Best local AI models for NVIDIA RTX 4070

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 is equipped with 12 GB of GDDR6X memory. This video memory determines the maximum size of the artificial intelligence models you can run locally. To fit a model entirely on your graphics card, the total memory used by the model must remain under this 12 GB limit. Running models fully inside your video memory ensures the fastest possible processing speeds.

The quantization column shows the compression level applied to each model. Raw models are often too large for consumer hardware, so they are compressed into smaller formats like Q4_K_M, Q5_K_M, Q6_K, or Q8_0. A lower quantization level like Q4_K_M reduces the memory footprint significantly. This compression allows you to run larger architectures like the DeepSeek-Coder-V2 16B or Kimi-VL A3B within 11.7 GB of video memory.

When a model exceeds your 12 GB limit, you must use CPU offload. This technique splits the workload between your graphics card and your system RAM. For example, running the FLUX.1 dev model at FP8 requires 14.4 GB of memory, which uses 16.4 GB of system RAM alongside your GPU. Other models like Codestral 22B require 16.1 GB of memory and use 18.1 GB of system RAM. CPU offload makes these larger models runnable, but it reduces generation speed because system RAM is much slower than GDDR6X.

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 Gemma 3 12B, represent the model at a base 4k context window. If you increase the context length to process longer documents, the memory usage will rise. This extra memory demand can push a model that fits at 4k context over the 12 GB limit, forcing performance-degrading CPU offload.

For optimal local performance without offloading, you can target models that leave a comfortable safety margin. The Gemma 2 9B model at Q6_K uses 10.3 GB of memory, leaving room for context. If you require maximum precision, smaller models like GLM-4 9B or Yi-Coder 9B can run at a high Q8_0 quantization while using 11.4 GB of memory.