Best local AI models for NVIDIA RTX 4070 Ti

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 RTX 4070 Ti features 12 GB of GDDR6X memory. This dedicated memory determines the maximum size of the artificial intelligence models you can run locally. To load a model entirely on your graphics card, the model files and the active workspace must fit within this 12 GB limit. Running models fully on your video memory ensures the fastest processing speeds.

The quantization column indicates the compression level applied to each model. Raw models are often too large for consumer hardware, so developers use quantization to reduce size while preserving quality. For this hardware, a Q4_K_M quantization represents a four bit format that allows larger models like DeepSeek-Coder-V2 16B, Kimi-VL A3B, Apriel-1.5-15B-Thinker, and StarCoder2 15B to fit. Highly optimized models like Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and FLUX.1 schnell can run at a higher Q6_K quantization.

When a model exceeds your 12 GB limit, you must use CPU offload. This process splits the model between your graphics card and your system memory. For example, FLUX.1 dev requires 14.4 GB at FP8, which uses your video memory and 16.4 GB of system RAM. Other models like CogVLM2, Solar Pro, and Codestral 22B also require system RAM to function. While offloading allows you to run these larger models, it significantly reduces generation speed because system RAM is much slower than GDDR6X memory.

Your available memory must also accommodate the context window. The memory figures listed, such as 11.9 GB for Phi-4 or 11.8 GB for Pixtral 12B, represent the model at its base state. As you write longer prompts and receive longer responses, the context window expands. A standard 4k context window requires additional memory. If you push the context window too high, you may exceed the 12 GB limit and experience slowdowns.

Selecting the right model depends on your specific task. For coding and reasoning, DeepSeek-Coder-V2 16B and Qwen2.5 14B offer excellent performance within the memory limit. For media generation, Wan 2.2 T2I and HunyuanVideo fit within your video memory. If you have 32 GB of system RAM, you can expand your options to larger models like Reka Flash 3 or Solar Pro by accepting the speed trade-offs of CPU offloading.