Best local AI models for NVIDIA Quadro P1000

The largest models that fit fully

The 30 largest of the 81 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 P1000 is an entry level professional graphics card equipped with 4 GB of GDDR5 memory. This memory size determines the maximum size of the artificial intelligence models you can run entirely on the hardware. To fit models within this limit, we use quantized versions which compress the model weights to save space. The quant column shows the specific quantization level that balances model accuracy with memory usage.

For local execution without sharing system memory, your model must fit within the 4 GB GDDR5 limit. The largest fitting models include Lumina-Next or Lumina-Image 2.0 and CogVideoX 2B or 5B. Both of these 5B models run at a Q4_K_M quantization and use 3.7 GB of memory. DeepSeek-VL2 at 4.5B with a Q5_K_M quantization fits tightly by using 3.8 GB of memory.

Several high quality vision and language models fit within the native memory. Phi-3.5-vision at 4.2B runs at Q5_K_M quantization using 3.6 GB. You can also run 4B models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1. These 4B models use a Q6_K quantization and require 3.9 GB of memory.

Other compact models fit well on this hardware. Phi-4-mini-instruct and Phi-3.5 Mini at 3.8B use a Q6_K quantization and consume 3.7 GB of memory. OmniGen or OmniGen2 at 3.8B also uses 3.7 GB at Q6_K. For image generation, SDXL Turbo and SDXL Lightning at 3.5B use 3.4 GB of memory with a Q6_K quantization. Smaller models like SmolLM3 3B and Replit Code v1.5 3B can run at a higher Q8_0 quantization using 3.8 GB of memory.

When a model is too large for the onboard memory, you can offload parts of it to your system RAM. This offload process allows you to run larger models but reduces processing speed because system RAM is slower than GDDR5. For example, running Mistral 7B at Q4_K_M requires 5.7 GB of memory, which uses 7.7 GB of system RAM. Similarly, Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5 require 5.1 GB of memory at Q4_K_M and use 7.1 GB of system RAM.

Be aware of the context window size when running these models. The memory usage figures listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents, the memory requirements will rise. This extra memory usage might force you to use a lower quantization or offload more layers to your system RAM.