Google AI breakthrough shows why we don't need more data centers

Mashable ^ | March 27, 2026 | Chris Taylor

[Twotone]

We have seen the future of AI via Large Language Models. And it's smaller than you think.

That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center energy to do its job and performed surprisingly well on benchmark tests against heftier American AI models. (Ironically, it was built atop an open source U.S. model, Meta's Llama).

DeepSeek may have foundered on privacy concerns, but the trend towards smaller and smarter AI isn't going away. The evolution is on display again in TurboQuant, a compression algorithm that Google quietly unveiled this week via a Google Research paper.

The paper itself is pretty impenetrable if you're not an AI nerd who talks tokens and high-dimensional vectors. We'll get into a more detailed explanation below. But here's the TL;DR: The TurboQuant algorithm can make LLMs' memory usage six times smaller.

What does that mean? Less energy usage, perhaps to the point where running a powerful AI model on your powerful smartphone becomes possible. Less RAM usage, right on time for the ongoing RAM shortage.

Certainly, algorithms like this can help LLMs make more efficient use of the data centers they're hosted in — either by using the extra space to run more complex models, or, hear me out, by allowing us not to rush into building so many unpopular new data centers in the first place.

And that, paradoxically, could be a problem for the AI economy, at least as it's currently structured.

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TOPICS: Business/Economy; News/Current Events
KEYWORDS: ai; aitruth; datacenters; google; luddites

[WinMod70]

“They” could always build them as dual purpose facilities. Data Center in front and ICE Detention in the back.

[Frank Drebin]

The only thing predictable is unpredictability.

As Crichton once said, "The big problem that a person in the year 1900 would see was: Where will they get all the horses for the year 2000 and what will we do with all the horseshit?"

[ProtectOurFreedom]

Strongest impact: Long-context, high-throughput production serving (enterprise chat, agents, search). i.e., cat videos and other AI slop.

Weaker impact: Short-prompt, low-batch, or prefill-heavy workloads.

Overall industry effect: Helps bend the cost/energy curve downward and buys time against the AI infrastructure explosion, but won’t single-handedly shrink the number of data centers needed globally—demand is growing too fast.

Local/edge inference: Shifts workloads that once required cloud APIs to consumer or enterprise hardware (e.g., Mac Mini M4 Pro or single-node servers), amortizing hardware costs in months instead of ongoing cloud bills.

Big caveat—Jevons paradox: Cheaper/faster inference often increases overall AI usage (more agents, longer contexts, new applications). Historical precedent with storage and compute shows efficiency gains rarely reduce total demand—they enable growth. So while efficiency per task improves, absolute DC buildout and energy use across the industry may still rise.

Bottom Line and Timeline: TurboQuant is software-only (training-free, easy to integrate), so benefits could appear quickly once rolled into frameworks like vLLM, Hugging Face, or cloud inference stacks. Early community reproductions (e.g., on MLX) already show the memory and speed gains.

[Carry_Okie]

Where will they get all the horses for the year 2000 and what will we do with all the horseshit?"

We made it electronic, with plenty of horse's a$$es in the media dispensing bounteous horseshit.

[RoosterRedux]

Jevons Paradox.

More efficient AI inference doesn’t mean fewer data centers; it likely means the same or more infrastructure running far more inference at lower cost, expanding use and aggregate demand. The article’s own video streaming analogy proves the point–compression didn’t reduce internet infrastructure demand, it exploded it.

Nvidia doesn’t necessarily lose and may, in fact, grow significantly, developing more advanced and more efficient chips to meet the expanded demand this efficiency unlocks.

And major hyperscaler capex guidance hasn’t broadly softened at all–Google et al. are still spending aggressively. Efficiency gains at the model level tend to get absorbed by expanding demand at the system level, not replaced by restraint.

And as to the article’s suggestion that LLMs could run on your phone–efficiency gains don’t accumulate into smaller infrastructure requirements, they get consumed by the next generation of more capable models. The frontier keeps moving. Every time someone figures out how to run last year’s model cheaper, the labs use that headroom to build something more powerful, not to downsize.

The on-device dream is chasing a target that keeps moving away from it.

[FreedomPoster]

Good. I’m ready for an AI crash so that RAM and disk storage prices fall back to something reasonable.

[No.6]

This is very good also because regular people can run powerful LLMs on their local GPU (when not busy doing important things like rendering games) and have the goodness without being owned by techbros(tm).

I could use a “helper”; I will not be dependent on Google or Microsoft or xAI etc. (I am not a number, etc.)

