Best local AI models for NVIDIA RTX 4060

The largest models that fit fully

The 30 largest of the 123 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 4060 graphics card features 8 GB GDDR6 of dedicated video memory. This memory size determines which artificial intelligence models you can run entirely on your hardware. When a model fits completely within this video memory, it processes tokens at maximum speed. If a model exceeds this limit, you must use alternative execution strategies.

The quantization column shows the compression level used to fit these models into memory. Quantization reduces the precision of model weights to save space. For example, Gemma 2 9B fits into 8 GB used with the Q4_K_M quantization. Other models like Granite 3.3 8B, Ministral 8B, and InternLM 3 8B utilize the Q6_K quantization to fit within 7.9 GB of video memory. Lower quantization levels like Q4_K_M allow larger models to fit but may slightly reduce output quality.

Models with higher parameter counts require CPU offloading when paired with 32 GB of system RAM. For instance, FLUX.1 dev requires 14.4 GB at FP8 or optimized settings, which uses 16.4 GB of system RAM. Similarly, Gemma 3 12B, Gemma 4 12B, and Mistral NeMo 12B require 8.8 GB at Q4_K_M and use 10.8 GB of system RAM. Offloading allows you to run larger architectures like Vicuna 13B, which needs 9.5 GB at Q4_K_M and uses 11.5 GB of system RAM, but this process significantly reduces generation speed.

You must also consider the context window size when loading these models. The memory figures listed are calculated at a standard 4k context window. Running longer conversations or processing larger documents increases memory consumption. If you exceed the 4k context limit, the model may run out of video memory and force slower CPU processing.

For optimal local performance without offloading, several highly capable models fit comfortably. Llama 3.1 8B uses 7.4 GB of video memory at Q5_K_M quantization. Mistral 7B uses 7.4 GB at Q6_K quantization. Smaller options like Qwen2.5 7B and Falcon 3 7B require only 6.9 GB of video memory at Q6_K quantization, leaving safety headroom for system tasks.