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Showing posts with label groq. Show all posts
Showing posts with label groq. Show all posts

Thursday, December 25, 2025

Groq, the LPU, and NVIDIA’s $20 Billion Power Move: The Inference War Reaches Its Turning Point


Groq, the LPU, and NVIDIA’s $20 Billion Power Move: The Inference War Reaches Its Turning Point

In the long arc of computing history, revolutions rarely arrive with fanfare. They sneak in sideways—through bottlenecks, edge cases, and “unimportant” optimizations that suddenly become existential. In artificial intelligence, that moment has arrived. Not in training, where GPUs reign supreme, but in inference—the act of turning trained intelligence into real-time action.

At the center of this shift stood Groq, a quiet Silicon Valley startup founded in 2016. And on December 24–25, 2025, NVIDIA effectively declared the inference war too important to leave to chance, announcing its largest acquisition ever: a $20 billion all-cash deal to acquire Groq’s assets, intellectual property, and key talent.

This was not just a buyout. It was a preemptive strike.

Groq Is Not Grok (and That Distinction Matters)

First, a necessary clarification in an era of confusing brand echoes: Groq has nothing to do with Grok, the large language models developed by Elon Musk’s xAI. Groq is hardware. Grok is software. One moves electrons; the other moves words.

Groq was founded in Mountain View, California, by Jonathan Ross, a former Google engineer who helped create Google’s Tensor Processing Unit (TPU), along with fellow ex-Googler Douglas Wightman. Their ambition was audacious: challenge GPU dominance not by being more flexible, but by being ruthlessly specific.

If GPUs are Swiss Army knives, Groq wanted to build a scalpel.

The Big Idea: Inference Is Not Training

For over a decade, AI hardware progress was driven by training—throwing massive parallel compute at giant datasets to produce ever-larger models. GPUs thrived here. But once models are trained, the economics flip.

Inference is where AI meets reality:

  • Chatbots responding in milliseconds

  • Voice assistants that cannot hesitate

  • Autonomous systems where latency equals danger

  • Financial systems where predictability beats peak throughput

In this world, variability is poison.

Groq bet that deterministic, ultra-low-latency inference would matter more than raw parallel horsepower. And they designed a chip around that belief.

The Language Processing Unit: A Different Philosophy of Compute

Groq’s Language Processing Unit (LPU) is not a faster GPU. It is a rejection of the GPU paradigm.

Determinism Over Chaos

GPUs rely on dynamic scheduling, caches, and complex memory hierarchies. That flexibility is powerful—but it introduces unpredictability. Two identical inference requests can take different amounts of time.

The LPU eliminates this uncertainty entirely.

Groq’s architecture is built around a compiler-driven, statically scheduled model. Every operation is planned in advance. Every data movement is known. Every cycle is accounted for.

The result:
The same input produces the same output in the same amount of time—every time.

In a world of real-time AI, that predictability is priceless.

Inside the LPU: How It Works

At the heart of the LPU is Groq’s Tensor Streaming Processor (TSP) architecture—a radical departure from CPU and GPU design.

Key Architectural Pillars

1. SRAM-Centric Design
Instead of relying on high-bandwidth memory (HBM), the LPU uses massive on-chip SRAM—about 230 MB per chip, delivering ~80 TB/s of bandwidth. Data stays close to compute, slashing latency and power draw.

2. Streaming Dataflow
Data moves through the chip like water through a canal system—steady, predictable, uninterrupted. No stalls. No cache misses. No surprises.

3. Tensor Parallelism
Operations are sliced and distributed across processing elements optimized for tensor math, enabling efficient handling of modern architectures like Mixture of Experts (MoE).

4. Static Scheduling via Compiler
Groq’s proprietary compiler maps trained models (from PyTorch, ONNX, etc.) directly onto hardware, determining every instruction’s timing and data path before execution begins.

5. TruePoint Numerics
A custom numeric format balances precision and performance, avoiding the overhead of full floating-point arithmetic while maintaining accuracy.

Multiple LPUs can be clustered into racks—such as GroqRack, delivering petaflop-scale performance with millisecond-level latency.

Performance: Why NVIDIA Took Notice

Groq’s claims were not subtle—and benchmarks backed them up.

  • 1,000+ tokens per second for large language models

  • 3–10× faster inference than GPUs like NVIDIA A100 and H100

  • Milliseconds of latency, even at scale

  • 3× better energy efficiency, and up to 5× lower inference costs

In LLMPerf and other inference benchmarks, LPUs consistently topped the charts.

