From MIPS to exaflops in mere decades: Compute power is exploding, and it will transform AI

On the current Nvidia GTC convention, the corporate unveiled what it described as the primary single-rack system of servers able to one exaflop — one billion billion, or a quintillion, floating-point operations (FLOPS) per second. This breakthrough relies on the most recent GB200 NVL72 system, which includes Nvidia’s newest Blackwell graphics processing models (GPUs). A typical laptop rack is about 6 toes tall, just a little greater than 3 toes deep and fewer than 2 toes broad.

Shrinking an exaflop: From Frontier to Blackwell

A few issues in regards to the announcement struck me. First, the world’s first exaflop-capable laptop was put in just a few years in the past, in 2022, at Oak Ridge Nationwide Laboratory. For comparability, the “Frontier” supercomputer constructed by HPE and powered by AMD GPUs and CPUs, initially consisted of 74 racks of servers. The brand new Nvidia system has achieved roughly 73X better efficiency density in simply three years, equal to a tripling of efficiency yearly. This development displays outstanding progress in computing density, vitality effectivity and architectural design.

Secondly, it must be mentioned that whereas each techniques hit the exascale milestone, they’re constructed for various challenges, one optimized for pace, the opposite for precision. Nvidia’s exaflop specification relies on lower-precision math — particularly 4-bit and 8-bit floating-point operations — thought-about optimum for AI workloads together with duties like coaching and working massive language fashions (LLMs). These calculations prioritize pace over precision. In contrast, the exaflop score for Frontier was achieved utilizing 64-bit double-precision math, the gold customary for scientific simulations the place accuracy is crucial.

We’ve come a great distance (in a short time)

This degree of progress appears virtually unbelievable, particularly as I recall the state-of-the-art once I started my profession within the computing {industry}. My first skilled job was as a programmer on the DEC KL 1090. This machine, a part of DEC’s PDP-10 collection of timeshare mainframes, provided 1.8 million directions per second (MIPS). Other than its CPU efficiency, the machine linked to cathode ray tube (CRT) shows through hardwired cables. There have been no graphics capabilities, simply gentle textual content on a darkish background. And naturally, no Web. Distant customers linked over telephone traces utilizing modems working at speeds as much as 1,200 bits per second.

500 billion instances extra compute

Whereas evaluating MIPS to FLOPS provides a normal sense of progress, you will need to keep in mind that these metrics measure totally different computing workloads. MIPS displays integer processing pace, which is helpful for general-purpose computing, significantly in enterprise functions. FLOPS measures floating-point efficiency that’s essential for scientific workloads and the heavy number-crunching behind trendy AI, such because the matrix math and linear algebra used to coach and run machine studying (ML) fashions.

Whereas not a direct comparability, the sheer scale of the distinction between MIPS then and FLOPS now gives a strong illustration of the fast development in computing efficiency. Utilizing these as a tough heuristic to measure work carried out, the brand new Nvidia system is roughly 500 billion instances extra highly effective than the DEC machine. That sort of leap exemplifies the exponential development of computing energy over a single skilled profession and raises the query: If this a lot progress is feasible in 40 years, what would possibly the following 5 convey?

Nvidia, for its half, has provided some clues. At GTC, the corporate shared a roadmap predicting that its next-generation full-rack system based mostly on the “Vera Rubin” Extremely structure will ship 14X the efficiency of the Blackwell Extremely rack transport this yr, reaching someplace between 14 and 15 exaflops in AI-optimized work within the subsequent yr or two.

Simply as notable is the effectivity. Attaining this degree of efficiency in a single rack means much less bodily house per unit of labor, fewer supplies and probably decrease vitality use per operation, though absolutely the energy calls for of those techniques stay immense.

Does AI really want all that compute energy?

Whereas such efficiency positive aspects are certainly spectacular, the AI {industry} is now grappling with a elementary query: How a lot computing energy is actually obligatory and at what value? The race to construct huge new AI knowledge facilities is being pushed by the rising calls for of exascale computing and ever-more succesful AI fashions.

