NVIDIA’s GTC in Four Headlines

Impressive AI hardware innovations and interesting software moves.

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Edge 381: We start a new series about autonomous agents! We introdice the main concepts in agents and review the AGENTS framework from ETH Zurich. Additionally, we provide an overview of BabyAGI.
Edge 382: We review PromptBreeder, Google Deemind’s self-improving prompt technique.

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📝 Editorial: NVIDIA’s GTC in Four Headlines

I tried to resist making this weekend’s editorial about NVIDIA because I think you might have been inundated with headlines from the GTC conference. Unable to resist, I instead decided to present the four most impactful announcements in terms of ambition and market impact. If I have to summarize two key takeaways from NVIDIA’s AI announcements this week, they would be these:

NVIDIA is not only outgrowing but also out-innovating everyone else in AI compute hardware by a large margin.
NVIDIA’s software ambitions should be taken seriously.

To put that in context, here are four key announcements from this week’s GTC:

Blackwell GPU Series: NVIDIA unveiled the Blackwell B200 GPU, optimized for trillion-parameter models. The chip can improve LLM inference by up to 30x, which is quite remarkable.
NIM Microservices: My favorite announcement of GTC was the NIM platform, which delivers models optimized for inference and packaged as containers. NIM speeds up inference by using the Triton Inference Server, TensorRT, and TensorRT-LLM.
Project GR00T: I think the coolest and most ambitious announcement was Project GR00T, which focuses on developing foundation models for humanoid robots. The stack is based on multimodal models for video, audio, and language.
Distribution: An overlooked announcement during GTC was the impressive list of strategic alliances with top software companies like Microsoft, Google, Amazon, Dell, Oracle, and many others. NVIDIA is simply everywhere.

There were many additional announcements at GTC, but the aforementioned four are incredibly impactful. NVIDIA’s AI hardware dominance is unquestionable, but it’s quickly making inroads in the software space.

🔎 ML Research

Chronos

Amazon Science published a paper introducing Chronos, a family of pretrained foundation models for time series forecasting. Chronos models time-series data using the same techniques used by LLMs —> Read more.

Moirai

Salesforce Research also got into time series forecasting with foundation models with the publication of a paper detailing Moirai. The new model follows the paradigm of a universal forecasting model that can issues predictions across many domains and time scales —> Read more.

TacticAI

Google DeepMind published a paper detailing TacticAI, a model that can provide technical insights in football( soccer) plays, particularly corner kicks. The model uses geometric deep learning by generating possible reflections of a game situation including the player’s relationships —> Read more.

RAFT

Researcers from UC Berkely published a paper introducing retrieval-augmented fine-tuning(RAFT), a training technique that improves the ability of models to answer questions based on external data. Given a question, RAFT ignores the documents that are not relevant to formulagte the answer leading to more accurate outputs —> Read more.

Evolutionary Optimization and FM Model Merging

Researchers from Sakana AI published a paper that uses evolutionary optimization to merge foundation models. The technique attempts to harness the collective intelligence of different models to create more powerful foundation models —> Read more.

SceneScript

Meta AI Research published a paper detailing SceneScript, a method for reconstructing layouts of physical spaces. The technique can have profound implications in augmented reality scenarios that merge physical and virtual spaces —> Read more.