Best local AI models for NVIDIA Quadro K5100M

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 K5100M is an older mobile workstation graphics card equipped with 8 GB of GDDR5 dedicated video memory. This memory pool determines the size of the artificial intelligence models you can run locally. To load a model entirely on this GPU, the model files and its active working memory must fit within this 8 GB limit. Running models fully on the graphics card ensures the fastest possible processing speeds.

Quantization is a method that compresses model files to save space. In the model listings, the best quant column shows the highest quality quantization level that still fits inside your video memory. For example, the Mochi 1 10B model fits at a Q4_K_M quantization which uses 7.3 GB of video memory. Other models like Llama 3.1 8B can run at a higher quality Q5_K_M quantization using 7.4 GB of video memory. Smaller models like Mistral 7B can run at an even higher quality Q6_K quantization using 7.4 GB of video memory.

When a model is too large for the 8 GB video memory, you must use CPU offload. This technique splits the model between your graphics card and your system RAM. We assume your system has 32 GB of system RAM for these calculations. Offloading allows you to run larger models but it comes with a speed penalty. Your system must constantly move data between the system RAM and the GPU which slows down generation times significantly.

Several larger models are viable through CPU offload. The Gemma 3 12B model requires 8.8 GB of video memory at Q4_K_M quantization and needs 10.8 GB of system RAM. The FLUX.1 dev model is a 12B parameter model that needs 14.4 GB of video memory at FP8 or optimized settings and requires 16.4 GB of system RAM. Even a Vicuna 13B model can run this way by using 9.5 GB of video memory at Q4_K_M quantization and 11.5 GB of system RAM.

You must also consider the active context window when loading these models. The memory numbers listed here are calculated using a standard 4k context window. If you increase the context length to process longer documents or longer chat histories, the memory usage will rise. This extra memory demand might force you to use a lower quantization level or rely more heavily on CPU offload to prevent out of memory errors.