New Nvidia technology provides instant answers to encyclopedic-length questions

Have a question that needs to process an encyclopedia-length dataset? Nvidia says its new technique can answer it instantly.

Built leveraging the company’s Blackwell processor’s capabilities, the new “Helix Parallelism” method allows AI agents to process millions of words — think encyclopedia-length — and support up to 32x more users at a time.

While this could dramatically improve how agents analyze voluminous amounts of text in real time, some note that, at least for enterprise applications, it may be overkill.

“Nvidia’s multi-million-token context window is an impressive engineering milestone, but for most companies, it’s a solution in search of a problem,” said Wyatt Mayham, CEO and cofounder at Northwest AI Consulting. “Yes, it tackles a real limitation in existing models like long-context reasoning and quadratic scaling, but there’s a gap between what’s technically possible and what’s actually useful.”

*Helix Parallelism helps fix LLMs’ big memory problem*

Large language models (LLMs) still struggle to stay focused in ultra-long contexts, experts point out.

“For a long time, LLMs were bottlenecked by limited context windows, forcing them to ‘forget’ earlier information in lengthy tasks or conversations,” said Justin St-Maurice, technical counselor at Info-Tech Research Group.

And due to this “lost in the middle” problem, models tend to use only 10% to 20% of their inputs effectively, Mayham added.

Nvidia researchers pointed out that two serious bottlenecks include key-value (KV) cache streaming and feed-forward network (FFN) weight loading. Essentially, when producing an output, the model must scan through past tokens stored in a cache, but this strains GPU memory bandwidth. The agent also must reload large FFN weights from memory when processing each new word, slowing processes down considerably.

Traditionally, to address this, developers have turned to model parallelism, a machine learning (ML) technique that distributes components of a large neural network across multiple devices (such as Nvidia GPUs) rather than just using one. But eventually, this can lead to even more memory problems.