A new breakthrough at Nvidia promises to dramatically speed up LLMs (‘Large Language Models’ such as OpenAI’s ChatGPT, Google Gemini, Anthropic’s Claude, Meta’s Llama etc.), making them not twice, nor thrice, but 53 times faster than they currently are!

Nvidia’s implementation of this new tech has resulted in a new family of language models they call Jet-Nemotron, which reportedly matches or beats the accuracy of big-name models like Qwen3, Qwen2.5, Gemma3, and Llama‑3.2 across many benchmark tests. Crucially, it offers up to 53.6× faster generation and 6.1× faster ‘prefill’ – meaning it starts “thinking” and generating responses much quicker. Essentially, the new model is capable of matching the present ones in terms of quality, whilst being much, much faster.

How does Jet-Nemotron work?

Properly understanding how this new ‘neural architecture exploration pipeline’ works will require a bit of an understanding of AI tech, but in layman’s terms, it is a way of modifying existing AI models in as efficient a way as possible, allowing them to be improved whilst retaining all of their accumulated knowledge from pre-training without extensive rewriting.

To use the correct terminiology, as explained by Jackson Atkins on Twitter/X, it ‘locks down core MLP layers’ then ‘uses a hardware-aware search to replace most of the slow, O(n²) full-attention layers with a new, hyper-efficient linear attention design called JetBlock’

What does this mean in practice?

• Greater cost efficiency: The vastly increased throughput speed means (according to Mr Atkins) an approximately 98% cost reduction for inference at scale – a huge saving which frees up capital for use elsewhere.
• Lower hardware requirements: The new tech means that similar results can be achieved with a much lower memory requirement (a 154MB cache would be sufficient), meaning a lower hardware barrier point for entry and also much more efficient use of existing hardware.
• Lower time & capital requirement for entrants into the market: Spending time and money to give new LLMs the necessary pretraining can cost a company that is looking to enter the AI marketplace millions; however, now they can much more easily innovate upon existing architecture and create something new at a much lower cost.

About the Author

Aaron Ritchie

Aaron's laptop knowledge makes him the go-to guy on PC Guide. But he still finds time for features, deals and much more.