Best local AI models for NVIDIA RTX 4070 SUPER

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 SUPER features 12 GB of GDDR6X video memory. This memory size determines which artificial intelligence models you can run entirely on your graphics card. For the fastest generation speeds, the model files and their working memory must fit within this 12 GB limit. Running models completely inside video memory prevents slow data transfers from your system RAM.

To fit larger models into video memory, developers use quantization. The quant column shows the compression level applied to each model. For example, the Q4_K_M quant compresses weights to approximately four bits. This allows the DeepSeek-Coder-V2 16B model to fit into 11.7 GB of video memory. Higher quants like Q6_K or Q8_0 offer better accuracy but require more memory. Mistral NeMo 12B fits at Q6_K using 11.8 GB, while Nemotron Nano 9B fits at Q8_0 using 11.4 GB.

When a model exceeds 12 GB, you must offload some layers to your system CPU. This offloading process requires a system with at least 32 GB of system RAM. For instance, FLUX.1 dev needs 14.4 GB of memory at FP8, which requires 16.4 GB of system RAM for offloading. Similarly, Codestral 22B needs 16.1 GB at Q4_K_M, which utilizes 18.1 GB of system RAM. Offloading allows you to run these larger models, but it significantly reduces generation speed.

The memory figures listed for these models assume a standard 4k context window. As your conversation or text prompt grows longer, the model requires additional video memory to track the history. If you write very long prompts or generate long responses, the memory usage will exceed the listed values. This can cause the model to slow down or fail if it runs out of video memory.

You can run diverse model types on this hardware. For text and reasoning, you can run Phi-4 at Q5_K_M using 11.9 GB or Gemma 3 12B at Q6_K using 11.8 GB. For video generation, HunyuanVideo fits at Q5_K_M using 11.1 GB, and Mochi 1 fits at Q6_K using 9.8 GB. Image generation models like FLUX.1 schnell also run locally at Q6_K using 11.8 GB.