The $20 Billion Move That Signals Nvidia’s Next Act
Nvidia just made its largest deal ever. On December 24, 2025, the company agreed to pay $20 billion to license Groq’s inference chip technology and hire its founder Jonathan Ross, the engineer who helped create Google’s TPU. The message is clear: Nvidia isn’t just defending its AI training dominance. It’s moving aggressively into inference, the next battleground where custom chips threaten GPU hegemony.
Current price: $187-189. Market cap: $4.6 trillion. Year-to-date return: +37%. The stock sits about 9% below its October all-time high of $212, correcting after a November earnings beat that somehow disappointed momentum traders. For long-term investors, the setup looks familiar: Nvidia consolidating before its next leg higher, backed by a $500 billion order backlog and the most important product cycle in semiconductor history.
NVDA Stock Snapshot
Why the Groq Deal Changes Everything
Groq’s Language Processing Units (LPUs) don’t compete with Nvidia’s GPUs directly. They use a completely different architecture: on-chip SRAM instead of external high-bandwidth memory, deterministic scheduling instead of probabilistic processing. The result is inference speeds up to 10x faster with one-tenth the energy consumption for certain workloads.
The catch? LPUs need hundreds of chips linked together to run large models that fit on just two or four Nvidia GPUs. That makes them specialized tools, not general-purpose replacements. But for real-time chatbots, AI agents, and latency-sensitive applications, Groq’s technology is genuinely superior.
Nvidia’s move accomplishes two strategic objectives simultaneously. First, it eliminates a potential competitor before hyperscalers like Microsoft or Meta could acquire Groq and build an alternative inference stack. Second, it gives Nvidia a new product category to offer customers who need ultra-low-latency inference but don’t want to leave the Nvidia ecosystem. Bank of America’s Vivek Arya envisions future Nvidia systems where GPUs and LPUs coexist in the same rack, connected via NVLink.
The $20 billion price tag raised eyebrows. Groq was valued at $6.9 billion just three months earlier. But as Bernstein analyst Stacy Rasgon noted, that’s “pocket change” for a company with $61 billion in cash and $4.6 trillion in market cap. It works out to about 82 cents per share.
The Blackwell Cycle: Why 2026 Could Be Nvidia’s Biggest Year Yet
Nvidia’s Blackwell architecture began shipping in Q4 2025, delivering 4x performance improvements over the H100/H200 generation. Every major hyperscaler has committed billions to Blackwell deployments through 2026. Microsoft, Amazon, Google, Meta, and Oracle are all racing to build AI infrastructure, and they’re all writing checks to Jensen Huang.
CEO Huang disclosed a combined $500 billion order book for Blackwell and the upcoming Rubin architecture through fiscal 2027. That’s roughly 3x Nvidia’s annual revenue run rate. The backlog signals sustained demand even as AMD’s MI300X gains traction and Google’s TPUv5 improves performance per dollar.
The CUDA software moat keeps tightening. December 2025’s CUDA 13.1 release marked the biggest update in 20 years, delivering up to 4x performance gains on Blackwell chips. Enterprises have invested years building CUDA-optimized codebases. Switching to AMD’s ROCm or Intel’s oneAPI means rewriting millions of lines of code. Few companies will make that investment when Nvidia keeps delivering generational performance leaps.
NVDA Live Chart
Key Trading Levels to Watch
NVDA’s technical structure tells a story of consolidation after a massive run. The stock peaked at $212.19 in late October 2025, pulled back 9-10% into December, and now trades in a range between $185-195. Here’s how to read the chart:
Resistance: $195 represents the immediate ceiling where sellers have emerged repeatedly since November. A clean break above $200 would signal the correction is over and the all-time high at $212 comes back into play. Above that, there’s no technical resistance, just blue sky.
Support: The $180-182 zone has held multiple tests. Below that, $165-170 represents the 200-day moving average area where institutional buyers would likely step in aggressively. The 52-week low at $86.62 feels like ancient history given fundamentals.
Volume patterns: Watch for accumulation days (price up on higher-than-average volume) versus distribution days (price down on high volume). Recent weeks show mixed signals, consistent with a market digesting November earnings and year-end portfolio rebalancing.
Technical Signal Dashboard
Q3 FY2026 Earnings: The Numbers Behind the Headlines
Nvidia reported Q3 fiscal 2026 results on November 19, 2025 that beat estimates yet somehow disappointed momentum traders. The numbers were objectively extraordinary:
Revenue: $57 billion total, up from $18.1 billion in the year-ago quarter. That’s 215% year-over-year growth at massive scale.
Data Center: $51.2 billion, representing 90% of total revenue and 66% growth year-over-year. This segment is Nvidia’s AI business, and it’s still accelerating.
Gross Margins: Mid-70% range, exceptional for a hardware company and reflecting Nvidia’s pricing power in a supply-constrained market.
Q4 Guidance: $65 billion revenue (±2%), implying continued sequential growth as Blackwell ramps.
Why did the stock sell off after these results? Expectations had gotten extreme. Some traders expected even higher guidance. Others worried about gross margin compression as Blackwell production scales. The selloff created a better entry point for patient investors focused on 2026 and beyond.
NVDA Fundamental Data
Company Profile
Bull Case: Why NVDA Could Hit $300
Wall Street’s average 12-month price target sits at $258, implying 37% upside from current levels. The bull case for even higher prices rests on several catalysts:
Blackwell demand exceeds supply through 2026. Every hyperscaler is racing to build AI capacity. Nvidia can’t manufacture chips fast enough. That means sustained pricing power and potential upside to revenue estimates.