Groq didn’t just outperform GPUs. It made them look like overkill.

GPUs, TPUs, and LPUs: Three Different Futures

  • GPUs remain unmatched for training and general-purpose acceleration—but suffer from inference inefficiency and variability.

  • TPUs (Google’s domain) balance training and inference well, especially at cloud scale, but rely heavily on HBM and are ecosystem-locked.

  • LPUs are pure inference weapons—narrow, fast, predictable, and devastatingly efficient.

If GPUs are freight trains and TPUs are high-speed rail, LPUs are fighter jets: expensive, specialized, and unbeatable in their airspace.

The Acquisition: NVIDIA’s Instagram Moment

NVIDIA’s $20 billion move is best understood not as a WhatsApp-style adjacency expansion, but as an Instagram-style neutralization of a rising threat.

Groq was not shopping itself. But it was becoming too successful, too visible, and too dangerous—especially as AI demand shifted from training to inference.

Deal Structure (and Why It Matters)

  • Acquisition of Groq’s core assets, IP, and patents

  • Non-exclusive licensing, not a full company takeover

  • Acqui-hire of key executives, reportedly including Jonathan Ross

  • Groq continues operating independently under new leadership

  • GroqCloud remains active—for now

This hybrid structure mirrors recent Big Tech maneuvers (Microsoft–Inflection, Meta–Scale AI), designed to:

  • Accelerate integration

  • Reduce antitrust exposure

  • Neutralize competition quietly

It is corporate judo.

Why NVIDIA Needed Groq

NVIDIA dominates training—but inference is becoming the real money.

As AI scales:

  • Training happens once

  • Inference happens billions of times

Groq’s LPU solves three looming problems for NVIDIA:

  1. Inference efficiency as costs and energy constraints tighten

  2. HBM shortages, which threaten GPU scaling

  3. Rising competitors like AMD, Cerebras, and custom ASIC startups

By absorbing Groq’s technology, NVIDIA fills its most dangerous gap.

Industry-Wide Consequences: The Inference Era Begins

The Good

  • Faster, cheaper inference

  • Real-time AI becomes ubiquitous

  • More applications become economically viable

The Bad

  • Hardware consolidation accelerates

  • Barriers to entry rise for startups

  • NVIDIA’s market share (already ~80–90%) hardens further

The Uncomfortable

  • Regulatory scrutiny intensifies in the U.S., EU, and China

  • AI hardware becomes geopolitically strategic

  • Innovation risks being centralized

The inference revolution may democratize AI usage—but not AI ownership.

Final Thought: A Scalpel Enters the Empire

Groq set out to build the fastest inference engine in the world. It succeeded—so completely that the reigning emperor of AI hardware decided it was safer to own the blade than to fight it.

This deal marks a turning point. AI is no longer about who can train the biggest model. It’s about who can respond the fastest, the cheapest, and the most predictably.

The age of brute-force intelligence is giving way to the age of precision.

And NVIDIA, once again, has placed itself at the center of history—this time by recognizing that sometimes, the smallest, sharpest tool matters more than the biggest hammer.



ग्रोक, एलपीयू और NVIDIA की 20 अरब डॉलर की चाल: इन्फ़रेंस युद्ध का निर्णायक मोड़

कंप्यूटिंग के इतिहास में क्रांतियाँ अक्सर शोर मचाकर नहीं आतीं। वे चुपचाप प्रवेश करती हैं—बॉटलनेक, किनारे के उपयोग-मामलों और “गैर-महत्वपूर्ण” लगने वाले अनुकूलनों के रास्ते—और अचानक अस्तित्व का प्रश्न बन जाती हैं। कृत्रिम बुद्धिमत्ता (AI) में वही क्षण अब आ चुका है।
यह क्षण ट्रेनिंग में नहीं है, जहाँ GPU अब भी राज करते हैं, बल्कि इन्फ़रेंस में है—यानी प्रशिक्षित बुद्धिमत्ता को वास्तविक समय में काम में लगाने की प्रक्रिया में।

इस बदलाव के केंद्र में था Groq, 2016 में स्थापित एक अपेक्षाकृत शांत सिलिकॉन वैली स्टार्टअप। और 24–25 दिसंबर 2025 को NVIDIA ने स्पष्ट कर दिया कि इन्फ़रेंस युद्ध को वह संयोग पर नहीं छोड़ने वाला। कंपनी ने अपनी अब तक की सबसे बड़ी डील की घोषणा की—20 अरब डॉलर की ऑल-कैश डील, जिसके तहत उसने Groq की तकनीक, बौद्धिक संपदा और प्रमुख प्रतिभाओं का अधिग्रहण किया।

