Best local AI models for NVIDIA TITAN V

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 TITAN V features 12 GB of HBM2 memory. This high bandwidth memory determines the size of the artificial intelligence models you can run locally. To fit a model entirely on this hardware, the total memory used by the model weights and the active context must remain under this 12 GB limit.

The quantization column shows the compression level applied to each model. Quantization reduces the precision of model weights to save space. For example, the Q4_K_M quant allows the DeepSeek-Coder-V2 16B model to run using 11.7 GB of memory. Higher quants like Q6_K or Q8_0 offer better accuracy but require more space, which is why the Gemma 3 12B model uses 11.8 GB at Q6_K and the Nemotron Nano 9B model uses 11.4 GB at Q8_0.

When a model exceeds the 12 GB physical memory of the card, you must use CPU offload. This process splits the model between your graphics card and your system RAM. Running FLUX.1 dev requires 14.4 GB of memory at FP8, which means you need 16.4 GB of system RAM alongside your GPU. CPU offload allows you to run larger models like Codestral 22B, but it reduces processing speed because data must travel between the system RAM and the GPU.

Context window size also impacts your available memory. The memory usage figures listed on this page assume a standard 4k context window. If you increase the context window to process longer documents or longer chat histories, the memory usage will rise. This extra memory consumption might force you to use a smaller model or a lower quantization level to prevent out of memory errors.

Many powerful models fit within the local limits of this card. You can run Phi-4 or Wan 2.2 T2I at Q5_K_M quantization using 11.9 GB of memory. For video generation, HunyuanVideo fits at Q5_K_M using 11.1 GB of memory. If you have 32 GB of system RAM, you can expand your options to run CogVLM2 or Solar Pro by offloading the extra weight to your system memory.