Best local AI models for NVIDIA Quadro K3100M

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 K3100M is a mobile workstation 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. To fit within this 4 GB limit, models must be compressed using quantization, which reduces the precision of the model weights to save space.

The best quantization column shows the highest quality format that fits inside your video memory. For example, the 5B parameter Lumina-Next or Lumina-Image 2.0 and CogVideoX 2B or 5B models can run at a Q4_K_M quantization, which uses 3.7 GB of memory. Models like Qwen3 4B, Gemma 3 4B, Gemma 4 E4B, MiniCPM 3 4B, Danube 3 4B, and Fish Speech 1.5 or OpenAudio S1 can run at a higher quality Q6_K quantization, using 3.9 GB of memory.

Smaller models can run at even higher precision. The SmolLM3 3B, Replit Code v1.5 3B, Kandinsky 3.1, Voxtral Mini or Small, Orpheus TTS, and Higgs Audio v2 models fit within 3.8 GB of memory using the Q8_0 quantization. Other options include the 2.8B Allegro using 3.6 GB, the 2.7B Open-Sora Plan using 3.4 GB, and the 2.5B Stable Diffusion 3.5 Medium using 3.2 GB, all at Q8_0 precision.

When a model exceeds the 4 GB video memory limit, you must use CPU offload. This technique splits the model between your graphics card and your system RAM, assuming you have 32 GB of system RAM. Offloading allows you to run larger models, but it costs processing speed because transferring data between system RAM and your graphics card is much slower than using dedicated video memory.

With CPU offload, you can run the 7B parameter Mistral, which needs 5.7 GB of memory at Q4_K_M quantization and requires 7.7 GB of system RAM. Other 7B models like Qwen2.5 0.5B or 1.5B or 3B or 7B, OLMo 2 1B or 7B, Falcon 3 1B or 3B or 7B, Command R7B, and OpenHermes 2.5 need 5.1 GB of memory at Q4_K_M and require 7.1 GB of system RAM.

Be aware of the context window limit when running these models. The memory usage figures are calculated using a standard 4k context window. If you increase the context length to process longer documents or conversations, the memory requirements will rise quickly, which may force you to use lower quantization levels or rely more heavily on CPU offload.