Best local AI models for NVIDIA RTX 4080 Laptop

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 4080 Laptop GPU features 12 GB of GDDR6 memory. This dedicated video memory is the most important factor for running artificial intelligence models locally. To run a model completely on your graphics hardware, the model files and the active workspace must fit entirely within this 12 GB limit. If a model exceeds this capacity, your system must use slower memory options which reduces processing speed.

The quantization column indicates the level of compression applied to each model. Raw models are often too large for consumer hardware, so developers compress them into smaller formats. A Q4_K_M quantization represents a four bit format that balances file size and output quality. Higher quantizations like Q5_K_M, Q6_K, and Q8_0 provide better precision but require more memory. For example, the 14B parameter Phi-4 fits within 11.9 GB of memory using a Q5_K_M quantization.

For models that exceed your 12 GB video memory, you can use CPU offloading. This technique splits the workload between your graphics card and your system RAM. If your laptop has 32 GB of system RAM, you can run larger models like Codestral 22B. This model needs 16.1 GB of memory at Q4_K_M quantization, which requires 18.1 GB of system RAM to handle the overflow. CPU offloading allows you to run these larger models, but your generation speed will be slower.

Several high performance models fit directly into your local video memory. You can run DeepSeek-Coder-V2 16B or Kimi-VL A3B at Q4_K_M quantization using 11.7 GB of memory. The 14.7B parameter Qwen2.5 14B fits within 11.6 GB using Q4_K_M. For image and video generation, FLUX.1 schnell and FLUX.1 Krea dev fit within 11.8 GB using a Q6_K quantization. Smaller models like Gemma 2 9B fit easily within 10.3 GB using a Q6_K quantization.

When running these models, you must consider the context window size. The memory usage figures listed here are calculated using a standard 4k context window. If you increase the context window to process longer documents or larger chat histories, the memory requirements will grow. To prevent your system from running out of video memory, you may need to select a smaller model or use a more compressed quantization level.