Essentially the most formidable effort is the $500 billion Undertaking Stargate, which envisions 20 knowledge facilities throughout the U.S., every spanning half 1,000,000 sq. toes. A wave of different hyperscale initiatives is both underway or in planning phases around the globe, as firms and international locations scramble to make sure they’ve the infrastructure to help the AI workloads of tomorrow.

Some analysts now fear that we could also be overbuilding AI knowledge heart capability. Concern intensified after the discharge of R1, a reasoning mannequin from China’s DeepSeek that requires considerably much less compute than lots of its friends. Microsoft later canceled leases with a number of knowledge heart suppliers, sparking hypothesis that it could be recalibrating its expectations for future AI infrastructure demand.

Nevertheless, The Register suggested that this pullback could have extra to do with a number of the deliberate AI knowledge facilities not having sufficiently sturdy means to help the ability and cooling wants of next-gen AI techniques. Already, AI fashions are pushing the bounds of what current infrastructure can help. MIT Know-how Evaluation reported that this can be the explanation many knowledge facilities in China are struggling and failing, having been constructed to specs that aren’t optimum for the current want, not to mention these of the following few years.

AI inference calls for extra FLOPs

Reasoning fashions carry out most of their work at runtime by means of a course of referred to as inference. These fashions energy a number of the most superior and resource-intensive functions at the moment, together with deep analysis assistants and the rising wave of agentic AI techniques.

Whereas DeepSeek-R1 initially spooked the {industry} into pondering that future AI would possibly require much less computing energy, Nvidia CEO Jensen Huang pushed again onerous. Speaking to CNBC, he countered this notion: “It was the precise reverse conclusion that everyone had.” He added that reasoning AI consumes 100X extra computing than non-reasoning AI.

As AI continues to evolve from reasoning fashions to autonomous brokers and past, demand for computing is prone to surge as soon as once more. The following breakthroughs could come not simply in language or imaginative and prescient, however in AI agent coordination, fusion simulations and even large-scale digital twins, every made attainable by the sort of computing means leap we’ve got simply witnessed.

Seemingly proper on cue, OpenAI simply introduced $40 billion in new funding, the biggest non-public tech funding spherical on document. The corporate mentioned in a blog post that the funding “allows us to push the frontiers of AI analysis even additional, scale our compute infrastructure and ship more and more highly effective instruments for the five hundred million individuals who use ChatGPT each week.”

Why is a lot capital flowing into AI? The explanations vary from competitiveness to nationwide safety. Though one specific issue stands out, as exemplified by a McKinsey headline: “AI may enhance company earnings by $4.4 trillion a yr.”

What comes subsequent? It’s anyone’s guess

At their core, info techniques are about abstracting complexity, whether or not by means of an emergency car routing system I as soon as wrote in Fortran, a pupil achievement reporting software in-built COBOL, or trendy AI techniques accelerating drug discovery. The objective has at all times been the identical: To make better sense of the world.

Now, with highly effective AI starting to look, we’re crossing a threshold. For the primary time, we could have the computing energy and the intelligence to deal with issues that have been as soon as past human attain.

New York Occasions columnist Kevin Roose recently captured this moment well: “Each week, I meet engineers and entrepreneurs engaged on AI who inform me that change — massive change, world-shaking change, the sort of transformation we’ve by no means seen earlier than — is simply across the nook.” And that doesn’t even rely the breakthroughs that arrive every week.

Simply prior to now few days, we’ve seen OpenAI’s GPT-4o generate nearly perfect images from textual content, Google launch what could be the most superior reasoning mannequin but in Gemini 2.5 Professional and Runway unveil a video mannequin with shot-to-shot character and scene consistency, one thing VentureBeat notes has eluded most AI video turbines till now.

What comes subsequent is actually a guess. We have no idea whether or not highly effective AI will likely be a breakthrough or breakdown, whether or not it’ll assist clear up fusion vitality or unleash new organic dangers. However with ever extra FLOPS coming on-line over the following 5 years, one factor appears sure: Innovation will come quick — and with pressure. It’s clear, too, that as FLOPS scale, so should our conversations about duty, regulation and restraint.

Gary Grossman is EVP of know-how follow at Edelman and world lead of the Edelman AI Middle of Excellence.