Inference becomes a second growth engine. The Groq acquisition signals Nvidia’s ambition to dominate inference as thoroughly as it dominates training. If successful, the total addressable market expands significantly.
Sovereign AI spending accelerates. Governments worldwide are building national AI infrastructure. Saudi Arabia, UAE, Japan, France, and others represent billions in potential orders that don’t depend on U.S. tech company capex cycles.
The CUDA moat proves impenetrable. Every year that passes with developers building on CUDA makes switching costs higher. AMD and Intel keep promising competitive software stacks. Enterprises keep choosing Nvidia anyway.
Bear Case: The Risks Nobody Wants to Discuss
At $4.6 trillion market cap, Nvidia is priced for perfection. Here’s what could go wrong:
Export controls escalate. The U.S. government already restricts Nvidia’s most advanced chips from China. Further restrictions could eliminate billions in annual revenue. The H20 chip, designed specifically for Chinese customers, generates lower margins than flagship products.
AI spending hits a wall. Hyperscalers are spending $200+ billion annually on AI infrastructure. If enterprise customers can’t monetize their AI investments, that spending could contract rapidly. Nvidia’s valuation assumes the spending spree continues indefinitely.
Custom silicon gains traction. Amazon’s Trainium, Google’s TPUv5, Microsoft’s Maia, and Meta’s MTIA chips are all improving. These custom ASICs won’t replace Nvidia for general-purpose AI training, but they could capture significant inference workloads that would otherwise run on GPUs.
The multiple compresses. NVDA trades at roughly 47x trailing earnings and 35x forward estimates. If growth slows to “merely” 30-40% annually, the stock could de-rate significantly even as earnings grow.
NVDA News Feed
Trading Strategies for Different Timeframes
Long-term investors (1-3 years): The fundamental case for owning Nvidia remains compelling. AI infrastructure spending is still in early innings. Blackwell and Rubin architectures extend Nvidia’s technology lead. Consider building a position through dollar-cost averaging rather than timing entries perfectly.
Swing traders (weeks to months): The $180-212 range offers clear levels to trade against. Buy near support with stops below. Take profits near resistance. Earnings dates (next: late February 2026) create predictable volatility for options strategies.
Day traders: NVDA’s high volume and volatility make it ideal for intraday momentum trades. The stock often moves 2-4% daily. Watch for correlation with other Magnificent 7 names and broader market sentiment. Pre-market moves on news can set up the day’s direction.
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Frequently Asked Questions
What does Nvidia actually make?
Nvidia designs graphics processing units (GPUs) and the software that runs on them. Originally built for video games, GPUs turned out to be perfect for AI workloads that require massive parallel computation. Today, Nvidia’s data center GPUs power virtually every major AI model, from ChatGPT to Claude to Gemini.
Why is NVDA stock so expensive?
At $187 per share after a 10-for-1 stock split in June 2024, NVDA trades at about 47x trailing earnings. That’s expensive for a hardware company but arguably cheap for a company growing revenue 200%+ annually with 75% gross margins and a technological moat competitors can’t breach. The market is pricing in years of continued AI infrastructure spending.
What is the Groq acquisition about?
In December 2025, Nvidia agreed to pay $20 billion to license Groq’s inference chip technology and hire its leadership team. Groq makes LPUs (Language Processing Units), specialized chips that run AI models at extremely low latency. The deal expands Nvidia’s product portfolio beyond GPUs and eliminates a potential competitor in the inference market.
What is Blackwell?
Blackwell is Nvidia’s latest GPU architecture, shipping in Q4 2025. It delivers approximately 4x performance improvements over the previous H100/H200 generation. Major cloud providers have committed billions to Blackwell purchases through 2026. The architecture is named after mathematician David Blackwell.
Is AMD a real threat to Nvidia?
AMD’s MI300X GPU competes with Nvidia in data centers and has won some significant contracts. However, Nvidia’s CUDA software ecosystem creates switching costs that protect its market share. Most enterprises have built their AI infrastructure on CUDA and won’t rewrite their code for AMD’s ROCm platform unless forced to.
What are the biggest risks to NVDA stock?
Export restrictions on China sales, potential slowdown in hyperscaler AI spending, custom silicon competition from cloud providers (Google TPU, Amazon Trainium, Microsoft Maia), and valuation compression if growth rates moderate. At $4.6 trillion market cap, any disappointment gets punished severely.
When does Nvidia report earnings next?
Nvidia reports Q4 fiscal 2026 earnings in late February 2026. The company guided for $65 billion in revenue (±2%). Watch for Blackwell ramp commentary, gross margin trends, and any updates on the Groq integration.
How does Nvidia fit in the Magnificent 7?
Among the Magnificent 7 tech giants, Nvidia is unique: it’s the only pure-play AI infrastructure company. Apple, Microsoft, Google, Amazon, Meta, and Tesla all use AI. Nvidia enables it. The company sells billions in GPUs to its Magnificent 7 peers, making NVDA both competitor and critical supplier.
Track Nvidia with Context
Bookmark this page to monitor NVDA alongside BusinessTech.News coverage of AI breakthroughs, semiconductor policy, and data center buildouts. When Nvidia reports earnings, when competitors announce chips, when export rules change, watch the chart above to see how markets price the news in real-time.
Nvidia isn’t just a stock. It’s the infrastructure bet on whether AI transforms the global economy or stalls as overhyped technology. At $4.6 trillion, the market has already placed its wager. Follow NVDA to follow the AI revolution’s financial reality.
This page is for informational purposes only and does not constitute investment advice. Always conduct your own research before making investment decisions.