यह सिर्फ़ अधिग्रहण नहीं था।
यह एक पूर्व-प्रहार (preemptive strike) था।

Groq, Grok नहीं है (और यह फर्क बहुत मायने रखता है)

आज के भ्रमित करने वाले ब्रांड नामों के दौर में एक बात साफ़ करना ज़रूरी है:
Groq का Grok से कोई संबंध नहीं है।

  • Groq → हार्डवेयर कंपनी

  • Grok → xAI द्वारा विकसित बड़े भाषा मॉडल (LLMs)

Groq शब्द नहीं चलाता, वह इलेक्ट्रॉनों को गति देता है।

Groq की स्थापना माउंटेन व्यू, कैलिफ़ोर्निया में जोनाथन रॉस ने की थी—जो पहले Google में इंजीनियर थे और Google के Tensor Processing Unit (TPU) के निर्माण में शामिल रहे। उनके साथ अन्य पूर्व-Google इंजीनियर भी थे, जिनमें डगलस वाइटमैन प्रमुख हैं।

उनका लक्ष्य साहसी था:
GPU को ज़्यादा लचीला बनाकर नहीं, बल्कि बेहद विशिष्ट बनकर चुनौती देना।

अगर GPU स्विस आर्मी नाइफ है, तो Groq एक सर्जिकल स्कैल्पेल बनाना चाहता था।

मूल विचार: इन्फ़रेंस, ट्रेनिंग नहीं है

पिछले एक दशक तक AI हार्डवेयर की प्रगति का केंद्र ट्रेनिंग रही—भारी पैमाने पर समानांतर कंप्यूटिंग, विशाल डेटा सेट और विशाल मॉडल।

लेकिन एक बार मॉडल प्रशिक्षित हो जाने के बाद, अर्थशास्त्र बदल जाता है।

इन्फ़रेंस वह जगह है जहाँ AI वास्तविक दुनिया से टकराता है:

  • चैटबॉट्स जिन्हें मिलीसेकंड में जवाब देना होता है

  • वॉयस असिस्टेंट जिन्हें हिचकिचाना नहीं चाहिए

  • स्वायत्त प्रणालियाँ जहाँ विलंब जानलेवा हो सकता है

  • वित्तीय प्रणालियाँ जहाँ अनुमानित समय, अधिकतम शक्ति से ज़्यादा मायने रखता है

इस दुनिया में अनिश्चितता ज़हर है।

Groq ने दांव लगाया कि निर्धारित (deterministic), अल्ट्रा-लो-लेटेंसी इन्फ़रेंस ही भविष्य होगा—और उसने उसी विश्वास के चारों ओर चिप डिज़ाइन की।

Language Processing Unit (LPU): कंप्यूटिंग का नया दर्शन

Groq की Language Processing Unit (LPU) कोई तेज़ GPU नहीं है।
यह GPU मॉडल का सीधा इनकार है।

अराजकता के बजाय निर्धारण (Determinism)

GPU डायनामिक शेड्यूलिंग, कैश और जटिल मेमोरी पदानुक्रम पर निर्भर करते हैं। यह लचीलापन शक्तिशाली है—लेकिन इससे अनिश्चितता आती है।

एक ही इन्फ़रेंस अनुरोध दो बार अलग-अलग समय ले सकता है।

LPU इस अनिश्चितता को पूरी तरह समाप्त कर देता है।

Groq की वास्तुकला कंपाइलर-ड्रिवन, स्टैटिक शेड्यूलिंग पर आधारित है। हर ऑपरेशन पहले से तय होता है। हर डेटा मूवमेंट ज्ञात होता है। हर साइकिल गिनी जाती है।

परिणाम:
एक ही इनपुट, हर बार बिल्कुल एक ही समय में आउटपुट देता है।

रीयल-टाइम AI में यह पूर्वानुमेयता सोने से भी ज़्यादा कीमती है।

LPU के भीतर: यह कैसे काम करता है

LPU के केंद्र में है Groq की Tensor Streaming Processor (TSP) वास्तुकला—जो CPU और GPU दोनों से बुनियादी रूप से अलग है।

