Best local AI models for NVIDIA Quadro K3000M

The largest models that fit fully

The 30 largest of the 56 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 K3000M is a mobile workstation graphics card equipped with 2 GB of GDDR5 memory. This hardware memory size determines the maximum size of the artificial intelligence models you can run entirely on the graphics processor. When a model fits completely within this 2 GB limit, it executes with the fastest possible processing speeds because the system does not need to transfer data over the slower system bus.

To fit modern models into this memory limit, developers use quantization. The quant column shows the specific compression level required to make a model fit. For example, the Allegro 2.8B model fits within 2 GB of memory when using the Q4_K_M quantization level. Smaller models like Moondream 2 can run at a higher Q6_K quality level while using 1.9 GB of memory. TinyLlama 1.1B can run at the high quality Q8_0 quantization level while using only 1.4 GB of memory.

When a model exceeds the 2 GB hardware limit, you must use CPU offload. This technique splits the model layers between your graphics card and your system RAM. For instance, running the SmolLM3 3B model at Q4_K_M requires 2.2 GB of graphics memory and 4.2 GB of system RAM. While CPU offload allows you to run larger models like Stable Diffusion XL or MusicGen, it comes with a performance cost because transferring data between system RAM and the graphics card slows down generation speeds.

You must also consider the memory cost of context length. The memory figures listed for these models assume a standard base context window. If you increase the context window to 4k tokens or higher, the system requires additional memory to store the active conversation history. This extra memory usage can push a model that normally fits within the 2 GB limit into a situation where it must offload to system RAM.

By selecting the appropriate quantization level, you can run a variety of specialized models locally. You can run image generators like Stable Diffusion 3 Medium at Q6_K or audio tools like Whisper Large v3 at Q8_0. Understanding the balance between model size, quantization level, and system RAM offloading allows you to get the best possible performance from your hardware.