Best local AI models for NVIDIA GTX 850A

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 GTX 850A is an entry level graphics hardware option equipped with 2 GB of DDR3 memory. When running artificial intelligence models locally, this dedicated memory capacity is the main limiting factor. The model weights must fit inside this space to run at hardware speed. If a model exceeds this limit, the system must use slower system memory which reduces performance.

To make models fit into the 2 GB limit, developers use quantization. This process compresses the model weights to use fewer bits per parameter. In our catalog, the best quant column shows the optimal compression level for this hardware. For example, a Q4_K_M quant uses four bits per weight to fit larger models like Allegro 2.8B or Open-Sora Plan 2.7B into exactly 2 GB of memory. Smaller models like TinyLlama 1.1B can run at Q8_0 which uses eight bits per weight and requires 1.4 GB of memory.

You can run models that are larger than 2 GB by using CPU offload. This technique splits the model between your graphics card and your system RAM. If you have 32 GB of system RAM, you can run models like SmolLM3 3B or Kandinsky 3.1. These models need 2.2 GB of graphics memory at Q4_K_M and require an additional 4.2 GB of system RAM. Offloading allows you to run Stable Diffusion XL with its 3.417B parameters, but this requires 4.1 GB of graphics memory at FP8 and 6.1 GB of system RAM which will slow down generation speeds.

When running text models, the context window size affects your memory usage. The memory figures listed are calculated at a standard 4k context window. If you increase the context window to process longer documents, the system will require more memory. This extra demand can push a model that normally fits in 2 GB over the limit, forcing the system to offload data to your system RAM.

For audio and speech tasks, you can run Whisper Large v3 at Q8_0 which uses 2 GB of memory. You can also run Parler-TTS at Q5_K_M which uses 1.9 GB of memory. For vision tasks, Moondream 2 fits at Q6_K using 1.9 GB of memory. These options allow you to run diverse local AI tasks on your hardware as long as you select the correct quantization level.