Best local AI models for NVIDIA Quadro K1100M

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 K1100M is an older mobile workstation graphics card equipped with 2 GB GDDR5 memory. This dedicated memory size determines which artificial intelligence models can run directly on your hardware. When a model fits entirely within this 2 GB frame buffer, it executes with the best possible speed. If a model exceeds this limit, you must use alternative execution strategies to run it.

To fit modern models into 2 GB of memory, you must use quantized versions. The quant column shows the best quantization format for each model. Quantization reduces the precision of model weights to save space. For example, the Allegro 2.8B model fits in 2 GB of memory using the Q4_K_M quantization. Smaller models like Moondream 2 with 1.9B parameters can use the higher quality Q6_K quantization while staying under the 1.9 GB limit.

When a model requires more than 2 GB of memory, you can use CPU offloading. This technique splits the model weights between your graphics card and your system RAM. We assume your computer has 32 GB of system RAM for these scenarios. For example, running the SmolLM3 3B model requires 2.2 GB of memory at Q4_K_M quantization. This setup offloads the extra data to your system RAM, using 4.2 GB of system RAM in the process.

Offloading allows you to run larger models like the 3.5B parameter SDXL Turbo or SDXL Lightning. These models need 2.6 GB of memory at Q4_K_M quantization and use 4.6 GB of system RAM. However, offloading comes with a performance cost. Moving data between the graphics card and system RAM is much slower than keeping everything in the dedicated GDDR5 memory. Your generation speeds will drop significantly when offloading.

You must also consider the 4k context caveat when running local models. The memory figures listed for these models represent the base requirements. Running text models with long conversations or large inputs increases memory usage. A standard 4k context window requires extra memory for the key value cache. This extra demand can push a model that normally fits in 2 GB over the limit, forcing the system to offload data to RAM.

For the best experience on the NVIDIA Quadro K1100M, choose models that fit fully within your 2 GB GDDR5 memory. Models like TinyLlama 1.1B use only 1.4 GB of memory at Q8_0 quantization. This leaves plenty of headroom for system overhead and context. If you need to run larger tools like Stable Diffusion XL, prepare for slower generation times due to the 4.1 GB memory requirement and the 6.1 GB system RAM offload.