To grasp what’s within the playing cards for Nvidia in 2026, we have to return and try an important strikes the corporate made in 2025 and estimate how they may develop in 2026.
Nvidia (NVDA) ended the yr with a deal that has left many traders and analysts stunned, and even somewhat bit confused. Contributing to the confusion was the truth that when CNBC broke the information, it reported that Nvidia would purchase Groq for about $20 billion, however as soon as the official announcement from Groq got here out, it turned out that the deal was a non-exclusive licensing deal and expertise seize slightly than an organization acquisition.
Listed here are key questions Financial institution of America analyst Vivek Arya raised concerning the deal, in a analysis be aware shared with TheStreet:
- What does the “non-exclusive licensing agreement” referred to by Groq indicate?
- Might Nvidia have developed this know-how by itself?
- Can Groq cloud, nonetheless an unbiased firm, undercut Nvidia’s LPU-based
service with decrease pricing?
Regardless of having these questions and calling the deal stunning, Arya additionally mentioned the deal is strategic and complementary. He reiterated a purchase score and the value goal for Nvidia inventory of $275.
To grasp the Groq deal, we have to delve into what Groq know-how is about and what the dominant methods within the tech trade have morphed into.
Nvidia’s chip of the long run is an LPU.
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What’s Groq?
Groq’s foremost enterprise is GroqCloud, a man-made intelligence inference platform. AI inference is the method of producing a response from the AI mannequin that has already been skilled.
Groq affords builders a method to run AI fashions on the corporate’s {hardware} and procure responses in a short time for a aggressive worth. The rationale a comparatively small startup can compete with large gamers and supply aggressive pricing for AI inference is its {hardware}.
The corporate’s inference platform makes use of application-specific built-in circuit (ASIC) chips, which it calls the Language Processing Unit (LPU), developed and optimized particularly for LLM inference.
GPUs can be utilized for a lot of completely different calculations together with gaming, 3D rendering, crypto mining, AI coaching, and AI inference, however Groq’s LPU chips have just one goal — AI inference.
This implies they’ve razor-sharp focus, and that makes them many occasions as environment friendly at that individual activity.
What’s Nvidia getting from the take care of Groq?
When Gemini 3 launched, Google touted that it had been skilled 100% on its Tensor Processing Items (TPUs), and naturally, it’s doing inference on TPUs, too. You’ll have guessed appropriately that TPUs are additionally ASIC chips.
Following the information about Gemini Nvidia’s publish on X (previously Twitter):
“We’re delighted by Google’s success — they’ve made great advances in AI and we continue to supply to Google. NVIDIA is a generation ahead of the industry — it’s the only platform that runs every AI model and does it everywhere computing is done. NVIDIA offers greater performance, versatility, and fungibility than ASICs, which are designed for specific AI frameworks or functions.”
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The truth that Nvidia felt it wanted to deal with Gemini in its publish confirmed that the corporate is apprehensive concerning the aggressive energy of well-designed ASIC chips, and now now we have proof.
Groq’s announcement concerning the take care of Nvidia says: “As part of this agreement, Jonathan Ross, Groq’s founder, Sunny Madra, Groq’s president, and other members of the Groq team will join Nvidia to help advance and scale the licensed technology.”
Are you able to guess what Jonathan Ross’s job was at Google? He was one of many designers of Google’s first technology of TPUs, after all. Nvidia’s resolution to license Groq’s LPU tech stack and to “acqui-hire” its expertise staff is a quiet admission that ASIC chips characterize the way forward for AI.
What does the non-exclusive licensing a part of the take care of Groq imply for Nvidia?
A non-exclusive licensing deal was the one method to keep away from authorities scrutiny. The method here’s a mixture of Apple and Meta methods. Apple manufactures customized ARM chips and has a non-exclusive licensing settlement with ARM.
However what makes Apple chips nice is the expertise that solely Apple can appeal to; up to now, competitor ARM chips have been unable to catch up.
Nvidia has secured prime expertise on this transaction by mimicking Meta’s transfer, which was an funding in Scale AI. The entire take care of Scale AI turned out to be much more about getting Alexandr Wang to steer Meta’s Superintelligence unit than about investing in Scale AI.
That is the brand new dominant technique within the tech house the place expertise is extra useful than complete corporations.
Assuming that Nvidia’s contract with Groq doesn’t have some particular quirks, non-exclusive licensing ought to imply that different corporations can license the LPU designs and construct related LPUs. Nvidia is content material, because it isn’t getting the expertise, and it is betting they received’t be constructing something spectacular with simply the license.
The second of Arya’s questions, whether or not Nvidia might have constructed LPUs by itself, looks like a superfluous one. Even when the corporate might have developed such chips (assuming no patent points), it couldn’t have finished that in a fascinating timeframe.
This underscores my thesis that Nvidia began to fret about TPUs somewhat late.
Wider implications of the Groq-Nvidia licensing deal
To reply the third of Arya’s questions, we have to first decide Nvidia’s recreation plan for LPUs. In an e-mail to workers that was obtained by CNBC, Nvidia CEO Jensen Huang wrote the next.
“We plan to integrate Groq’s low-latency processors into the NVIDIA AI factory architecture, extending the platform to serve an even broader range of AI inference and real-time workloads.”
Huang has promoted the thought of AI factories for a while, and he seems more and more centered on it. This new LPU plan has lastly made the entire thing click on for me, and the sudden shift to inference is extraordinarily attention-grabbing and revealing.
After all of the hype of chasing AGI or Superintelligence, the market is shifting towards inference. You’d suppose that so long as we haven’t reached that incredible life-changing know-how, the coaching capabilities can be of paramount significance.