मुख्य वास्तुकला स्तंभ

1. SRAM-केंद्रित डिज़ाइन
HBM पर निर्भरता के बजाय, LPU भारी मात्रा में ऑन-चिप SRAM (लगभग 230 MB प्रति चिप) का उपयोग करता है, जिससे लगभग 80 TB/s बैंडविड्थ मिलती है।
डेटा कंप्यूट के पास रहता है—लेटेंसी और ऊर्जा खपत दोनों घटती हैं।

2. स्ट्रीमिंग डेटा-फ्लो
डेटा चिप के भीतर ऐसे बहता है जैसे नहर में पानी—निरंतर, अनुमानित, बिना रुकावट।
कोई कैश मिस नहीं, कोई स्टॉल नहीं।

3. टेन्सर पैरेललिज़्म
AI मॉडल के टेन्सर ऑपरेशंस को कुशलता से वितरित किया जाता है, जिससे Mixture of Experts (MoE) जैसे आधुनिक मॉडल संभाले जा सकें।

4. कंपाइलर-आधारित स्टैटिक शेड्यूलिंग
Groq का मालिकाना कंपाइलर प्रशिक्षित मॉडल (PyTorch, ONNX आदि) को सीधे हार्डवेयर पर मैप करता है।

5. TruePoint Numerics
एक कस्टम न्यूमेरिकल फ़ॉर्मेट, जो सटीकता और प्रदर्शन के बीच संतुलन बनाता है।

कई LPUs को जोड़कर क्लस्टर बनाए जा सकते हैं—जैसे GroqRack, जो मिलीसेकंड-स्तरीय लेटेंसी के साथ पेटाफ्लॉप-स्तरीय प्रदर्शन देता है।

प्रदर्शन: NVIDIA ने क्यों ध्यान दिया

Groq के दावे बड़े थे—और बेंचमार्क्स ने उन्हें सही ठहराया।

  • 1,000+ टोकन प्रति सेकंड (LLMs के लिए)

  • NVIDIA A100/H100 से 3–10 गुना तेज़ इन्फ़रेंस

  • मिलीसेकंड-स्तरीय लेटेंसी

  • 3 गुना अधिक ऊर्जा दक्षता और 5 गुना कम लागत

Groq ने GPU को केवल पछाड़ा नहीं—कई मामलों में उन्हें अत्यधिक भारी साबित कर दिया।

GPU, TPU और LPU: तीन अलग भविष्य

  • GPU → ट्रेनिंग और बहुउद्देश्यीय कार्यों में अपराजेय

  • TPU → ट्रेनिंग + इन्फ़रेंस का संतुलन, क्लाउड-केंद्रित

  • LPU → शुद्ध इन्फ़रेंस हथियार: तेज़, अनुमानित, कुशल

अगर GPU मालगाड़ी हैं और TPU हाई-स्पीड रेल, तो LPU फाइटर जेट है—विशेषीकृत और अपने क्षेत्र में अजेय।

अधिग्रहण: NVIDIA का “Instagram मोमेंट”

यह सौदा WhatsApp-जैसा विस्तार नहीं, बल्कि Instagram-जैसी प्रतिस्पर्धी निष्प्रभावीकरण रणनीति है।

Groq बिक्री के लिए नहीं था।
लेकिन वह बहुत तेज़, बहुत सफल और बहुत ख़तरनाक हो रहा था—ख़ासकर तब, जब बाज़ार ट्रेनिंग से इन्फ़रेंस की ओर झुक रहा था।

डील की संरचना

  • Groq की तकनीक, IP और पेटेंट का अधिग्रहण

  • नॉन-एक्सक्लूसिव लाइसेंसिंग

  • प्रमुख अधिकारियों का acqui-hire, संभवतः जोनाथन रॉस सहित

  • Groq स्वतंत्र रूप से संचालन जारी रखेगा

  • GroqCloud फिलहाल प्रभावित नहीं

यह संरचना:

  • तेज़ एकीकरण

  • कम एंटी-ट्रस्ट जोखिम

  • प्रतिस्पर्धा का शांत अंत

का रास्ता खोलती है।

NVIDIA को Groq की ज़रूरत क्यों थी

NVIDIA ट्रेनिंग में राजा है—लेकिन भविष्य का पैसा इन्फ़रेंस में है।

जैसे-जैसे AI फैलता है:

  • ट्रेनिंग एक बार होती है

  • इन्फ़रेंस अरबों बार

Groq तीन बड़ी समस्याएँ हल करता है:

