Best local AI models for NVIDIA Quadro P5200 MAX-Q

The largest models that fit fully

The 30 largest of the 155 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 P5200 MAX-Q is a mobile workstation graphics card equipped with 16 GB of GDDR5 memory. This dedicated memory size determines the maximum size of the artificial intelligence models you can run locally. To run a model entirely on your graphics hardware, the model files and its operational memory must fit within this 16 GB limit. Running models locally ensures complete data privacy and removes any reliance on external cloud services.

The quant column indicates the quantization level used to compress each model. Raw models are often too large for consumer hardware, so they are compressed into smaller formats like Q4_K_M, Q5_K_M, Q6_K, or Q8_0. A lower quantization level like Q4_K_M uses less memory but sacrifices a small amount of accuracy. A higher quantization level like Q8_0 or FP8 preserves more original model quality but requires significantly more of your 16 GB video memory.

For models that fit completely inside your video memory, you can run large options like the 21B gpt-oss-20b or Reka Flash 3 at a Q4_K_M quantization using 15.4 GB. You can also run the 20B Qwen-Image at Q4_K_M using 14.6 GB. If you prefer higher precision quants like Q6_K, you can run the 15B StarCoder2 3B / 7B / 15B using 14.8 GB or the 14.7B Qwen2.5 14B using 15.3 GB. Highly optimized Q8_0 options like the 12B Gemma 3 12B or Mistral NeMo 12B are also fully supported using 15.3 GB.

When a model exceeds your 16 GB video memory, you can use CPU offloading if your computer has at least 32 GB of system RAM. This technique splits the model between your graphics card and your system memory. For example, the 22B Solar Pro or Codestral 22B requires 16.1 GB at Q4_K_M, which needs 18.1 GB of system RAM to offload the overflow. Larger models like the 27B Gemma 3 27B require 19.8 GB at Q4_K_M and need 21.8 GB of system RAM. Offloading allows you to run these massive models, but it costs significant processing speed because system RAM is much slower than GDDR5 video memory.

You must also consider the memory cost of your context window. The memory figures listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or longer conversations, the model will require more memory. This extra memory demand might force you to use a lower quantization level or offload more layers to your system RAM to avoid running out of video memory.