reasonable processing speeds.

To fit modern models into this 2 GB limit, developers use quantization. Quantization is a compression method that reduces the precision of model weights. The quant column shows the optimal format for each model, such as Q4_K_M, Q5_K_M, Q6_K, or Q8_0. Lower quantization levels like Q4_K_M use less video memory but reduce output quality, while higher levels like Q8_0 preserve original model accuracy at the cost of a larger memory footprint.

For models that fit completely within the onboard memory, you can run options like the Allegro 2.8B model at Q4_K_M quant using 2 GB of video memory. The Open-Sora Plan 2.7B model also fits at Q4_K_M quant using 2 GB. Other viable options include the LFM2 2.6B model, the Playground v2.5 2.6B model, and the Stable Diffusion 3.5 Medium 2.5B model, which use between 1.8 GB and 1.9 GB of video memory.

You can also run smaller models with higher precision quants. The SmolVLM 2B model, Stable Diffusion 3 Medium 2B model, and Pyramid Flow 2B model all run at Q6_K quant using 2 GB of video memory. Extremely compact models like the TinyLlama 1.1B model and the SantaCoder 1.1B model run at Q8_0 quant, requiring only 1.4 GB of video memory.

When a model exceeds the 2 GB video memory limit, you must use CPU offload. This technique splits the model between your graphics card and your system RAM. For example, running the SmolLM3 3B model or the Kandinsky 3.1 3B model at Q4_K_M quant requires 2.2 GB of video memory and 4.2 GB of system RAM. Running the Stable Diffusion XL 3.417B model at FP8 or optimized settings requires 4.1 GB of video memory and 6.1 GB of system RAM. Offloading allows you to run larger models, but it significantly reduces generation speed because system RAM is much slower than GDDR5 video memory.

You must also consider the context window size when running text models. The memory figures listed are calculated for a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory requirement will grow. This extra memory usage can easily exceed the 2 GB limit of your card and force the system to slow down.