Bountiful_Harvest
1 일 전
(Written today, by an attorney - November 22, 2024) Interesting View:
"Kakashii@kakashiii111
NVIDIA Revenue by Region provides a closer look at two red flags that I've been talking about for over a year. First, the sophisticated system NVIDIA has built to sidestep U.S. export restrictions on AI chips. Second, the reliance of NVIDIA on the Magnificent 5's capex for its continuous growth (In this thread, I will not talk about the round-tripping methods or AI startups, including CoreWeave).
NVIDIA, the master of selling shovels of AI and creating FOMO among the industry, is also, apparently, playing international chess. Let’s take a closer look at this unfolding saga of market gymnastics, where Taiwan somehow fades, Singapore soars, and China quietly resurfaces. Behind the scenes, it all starts to look like a clever workaround—a sophisticated strategy to sidestep U.S. export restrictions on AI chips. Bravo, NVIDIA, truly a 4D chess move. Seems like you made it too complicated for the DOC and the DOJ.
Let’s start with Singapore, the real plot twist here. This tiny island nation, which was never an important region in NVIDIA’s history, and where sales were between zero and a few hundred million, coincidentally started to pick up right after the first introduction of U.S. Chips export restrictions in October 2023.
Singapore which had just $1 billion in sales in mid-2023, suddenly catapulted to $7.7 billion by October 2024, passing Taiwan, which until 2023 was the most important region for NVIDIA since its establishment. Yes, you’re reading it right—Singapore, historically irrelevant in the semiconductor space, is now NVIDIA’s second-largest market, surpassing both Taiwan and China. What happened? Well, Singapore isn’t building fabs or launching new AI ventures at this scale—it’s simply a strategic middleman.
Singapore known in its strong trade infrastructure, and neutral political stance make it the perfect hub for funneling chips to “other destinations” without raising too many eyebrows. Its proximity to China and to other black markets in the Middle East, India, and Russia and more.
Officially, NVIDIA started to disclose two quarters ago, after we raised eyebrows here on X, that Singapore is just the "billing location." But unofficially? well, I don't need to explain. Maybe NVIDIA will need to explain to the DOJ in the future.
Now, on to Taiwan, the former star of NVIDIA’s show. Taiwan, the number one region in revenue for NVIDIA from its establishment until 2023, when NVIDIA introduced the AI shovels and kicked off FOMO among the Magnificent 5, which propelled the U.S. to become the first region in terms of revenue, surpassing Taiwan for the first time.
Now, let's discuss this quarter, with all the crazy demand around, right Jensen? Somehow, from $5.7 billion in July 2024, sales dipped to $5.1 billion in October 2024. For the semiconductor region where all of NVIDIA's most important partners and distributors come from, this drop raises eyebrows. Most of NVIDIA's distributors and partners are Taiwanese, so how is this even possible?
Do we have a demand problem signaling here, or is it a step up in the sophisticated system to bypass the US export restrictions?
Is it related to the sudden surge in Singapore’s sales to surpass Taiwan as the second-largest market? The math might say “coincidence,” but the context says otherwise.
And then there’s China, the wild card in this story. Sales there jumped from $3.7 billion to $5.4 billion in just one quarter. What happened this quarter that led to a sudden double-digit percentage growth in China? We all know China can only get inferior chips compared to the H100/A100 and they don't like inferior chips.
So, Is it the Blackwell versions? or is it signaling that China knows more severe U.S. chip export restrictions are on the way? Be sure of this: this kind of leap isn’t casual—it’s strategic.
Moreover, NVIDIA knows China is a critical market for AI hardware, but with U.S. export restrictions tightening, the company has to tread carefully. Could this spike reflect a last-minute push to flood the Chinese market before more restrictions kick in? Or is Singapore quietly facilitating the flow of chips into China under the radar? Either way, it’s clear NVIDIA isn’t giving up on the Chinese market—no matter the future legal or compliance implications.
