If Google’s AI researchers had a humorousness, they’d have known as TurboQuant, the brand new, ultra-efficient AI reminiscence compression algorithm introduced Tuesday, “Pied Piper” — or, at least that’s what the internet thinks.
The joke is a reference to the fictional startup Pied Piper that was the main focus of HBO’s “Silicon Valley” TV sequence that ran from 2014 to 2019.
The present adopted the startup’s founders as they navigated the tech ecosystem, going through challenges like competitors from bigger corporations, fundraising, know-how and product points, and even (a lot to our delight) wowing the judges at a fictional model of TechCrunch Disrupt.
Pied Piper’s breakthrough know-how on the TV present was a compression algorithm that tremendously decreased file sizes with near-lossless compression. Google Analysis’s new TurboQuant can also be about excessive compression with out high quality loss, however utilized to a core bottleneck in AI methods. Therefore, the comparisons.
Google Analysis described the technology as a novel approach to shrink AI’s working reminiscence with out impacting efficiency. The compression technique, which makes use of a type of vector quantization to clear cache bottlenecks in AI processing, would basically permit AI to recollect extra data whereas taking on much less house and sustaining accuracy, in keeping with the researchers.
They plan to current their findings on the ICLR 2026 convention subsequent month, together with the 2 strategies which can be making this compression attainable: the quantization technique PolarQuant and a coaching and optimization technique known as QJL.
Understanding the maths concerned right here is one thing researchers and pc scientists might be able to do, however the outcomes are thrilling the broader tech trade as a complete.
If efficiently applied in the true world, TurboQuant might make AI cheaper to run by lowering its runtime “working reminiscence” — generally known as the KV cache — by “a minimum of 6x.”
Some, like Cloudflare CEO Matthew Prince, are even calling this Google’s DeepSeek second — a reference to the effectivity positive aspects pushed by the Chinese language AI mannequin, which was skilled at a fraction of the price of its rivals on worse chips, whereas remaining aggressive on its outcomes.
Nonetheless, it’s price noting that TurboQuant hasn’t but been deployed broadly; it’s nonetheless a lab breakthrough at the moment.
That makes comparisons with one thing like DeepSeek, and even the fictional Pied Piper, harder. On TV, Pied Piper’s know-how was going to transform the principles of computing. TurboQuant, in the meantime, might result in effectivity positive aspects and methods that require much less reminiscence throughout inference. However it wouldn’t essentially clear up the broader RAM shortages pushed by AI, provided that it solely targets inference reminiscence, not coaching — the latter of which continues to require large quantities of RAM.
