Best local AI models for NVIDIA Quadro K5200

The largest models that fit fully

The 30 largest of the 123 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 K5200 is equipped with 8 GB of GDDR5 graphics memory. This hardware limit dictates the size of the artificial intelligence models you can run locally. To fit a model entirely within this VRAM allocation, the model files must be smaller than the available space. Running models fully on your graphics card ensures the fastest processing speeds for text generation and image creation.

Quantization is a method that compresses model files to save memory. The quant column shows the best balance of size and quality for each model. For example, the 10B Mochi 1 model fits in 7.3 GB of VRAM using the Q4_K_M quantization level. Smaller models like Mistral 7B can run at a higher Q6_K quantization level using 7.4 GB of VRAM. Higher quantization levels preserve more of the original model accuracy.

When a model exceeds the 8 GB VRAM limit, you must use CPU offload. This technique splits the model between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these scenarios. For instance, Gemma 3 12B requires 8.8 GB of VRAM at the Q4_K_M quantization level and needs 10.8 GB of system RAM to function. Offloading allows you to run larger models like Vicuna 13B, but it significantly reduces generation speed.

Using CPU offload comes with a performance cost. Moving data between your system RAM and the Quadro K5200 graphics card is much slower than keeping all data inside the GDDR5 memory. While models like FLUX.1 dev can run by using 14.4 GB at FP8 with 16.4 GB of system RAM, the generation times will be much longer than fully contained models like Llama 3.1 8B at Q5_K_M which uses 7.4 GB of VRAM.

You must also consider the memory cost of context length. The listed VRAM usage figures are calculated using a standard 4k context window. If you increase the context window to process longer documents or chat histories, the model will require more memory. This extra memory demand can push a model that normally fits inside 8 GB VRAM into CPU offload territory, which will slow down your system.