Best local AI models for NVIDIA Quadro P4000

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 is a workstation graphics card equipped with 8 GB of GDDR5 memory. This dedicated video memory determines the size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 8 GB limit, it runs at maximum speed because the graphics processor can access the parameters directly without waiting for slower system memory.

To fit larger models into the available memory, files are compressed using quantization. The quant column shows the specific compression level used for each model. For example, a Q4_K_M quant uses approximately four bits per parameter, while a Q6_K quant uses approximately six bits per parameter. Higher quantization levels like Q6_K preserve more original model accuracy but require more memory space.

Several highly capable models fit entirely within the 8 GB boundary of the Quadro P4000. You can run the Mochi 1 10B model at the Q4_K_M quant which uses 7.3 GB of memory. The Gemma 2 9B model fits at the Q4_K_M quant using exactly 8 GB. Other options include Llama 3.1 8B at the Q5_K_M quant using 7.4 GB and Mistral 7B at the Q6_K quant using 7.4 GB.

If you want to run models that exceed 8 GB, you must use CPU offloading. This process splits the model layers between your graphics card and your system RAM. Offloading allows you to run larger models but it significantly reduces processing speed because the system RAM is much slower than GDDR5 memory.

Assuming your computer has 32 GB of system RAM, you can offload models like FLUX.1 dev 12B which needs 14.4 GB at FP8 and 16.4 GB of system RAM. You can also run Gemma 3 12B or Mistral NeMo 12B which both need 8.8 GB at the Q4_K_M quant and 10.8 GB of system RAM. Vicuna 13B is also accessible through offloading, requiring 9.5 GB at the Q4_K_M quant and 11.5 GB of system RAM.

When planning your deployments, remember the 4k context caveat. The memory usage figures listed for these models assume a standard context window of four thousand tokens. If you increase the context length to process longer documents, the memory usage will rise and may exceed your available hardware limits.