Best local AI models for NVIDIA TITAN RTX

The largest models that fit fully

The 30 largest of the 173 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 RTX graphics card features 24 GB of GDDR6 onboard memory. This memory size determines which artificial intelligence models you can run entirely on the hardware. When a model fits completely within this 24 GB limit, it executes at maximum speed. If a model exceeds this limit, you must offload some processing to your system RAM, which slows down performance.

The quantization column shows the compression level used to fit these models. Quantization reduces the size of a model so it uses less memory. For example, Qwen3 32B, Qwen3.5 dense variants, Aya Expanse 32B, Granite 4.0 32B, and Qwen2.5-Coder 32B all fit within 23.4 GB of memory using the Q4_K_M quantization. Other models like Gemma 3 27B, Wan 2.2, and Gemma 4 26B-A4B fit using the Q5_K_M quantization. Smaller models like DeepSeek-Coder-V2 16B and StarCoder2 15B can run at the higher quality Q8_0 quantization.

When you run larger models, you must use CPU offloading. This process splits the workload between your graphics card and your system RAM. If you have 32 GB of system RAM, you can run larger models like Yi 1.5 34B, Granite Code 34B, or Command R 35B. These models require more memory than the card has. For example, Command R 35B needs 25.6 GB of memory at Q4_K_M quantization and requires 27.6 GB of system RAM to run.

Every model requires extra memory to track the conversation history. This history is called the context window. The memory figures listed here assume a standard context window of 4000 tokens. If you increase this context window to process longer documents, the model will require more memory. This extra memory requirement might force you to use a smaller model or a lower quantization level to prevent system slowdowns.

You can optimize your setup by choosing the right model size for your specific task. Models like Mistral Small 3.2 24B and Devstral Small 1.1 24B fit within 23.6 GB using the Q6_K quantization. If you need image processing, Qwen-Image 20B fits within 19.7 GB using the Q6_K quantization. Choosing a model that fits entirely within the 24 GB limit of your NVIDIA TITAN RTX ensures the fastest possible generation speeds.