In this video, the creator shares their first impressions and hands-on experience with the new Nvidia Neotron 3 Nano 30B A3B model, a hybrid mixture of experts model featuring 30 billion parameters with only 3 billion active at a time. This design allows for significantly faster performance, boasting four times the throughput of the previous Neotron version and 60% fewer reasoning tokens, making it highly efficient. One of the standout features is its massive 1 million token context window, which is quite remarkable for a model of this size. The model is fully open with accessible weights and shared data sources, and it is available on platforms like Hugging Face.
The creator demonstrates using the model through Nvidia’s API and integrates it with Open Code, an open-source platform similar to Cloud Code, to test its capabilities. They set the context window to 1 million tokens and an output limit of 40,000 tokens, highlighting the model’s speed and efficiency in generating responses. The video shows the creator experimenting with Python code to generate images from text prompts, saving the images locally, and testing the model’s ability to follow instructions and tool calls. Despite occasional minor errors, the model consistently picks the correct tools and executes tasks rapidly.
Next, the creator attempts to build a simple Streamlit user interface to interact with the model locally. After some debugging and fixing errors with the API client, they successfully run the app, allowing users to input prompts and generate images in real-time. This demonstrates the model’s practical usability in building applications and workflows that require fast and efficient text-to-image generation. The creator appreciates the model’s speed and responsiveness, which makes development and testing smooth and enjoyable.
A significant part of the video focuses on testing the model’s ability to handle complex multi-step tool calls. The creator sets up a task involving web searches, file writing, reading, and running Python scripts to fetch and graph Bitcoin prices using the Open Gecko API. Despite the complexity and multiple tool interactions, the model completes the task successfully, generating relevant files and graphs. Although some initial data issues arise, such as repeated dates, the model quickly adapts and corrects the output upon further prompts, showcasing its strong contextual understanding and tool integration.
In conclusion, the creator highly recommends trying out the Nvidia Neotron 3 Nano 30B model, especially for those interested in large context windows and fast throughput in a relatively small model. The open availability of the model and its weights, combined with its impressive performance in reasoning and tool usage, make it a compelling option for developers and researchers. The video encourages viewers to explore the model on Hugging Face and other platforms, highlighting the fun and efficiency experienced during testing. Overall, the Nvidia Neotron 3 Nano 30B is presented as a powerful and versatile model worth exploring.
