Best local AI models for NVIDIA RTX 5050 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 5050 Laptop graphics card comes equipped with 8 GB of GDDR7 dedicated video memory. This memory size determines which artificial intelligence models you can run entirely on your graphics hardware. Keeping the model weights inside this fast video memory is crucial for achieving the highest generation speeds. If a model exceeds this limit, your system must use slower system memory to process the remaining data.

The quantization column indicates the compression level used to fit these models into your hardware. For example, Gemma 2 9B fits at the Q4_K_M quantization level which uses 8 GB of video memory. Other models like Llama 3.1 8B run at the Q5_K_M level using 7.4 GB of video memory. Many models such as Mistral 7B and Qwen2.5 7B can run at the higher quality Q6_K quantization level using 7.4 GB and 6.9 GB of video memory respectively.

When you run larger models, you must use CPU offloading to share the workload between your graphics card and your system memory. This approach assumes your laptop has 32 GB of system RAM. Offloading allows you to run models like Gemma 3 12B or Mistral NeMo 12B which need 8.8 GB of video memory and 10.8 GB of system RAM at the Q4_K_M quantization level. You can also run FLUX.1 dev which needs 14.4 GB of video memory and 16.4 GB of system RAM at the FP8 or optimized level.

CPU offloading comes with a significant performance cost. Sending data back and forth between the graphics card and the system RAM over the system bus is much slower than keeping everything inside the GDDR7 memory. While offloading allows you to run larger models like Vicuna 13B which needs 9.5 GB of video memory and 11.5 GB of system RAM at Q4_K_M, your generation speed will drop noticeably compared to running fully on the graphics card.

You must also consider the memory required for the context window. The memory figures listed for these models are calculated using a standard 4k context window. If you increase the context window to process longer documents or longer conversations, the model will require more memory. This extra memory usage might force you to use a lower quantization level or rely on CPU offloading to prevent running out of video memory during long sessions.