  1. इन्फ़रेंस लागत और ऊर्जा संकट

  2. HBM की कमी

  3. AMD, Cerebras जैसे प्रतिस्पर्धियों का उभार

Groq को अपनाकर NVIDIA ने अपना सबसे ख़तरनाक अंतर भर लिया।

व्यापक प्रभाव: इन्फ़रेंस युग की शुरुआत

सकारात्मक

  • तेज़ और सस्ता AI

  • रीयल-टाइम एप्लिकेशन का विस्फोट

नकारात्मक

  • हार्डवेयर एकाधिकार बढ़ेगा

  • स्टार्टअप्स के लिए बाधाएँ ऊँची होंगी

असहज सच्चाई

  • नियामकीय दबाव बढ़ेगा

  • AI हार्डवेयर भू-राजनीतिक हथियार बनेगा

अंतिम विचार: साम्राज्य में एक स्कैल्पेल

Groq ने दुनिया का सबसे तेज़ इन्फ़रेंस इंजन बनाने का लक्ष्य रखा—और इतना सफल हुआ कि AI हार्डवेयर के सम्राट ने उसे ख़रीद लेना ही सुरक्षित समझा।

यह सौदा संकेत है कि AI अब सिर्फ़ “सबसे बड़ा मॉडल कौन ट्रेन करता है” की कहानी नहीं है।
अब सवाल है:

कौन सबसे तेज़, सबसे सस्ता और सबसे भरोसेमंद जवाब देता है।

क्रूर शक्ति का युग समाप्त हो रहा है।
सटीकता का युग शुरू हो चुका है।

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Tuesday, June 24, 2025

The Next Big Shift in AI: Why It’s Not About Nvidia

 