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The issue is that LLMs have peaked, and though the shift to inference and “AI factories” is Huang’s stealthy pivot, LPUs are only one a part of the puzzle. Nvidia just lately introduced the Nvidia Nemotron 3 household of open fashions, knowledge, and libraries. These fashions are the important thing element of the newest pivot, which is AI factories and sovereign AI.
Knowledge possession, privateness, and mannequin fine-tuning are a number of the causes any firm or group that may afford to have a sovereign AI would need it. That is why open-source, and at the least open-weight, fashions are the long run, similar to ASIC chips.
We are able to see a gradual, ongoing shift towards this, as a whole bunch of educational papers introduced at NeurIPS, the premier AI convention, used Qwen, as reported by Wired.
“A lot of scientists are using Qwen because it’s the best open-weight model,” Andy Konwinski, cofounder of the Laude Institute, a nonprofit established to advocate for open U.S. fashions, advised Wired.
Huang’s plan appears to be an entire sovereign AI answer that provides the quickest inference for the bottom energy consumption supplied by LPUs, mixed with GPUs for coaching, and Nemotron as a starter software program platform.
Arya additionally wrote this in his be aware: “We envision future NVDA platforms where GPU and LPU co-exist in a rack, connected seamlessly with NVDA’s NVLInk networking fabric.”
I’ll adamantly say that this concept is inaccurate.
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LPUs have a very completely different reminiscence mannequin, primarily based on so-called SRAM reminiscence, which could be very costly and really quick. In accordance with Groq, its LPUs join immediately through a plesiosynchronous protocol, aligning a whole bunch of chips to behave as a single core.
Groq calls its chip-to-chip interconnect know-how RealScale. LPUs have one different key distinction in comparison with GPUs: they’re deterministic. These architectural variations imply that LPU and GPU chips can’t work collectively to run the identical software program (carry out inference), and inserting them in the identical racks would solely trigger issues and complicate issues.
Each LPU has little or no reminiscence; an enormous variety of LPUs are wanted to run large LLM fashions. This would be the deciding issue on what number of racks of LPUs might be wanted to run the mannequin.
It’s actually doable for Nvidia to develop its LPUs to be quite a bit completely different in comparison with Groq’s, to permit for the blending with GPUs in the identical racks, however in that case, they might take much more time to develop. I consider that for Huang’s plan for AI factories, pace of improvement takes precedence.
In any case, a launch of Nvidia’s LPUs in 2026 is very unlikely, contemplating that chip design takes at the least a yr. The Groq deal and inference pivot inform us we have to watch what occurs to OpenAI very intently.
Nvidia take care of OpenAI stays in query
On December 19, Reuters reported that SoftBank Group is racing to shut a $22.5 billion funding dedication to OpenAI. Contemplating that SoftBank’s pledge was to take a position that cash by the top of the yr, they’re slicing it fairly shut.
Ready till the final second to observe by makes the corporate look uncertain if it’s a good funding.
Nvidia’s settlement with OpenAI to take a position as much as $100 billion within the startup continues to be not finalized, in line with a Reuters report from December 2. OpenAI doesn’t anticipate to be money circulate constructive till 2030, in line with Forbes.
It is easy to see why Nvidia isn’t speeding to finalize the take care of OpenAI. The perfect-case state of affairs for OpenAI is that Nvidia is ready for it to have an IPO first, whereas the worst-case state of affairs, after all, is that there isn’t any deal.
OpenAI failing to safe extra investments can have a domino impact that may damage Oracle, Nvidia, and Microsoft probably the most. Nvidia’s AI factories technique is an efficient approach for the corporate to guard itself from dependence on OpenAI as a buyer.
What can we anticipate from Nvidia’s partnership with Intel?
In accordance with the leaks, Intel Serpent Lake is the primary chip that may function an built-in Nvidia GPU, which received’t launch earlier than 2027. Even that’s optimistic, and 2028 is extra possible, as reported by PC GAMER.
Nvidia’s income combine and estimates for 2026
The newest Financial institution of America analysis be aware that features estimates for Nvidia is from November. Arya and his staff estimate that Nvidia’s income for fiscal yr 2026 might be $212.83 billion, and non-GAAP EPS might be $4.66. Nvidia missed consensus estimates in Q3 for its income from gaming by 4%. There have been rumors that Nvidia is trying to lower gaming GPU manufacturing by as much as 40% in 2026 as a consequence of VRAM provide points, as reported by PC GAMER.
We are able to anticipate that because of the reminiscence trade going all-in on AI, skyrocketing RAM costs can have a aspect impact of fewer gaming PCs being bought and constructed, so the gaming income might simply miss the consensus once more.
Within the automotive section, the same scenario is clear, as Nvidia missed consensus estimates for Q3 by 6%. The corporate’s steering for This fall is considerably decrease than consensus estimates of $700 million, at $592 million.
The corporate’s steering for the professional visualization section for This fall is optimistic at $760 million and better than the consensus $643 million. OEM, together with the crypto section outlook for This fall, is near consensus at $174 million in comparison with $172 million.
Non data-center income segments look tiny in comparison with Nvidia’s outlook of $51.2 billion and consensus estimates of $57 billion for This fall. As the corporate focuses extra on its highest-margin merchandise, income from non-data heart segments will proceed to shrink.
The Vera Rubin line launch would be the defining second of 2026, as a result of if the chips convey the promised efficiency and effectivity uplift, it’s going to destroy any doubts concerning Nvidia’s supremacy.
The following yr would be the yr of Nvidia, particularly if the rumor that Google did not safe HBM shipments for its TPUs, as reported by Android Headlines, proves true.
I wouldn’t be stunned if this rumor is true, as Huang is at all times a number of steps forward of the competitors, aside from the second when he underestimated Google TPUs.
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