Best local AI models for NVIDIA Quadro P4000 MAX-Q

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 P4000 MAX-Q is a mobile workstation graphics card equipped with 8 GB of GDDR5 memory. This dedicated memory size dictates the maximum size of the artificial intelligence models you can run locally. To fit a model entirely on this hardware, the combined size of the model weights and the active context window must remain under the 8 GB physical limit.

Quantization is a compression technique that reduces the precision of model weights to save memory. In the model listings, the best quant column shows the optimal balance of size and quality. For example, the 10B Mochi 1 model fits in 7.3 GB of memory using the Q4_K_M quantization level. Other models like Gemma 2 9B use exactly 8 GB of memory at the Q4_K_M quantization level.

Many popular models can run at higher precision levels on this hardware. The 8B Llama 3.1 model fits comfortably using 7.4 GB of memory with the Q5_K_M quantization. Smaller models like Mistral 7B and Qwen2.5 7B can use the higher quality Q6_K quantization. At Q6_K, Mistral 7B uses 7.4 GB of memory and Qwen2.5 7B uses 6.9 GB of memory.

When a model exceeds the 8 GB physical limit of your graphics card, you must use CPU offloading. This process splits the model weights between your graphics memory and your system RAM. Assuming you have 32 GB of system RAM, you can run larger models like the 12B Gemma 3 or Mistral NeMo 12B. These models require 8.8 GB of memory at Q4_K_M and need 10.8 GB of system RAM to function.

CPU offloading allows you to run massive models but it comes with a performance cost. Transferring data between system RAM and graphics memory over the system bus is much slower than using dedicated GDDR5 memory. This transfer bottleneck significantly reduces the generation speed of your local model.

You must also consider the memory cost of the context window. The memory figures listed are calculated using a standard 4k context window. If you increase the context window to process longer documents or chat histories, the memory usage will rise. This extra memory demand can push a model past the 8 GB limit and force slow CPU offloading.