[No.6]

> The on-device dream is chasing a target that keeps moving away from it.

That’s reminiscent of what people said about PCs and smartphones. Can’t touch the big mainframes, why bother, it’s just a toy.

[Frank Drebin]

Fair to say, along with the growing evidence that LLMs are reaching the end of growth and the models are plateauing.

[Ancesthntr]

“Every time someone figures out how to run last year’s model cheaper, the labs use that headroom to build something more powerful, not to downsize.”
——————
Yes, just look at how much space is taken up by software that people use every day in the workplace. The gains in efficiency in your basic office software have been taken advantage of to make the programs incredibly sophisticated (needlessly so, in my opinion). It’s Jevon’s Paradox - RAM and hard drive space are incredibly cheap versus what they were 10, 20 or 30 years ago, so there is insatiable demand for it. This is exactly what is going to occur in the AI world.

But there’s also something else driving this besides the amount of memory or processing capacity that is available: AI is looked upon as the final frontier, and whoever controls it will be making scientific discoveries and be able to effectively rule the world. This means that there is no hurdle of “does this make sense from a business point of view?” It is about sheer survival, and thus gains in efficiency are only looked upon as a faster way to get to the end point of beating everybody else to the brass ring.

[dfwgator]

Fact is, you could host a quantized LLM on your local machine, and it would pretty much accomplish most tasks people do in ChatGPT.

[proxy_user]

The real question is what happens when real artificial intelligence comes along, and LLM is replaced by something far more human-like. What happens to all that investment then?

[RoosterRedux]

That's not what I was trying to say.;-)

What I was trying to say is that as chips and AI platforms gain efficiency, frontier LLMs will be gaining complexity and need even more compute. So while phones will be able to provide more AI locally, the most advanced AI platforms will simultaneously demand more advanced compute and data center capacity.

[RoosterRedux]

That's a possibility, I guess. But they're still pretty clumsy and have a long way to go.

I use Claude for most of my work at the moment and use Grok to corroborate. I only started using Claude when it seemed to leap ahead of the rest with Claude Code, Claude in Excel, and Claude Cowork. The entire landscape will probably change again shortly.

As good as Claude is–and it is very good–it's still like an error-prone (but very smart and very well-educated) intern.

[cymbeline]

Massive amounts of data are collected and stored, more so as time goes on.

Increasing amounts of equipment to hold plow through that data will be needed. Smarts (software) is a tiny part.

The capacity needed is to store raw data and the indices necessary to access it quickly and relate various pieces to other pieces.

It’s all about bulk.

[LambSlave]

But most people with the tech savvy and desire to do this are not interested in a chatbot; they are doing technical work/coding or video content creation. The quantized LLM available on local machines aren’t up to these tasks; honestly even the cutting edge paid LLMs available via API aren’t either. I’m not even talking about the problem space where you need perfect accuracy and rule following- that is beyond the reach of LLMs; just getting LLMs to do the things they are good at I find on highly complex projects with lots of things that need to stay in context they fall short.

[bigbob]

Buy me a 5090 and I won’t use any datacenter time

[spintreebob]

One of the alleged features of AI is massive data.
Yet the data is mostly data that is not copyrighted because it has no value and data that is copyrighted but is so prevalent in society that the copyright ca be ignored.

Also It is known that big data often is incomplete. Ask a simple factual question that requires many answers and AI will only list some of the factual answers, the ones fed to it and not all.

The solution: We need an AI that determines what data should be fed to AI.

[stig]

TurboQuant is a KV cache optimization that will save the amount of memory you need to run the KV cache. It does not improve the memory needed to store your model. It's a runtime optimization in the code you put into things like llama.cpp, LMStudio, Ollama, etc. You still need tons of memory to fit a model into your AI box.

TurboQuant boosts your context by a hefty factor so if your current local model has a 100K context (the amount of information the model can retain at any given time) it should boost it to 500K-600K. Which is great but when in the computer industry have we ever not needed more and more memory, higher memory bus speeds? The push to AGI will require lots of high speed memory and tons of heavy hardware.

Apple's new M5 Mac Studio only comes with 256GB of memory because they can't get enough memory. Even buying a Mac mini to run OpenClaw, Claude Code or Codex with a minimally sized system has delivery times out to August 2026 (and we are just talking about 32GB here).

So I don't get these predictions we don't need large data centers. That would be like saying I will never need a PC with more than a 32bit processor back in the 90s. Well it's 2026 and my MacBook Pro is running 64 bits with their vector registers going to 128bits on chip.