Best local AI models for NVIDIA Quadro M4000M

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 M4000M is a professional mobile graphics card equipped with 4 GB of GDDR5 memory. This dedicated video memory determines the maximum size of the artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 4 GB limit, it executes with the fastest possible processing speeds.

To fit larger models into the limited memory space, developers use quantization. The quant column indicates the compression level applied to the model weights. For example, a Q4_K_M quant uses approximately four bits per weight, while a Q6_K or Q8_0 quant uses more bits for higher accuracy. Using a Q4_K_M quant allows models like Lumina-Next or CogVideoX 2B to run within 3.7 GB of memory.

Models with higher precision quants require more memory but deliver better output quality. On this hardware, you can run the 4B parameter models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 at a Q6_K quantization level using 3.9 GB of memory. Smaller models like SmolLM3 3B and Kandinsky 3.1 can run at a high quality Q8_0 quantization level using 3.8 GB of memory.

When a model exceeds the 4 GB limit, you must use CPU offload. This technique splits the model layers between your graphics card and your system memory. For this setup, we assume you have 32 GB of system RAM. Offloading allows you to run larger models like Mistral 7B, which needs 5.7 GB at Q4_K_M and 7.7 GB of system RAM, but your processing speed will decrease significantly.

Other offload options include Qwen2.5 7B, OLMo 2 7B, Falcon 3 7B, Command R7B, and OpenHermes 2.5. These models require 5.1 GB at Q4_K_M and 7.1 GB of system RAM. You can also run Stable Diffusion XL with CPU offload, which requires 4.1 GB at FP8 and 6.1 GB of system RAM.

Memory consumption calculations must include the context window. Running a model with a standard 4k context window requires additional memory for the active conversation history. If you experience out of memory errors on your 4 GB card, you should reduce the context window size or select a smaller model to free up the necessary memory.