Best local AI models for NVIDIA RTX 4050 Laptop

The largest models that fit fully

The 30 largest of the 114 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 4050 Laptop graphics card features 6 GB GDDR6 of dedicated video memory. This memory size determines which artificial intelligence models can run entirely on your hardware. When a model fits completely within this video memory, it processes text and images at maximum speed. If a model exceeds this limit, your system must use slower system memory to handle the extra data.

The quantization column indicates the compression level applied to each model. We recommend specific levels like Q4_K_M or Q5_K_M to balance output quality and memory usage. For example, the Granite 3.3 8B model fits within 5.9 GB of video memory at a Q4_K_M quantization. Similarly, the Qwen2.5 7B model utilizes 6 GB of video memory at a Q5_K_M quantization.

Several high performance models fit directly into your video memory. You can run Mistral 7B or EXAONE 3.5 7.8B at Q4_K_M quantization using 5.7 GB of video memory. Other options like Falcon 3 7B and Janus-Pro 7B run at Q5_K_M quantization using exactly 6 GB of video memory. These configurations ensure that your graphics processor handles all computations without relying on system memory.

Running larger models requires CPU offloading which shares the workload with your system RAM. If you have 32 GB of system RAM, you can run models like Gemma 2 9B. This model requires 8 GB at Q4_K_M quantization and uses 10 GB of system RAM. Offloading allows you to run larger architectures like Mochi 1 10B or Open-Sora 2.0 11B, but your processing speed will decrease because system RAM is slower than video memory.

Memory calculations assume a standard 4k context window. Generating longer responses or processing large documents increases memory consumption. If you exceed the 4k context limit, the model will require more memory than the listed figures. Keep your prompts concise to maintain optimal performance within your hardware limits.