Best local AI models for NVIDIA GTX TITAN X

The largest models that fit fully

The 30 largest of the 147 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 TITAN X is equipped with 12 GB of GDDR5 memory. This onboard memory determines the size of the artificial intelligence models you can run locally. To fit a model entirely on the graphics card, the total size of the model files must remain under this 12 GB limit. Running models directly in the graphics memory ensures the fastest possible processing speeds.

The quantization column shows the compression level used to shrink these models. Quantization reduces the precision of model weights to save space. For example, DeepSeek-Coder-V2 16B and Kimi-VL A3B both use the Q4_K_M quantization to fit into 11.7 GB of memory. Models like Phi-4 and Wan 2.2 T2I use the Q5_K_M quantization to fit into 11.9 GB of memory. Smaller models like Gemma 4 12B and Mistral NeMo 12B can use a higher quality Q6_K quantization which fits into 11.8 GB of memory.

When a model exceeds the 12 GB graphics memory limit, you must offload some layers to your system RAM. This process requires a system with at least 32 GB of system RAM. Offloading allows you to run larger models, but it reduces processing speed because system RAM is much slower than GDDR5 graphics memory. For instance, FLUX.1 dev requires 14.4 GB of memory at FP8 and uses 16.4 GB of system RAM. Codestral 22B requires 16.1 GB of memory at Q4_K_M and uses 18.1 GB of system RAM.

Other models also utilize CPU offloading to run on this hardware configuration. CogVLM2 requires 13.9 GB of memory at Q4_K_M and uses 15.9 GB of system RAM. Qwen-Image requires 14.6 GB of memory at Q4_K_M and uses 16.6 GB of system RAM. Solar Pro requires 16.1 GB of memory at Q4_K_M and uses 18.1 GB of system RAM. These options expand your capabilities if you accept the slower performance of system memory.

You must also consider the memory cost of context length. The listed memory figures are calculated using a standard 4k context window. If you increase the context window to process longer documents or extended conversations, the memory usage will rise. This extra memory demand can push a model past the 12 GB limit of your graphics card, forcing the system to slow down or fail to run the model.