From miles away throughout the desert, the Nice Pyramid appears to be like like an ideal, easy geometry — a modern triangle pointing to the stars. Stand at the base, nonetheless, and the phantasm of smoothness vanishes. You see huge, jagged blocks of limestone. It is not a slope; it is a staircase.
Keep in mind this the subsequent time you hear futurists speaking about exponential progress.
Intel’s co-founder Gordon Moore (Moore’s Regulation) is famously quoted for saying in 1965 that the transistor depend on a microchip would double yearly. One other Intel govt, David Home, later revised this assertion to “compute energy doubling each 18 months.” For some time, Intel’s CPUs had been the poster youngster of this regulation. That is, till the progress in CPU efficiency flattened out like a block of limestone.
For those who zoom out, although, the subsequent limestone block was already there — the progress in compute merely shifted from CPUs to the world of GPUs. Jensen Huang, Nvidia’s CEO, performed an extended recreation and got here out a robust winner, constructing his personal stepping stones initially with gaming, then pc visioniand lately, generative AI.
The phantasm of easy progress
Expertise progress is stuffed with sprints and plateaus, and gen AI is not immune. The present wave is pushed by transformer structure. To cite Anthropic’s President and co-founder Dario Amodei: “The exponential continues till it doesn’t. And yearly we’ve been like, ‘Properly, this will’t probably be the case that issues will proceed on the exponential’ — after which yearly it has.”
However simply as the CPU plateaued and GPUs took the lead, we are seeing indicators that LLM progress is shifting paradigms once more. For instance, late in 2024, DeepSeek shocked the world by coaching a world-class mannequin on an impossibly small funds, partially through the use of the MoE method.
Do you keep in mind the place you lately noticed this method talked about? Nvidia’s Rubin press launch: The expertise contains “…the newest generations of Nvidia NVLink interconnect expertise… to speed up agentic AI, superior reasoning and massive-scale MoE mannequin inference at up to 10x decrease value per token.”
Jensen is aware of that reaching that coveted exponential progress in compute doesn’t come from pure brute power anymore. Generally you want to shift the structure totally to place the subsequent stepping stone.
The latency disaster: The place Groq matches in
This lengthy introduction brings us to Groq.
The largest beneficial properties in AI reasoning capabilities in 2025 had been pushed by “inference time compute” — or, in lay phrases, “letting the mannequin assume for an extended time period.” However time is cash. Shoppers and companies do not like ready.
Groq comes into play right here with its lightning-speed inference. For those who deliver collectively the architectural effectivity of fashions like DeepSeek and the sheer throughput of Groq, you get frontier intelligence at your fingertips. By executing inference quicker, you may “out-reason” aggressive fashions, providing a “smarter” system to prospects with out the penalty of lag.
From common chip to inference optimization
For the final decade, the GPU has been the common hammer for each AI nail. You utilize H100s to prepare the mannequin; you employ H100s (or trimmed-down variations) to run the mannequin. However as fashions shift towards “System 2” pondering — the place the AI causes, self-corrects and iterates before answering — the computational workload adjustments.
Coaching requires huge parallel brute power. Inference, particularly for reasoning fashions, requires quicker sequential processing. It should generate tokens immediately to facilitate advanced chains of thought with out the consumer ready minutes for a solution. Groq’s LPU (Language Processing Unit) structure removes the reminiscence bandwidth bottleneck that plagues GPUs throughout small-batch inference, delivering lightning-fast inference.
The engine for the subsequent wave of progress
For the C-Suite, this potential convergence solves the “pondering time” latency disaster. Think about the expectations from AI brokers: We wish them to autonomously guide flights, code whole apps and analysis authorized precedent. To do that reliably, a mannequin may want to generate 10,000 inside “thought tokens” to verify its personal work before it outputs a single phrase to the consumer.
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On an ordinary GPU: 10,000 thought tokens may take 20 to 40 seconds. The consumer will get bored and leaves.
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On Groq: That very same chain of thought occurs in lower than 2 seconds.
If Nvidia integrates Groq’s expertise, they clear up the “ready for the robotic to assume” downside. They protect the magic of AI. Simply as they moved from rendering pixels (gaming) to rendering intelligence (gen AI), they might now transfer to rendering reasoning in real-time.
Moreover, this creates a formidable software program moat. Groq’s greatest hurdle has all the time been the software program stack; Nvidia’s greatest asset is CUDA. If Nvidia wraps its ecosystem round Groq’s {hardware}, they successfully dig a moat so vast that rivals can not cross it. They might provide the common platform: The very best setting to prepare and the most effective setting to run (Groq/LPU).
Think about what occurs if you couple that uncooked inference energy with a next-generation open supply mannequin (like the rumored DeepSeek 4): You get an providing that will rival at present’s frontier fashions in value, efficiency and pace. That opens up alternatives for Nvidia, from immediately getting into the inference enterprise with its personal cloud providing, to persevering with to energy a rising variety of exponentially rising prospects.
The following step on the pyramid
Returning to our opening metaphor: The “exponential” progress of AI is not a easy line of uncooked FLOPs; it is a staircase of bottlenecks being smashed.
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Block 1: We could not calculate quick sufficient. Resolution: The GPU.
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Block 2: We could not prepare deep sufficient. Resolution: Transformer structure.
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Block 3: We will not “assume” quick sufficient. Resolution: Groq’s LPU.
Jensen Huang has by no means been afraid to cannibalize his personal product traces to personal the future. By validating Groq, Nvidia would not simply be shopping for a quicker chip; they might be bringing next-generation intelligence to the plenty.
Andrew Filev, founder and CEO of Zencoder
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