The Next Big Shift in AI: Why It’s Not About Nvidia, According to Groq’s Founder

The artificial intelligence (AI) revolution has been synonymous with Nvidia for years. Its graphics processing units (GPUs) have powered the training of massive AI models, cementing Nvidia’s dominance with an 80% share of the high-end chip market and a market cap soaring past $3 trillion. But Jonathan Ross, founder and CEO of Groq, argues that the future of AI isn’t about Nvidia—or GPUs at all. In a recent Yahoo Finance Opening Bid podcast, Ross outlined why the next big shift in AI will be driven by inference, efficiency, and innovation beyond Nvidia’s ecosystem. Here’s why his vision matters and what it means for the AI landscape.
A New Era: From Training to Inference
AI development has two core phases: training, where models learn from vast datasets, and inference, where trained models generate responses or make predictions in real-world applications. Nvidia’s GPUs excel at training, crunching through enormous datasets with brute computational force. However, Ross believes the future lies in inference, as AI models are increasingly deployed in real-time applications like chatbots, medical diagnostics, and autonomous systems.
Groq’s Language Processing Units (LPUs) are designed specifically for inference, offering speed, affordability, and energy efficiency that GPUs can’t match for this purpose. Unlike Nvidia’s chips, which were originally built for graphics and later adapted for AI, LPUs are purpose-built for running large language models (LLMs). Ross claims Groq’s LPUs are up to four times faster, five times cheaper, and three times more energy-efficient than Nvidia’s GPUs for inference tasks. This focus on inference taps into a growing market—estimated at $39 billion in 2024 and projected to reach $60.7 billion by 2028—where speed and cost are critical.
The Bottleneck of GPUs
Nvidia’s GPUs, while powerful, face limitations in inference. Their architecture requires trade-offs between throughput (processing multiple tokens) and interactivity (delivering fast responses to individual users). Ross points out that Nvidia’s latest Blackwell chip, though a leap forward, still maxes out at 50 tokens per second for interactive tasks—too slow for next-gen real-time AI solutions like conversational agents or digital twins. Groq’s LPUs, by contrast, eliminate this trade-off, optimizing both throughput and interactivity through a unique “assembly line” architecture that partitions models across multiple chips for lightning-fast processing.
Moreover, Nvidia’s GPUs are power-hungry, with some models targeting 1,000 watts per chip. This raises sustainability concerns, as data centers consume vast amounts of electricity and water. Groq’s LPUs, built with on-chip SRAM (100x faster than GPU’s HBM memory), deliver high performance with lower power consumption, making them a greener alternative. As businesses and governments prioritize energy efficiency, this could give Groq a competitive edge.
The Printing Press of AI
Ross likens today’s AI landscape to the “printing press era”—a nascent stage where the technology’s potential is just beginning to unfold. He predicts that LLMs will soon make so few mistakes that they’ll be reliable enough for high-stakes fields like medicine and law. More excitingly, he foresees AI models evolving from picking “probable” answers to inventing novel solutions, much like Albert Einstein’s breakthroughs in physics. This shift from replication to creation could unlock new drugs, scientific discoveries, and creative outputs—use cases that demand fast, efficient inference.
Groq’s LPUs are positioned to power this transition. By focusing on smaller models (up to 70 billion parameters) and achieving speeds of 500–750 tokens per second, Groq enables real-time applications that Nvidia’s GPUs struggle to support. For instance, a demo by HyperWrite’s CEO showcased Groq serving Mixtral at nearly 500 tokens per second, producing “pretty much instantaneous” responses. Such performance opens doors to use cases like real-time financial trading, autonomous vehicles, and healthcare diagnostics, where latency is a dealbreaker.
Challenging Nvidia’s Ecosystem
Nvidia’s dominance isn’t just about hardware; it’s about its CUDA software platform, which has locked developers into its ecosystem. Groq counters this by making its platform developer-friendly, offering OpenAI-compatible APIs that require just three lines of code to switch from other providers. Its GroqCloud platform, launched in 2024, hosts open-source LLMs like Meta’s Llama, and independent benchmarks by ArtificialAnalysis.ai confirm Groq’s superior speed for these models. Ross boldly predicted in 2024 that most startups would adopt Groq’s LPUs by year’s end, citing their cost and performance advantages.
However, challenging Nvidia is no small feat. Nvidia’s roadmap, accelerated by AI-driven chip design, keeps competitors on their toes. Critics argue that Groq’s LPUs, while fast for smaller models, may face scalability issues with trillion-parameter models. Additionally, widespread adoption requires convincing developers to optimize for a new architecture, a hurdle given Nvidia’s entrenched ecosystem. Ross acknowledges this, noting, “We’re nowhere near Nvidia yet,” but sees an opening as businesses seek alternatives to Nvidia’s high costs and supply constraints.
Groq’s Momentum
Groq’s traction is undeniable. Founded in 2016 by Ross, a former Google engineer who co-designed the Tensor Processing Unit (TPU), the company has raised over $1 billion, including a $640 million Series D round in August 2024 led by BlackRock, valuing it at $2.8 billion (now $3.5 billion per Yahoo Finance). Partnerships with Samsung for 4nm chip manufacturing, Carahsoft for government contracts, and Earth Wind & Power for European data centers signal ambitious scaling plans. Groq aims to deploy over 108,000 LPUs by Q1 2025, bolstered by recent hires like Meta’s Yann LeCun as a technical advisor and ex-Intel executive Stuart Pann as COO.
Ross’s vision extends beyond competing with Nvidia. His recent trip to Saudi Arabia with tech executives and President Donald Trump underscores Groq’s global ambitions, including a $1.5 billion funding deal with the Kingdom to expand infrastructure. This aligns with Groq’s mission to democratize AI compute, making it accessible to startups, enterprises, and governments—not just tech giants.
The Road Ahead
Can Groq dethrone Nvidia? Probably not anytime soon. Nvidia’s ecosystem, scale, and innovation pace are formidable. But Groq doesn’t need to topple Nvidia to succeed—it needs to carve out a niche in the inference market, where demand for speed, affordability, and sustainability is surging. As Ross puts it, “Compute is the new oil,” and Groq’s LPUs are poised to fuel a wave of AI applications that GPUs weren’t designed for.
The AI shift Ross envisions is about more than chips; it’s about enabling a future where AI invents, solves, and transforms. If Groq delivers on its promise, it could redefine how we interact with AI, making Nvidia’s dominance a chapter in the story, not the conclusion. For now, as Ross says, we’re at the beginning of AI’s printing press era—and Groq is writing its own page.
Sources:
  • Yahoo Finance, “Groq’s founder on why AI’s next big shift isn’t about Nvidia”
  • Yahoo Finance, “Nvidia rival Groq makes a bold prediction on what’s next for AI”
  • VentureBeat, “AI chip race: Groq CEO takes on Nvidia”
  • Forbes, “The AI Chip Boom Saved This Tiny Startup”
  • Groq, “What NVIDIA Didn’t Say”
  • Trajectory Ventures on X

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