Best local AI models for NVIDIA RTX 4060 Laptop

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 Laptop graphics card features 8 GB GDDR6 of dedicated video memory. This memory pool determines which artificial intelligence models can run entirely on your local hardware. When a model fits completely within this video memory, it processes tokens at maximum speed. If a model exceeds this limit, you must offload parts of it to your system memory.

To fit larger models into the 8 GB limit, we use quantized versions. Quantization reduces the precision of model weights to save space. The best quantization level for each model balances size and output quality. For example, Gemma 2 9B fits at the Q4_K_M quantization using exactly 8 GB. Smaller models like Llama 3.1 8B fit at the Q5_K_M quantization using 7.4 GB. You can run Granite 3.3 8B or Mistral 7B at the higher quality Q6_K quantization using 7.9 GB and 7.4 GB respectively.

Many popular models fit comfortably within your video memory. You can run Mochi 1 10B at Q4_K_M using 7.3 GB. The Nemotron Nano 9B, GLM-4 9B, Yi-Coder 9B, GLM-4-9B-Chat, and GLM-4V-9B models all run at Q5_K_M using 7.7 GB. For 8B models like InternLM 3 8B, OpenCoder 8B, MiniCPM-V 2.6, and Stable Diffusion 3.5 Large, the Q6_K quantization uses 7.9 GB. Excellent 7B models like Qwen2.5 7B, Falcon 3 7B, and Command R7B use 6.9 GB at Q6_K.

If you want to run larger models, you must use CPU offloading. This process shares the workload between your graphics card and your system RAM. We assume your laptop has 32 GB of system RAM for these cases. Offloading allows you to run models that exceed 8 GB, but it reduces generation speed because system RAM is slower than video memory.

With CPU offloading, you can run Gemma 3 12B, Gemma 4 12B, Mistral NeMo 12B, and Pixtral 12B. These models need 8.8 GB at Q4_K_M and require 10.8 GB of system RAM. You can also run FLUX.1 dev which needs 14.4 GB at FP8 and requires 16.4 GB of system RAM. Vicuna 13B needs 9.5 GB at Q4_K_M and requires 11.5 GB of system RAM. Open-Sora 2.0 needs 8.1 GB at Q4_K_M and requires 10.1 GB of system RAM.

Keep in mind that memory usage calculations assume a standard 4k context window. If you increase the context window to process longer documents, the model will require more memory. This extra demand might force you to use a lower quantization level or offload more layers to your system RAM.