And what about the U.S.? The U.S. has always been an important reigion for NVIDIA and used to rank number 3 among the years, just after Taiwan and China. But since 2023, there has been a shift, with the U.S. becoming NVIDIA’s largest market, raking in $14.8 billion in October 2024. As we all know, this is thanks to the big American 5—the Magnificent 5: Microsoft, Tesla, Meta, Alphabet, and Amazon. Yes, OpenAI also plays a role here.
But the U.S. may not stay number 1 for long with the explosive growth in Singapore’s sales. If this trend continues, Singapore could eventually overtake the U.S. as NVIDIA’s top market next year! Imagine that: a country with a population smaller than New York City overtaking the world’s biggest economy in AI chip revenue.
New Zealand has more sheep than people, so soon, we will joke that Singapore have more NVIDIA chips than people?"
ShawnP123
1 일 전
Nvidia needs a good PR company specifically one that understands what makes stocks move. There were two major issues in the Q conference which caused the higher short volume and continuing the large swings.
CFO stating 70% would be their low range. Well let's take a look at some other GP's.
AMD 50%, Intel 50%, Goog 56%, IONQ 51%, AVGO 65%, PLTR 78%, Apple 44%, Micron 35%. So by her stating 70% is low, is basically a contradiction to some other major companies. When someone asked her the question on what would be low, I think a better response would have been " anything below 65%. I doubt very much they will be able to maintain their 74% too much longer. Competition is one reason. So now their GP's are low.
The other issue is some of the conflict or interpretation of Jensen's remarks on Blackwell production/yields. He stated they are in full production but they are trying to improve yields. He also stated the reason for the low GP was the effort/expense put into bringing Blackwell to production. So, there the assumption of yield issue still exists. He's lucky no one asked a question of what is a normal yield and where does Blackwell stand.
I think they need to be better prepared for the Q presentation since we potentially going to see the very high percentage increases we've seen in the past and they will have to depend on being much better in what/how they present. So be prepared for the large swings and downward pressure from articles which are going to be somewhat to more negative. Nvidia is a target.
Konaploinks
1 일 전
"cuda-q and ionq quantum simulation to inference dna sequence with google alpha fold3 for syncon creation of dna/rna/protein treatment?
Show drafts
This is a really fascinating and complex question that explores the cutting edge of how quantum computing, AI, and biotechnology might converge!
Here's a breakdown of the different components and their potential interplay:
1. CUDA-Q and IonQ Quantum Simulation
CUDA-Q: Nvidia's CUDA-Q is a platform designed to accelerate quantum computing workloads. It provides tools and libraries for developers to write quantum algorithms and run them on GPUs and quantum processors, including those from IonQ.
IonQ Quantum Computers: IonQ builds trapped-ion quantum computers, which are a leading type of quantum computer with high qubit fidelity and long coherence times.
Quantum Simulation: Together, CUDA-Q and IonQ's quantum computers could be used to perform quantum simulations of biological molecules, such as DNA, RNA, and proteins. These simulations could provide insights into the behavior and interactions of these molecules at the atomic level.
2. Google AlphaFold3 and DNA Sequence Inference
AlphaFold3: Google's AlphaFold3 is a powerful AI model that can predict the 3D structure of proteins from their amino acid sequences. It has revolutionized protein folding prediction.
DNA Sequence Inference: While AlphaFold3 primarily focuses on proteins, it's conceivable that similar AI models could be developed or adapted to infer information about DNA or RNA sequences based on their structure or other properties. This could involve predicting gene function, identifying potential mutations, or understanding how DNA interacts with other molecules.
3. SynCon Creation of DNA/RNA/Protein Treatments
SynCon (Synthetic Consensus) Vaccine Design: SynCon is a vaccine design approach that involves creating a synthetic version of a virus's genetic sequence that represents a "consensus" among various strains, aiming to provide broader protection against different variants.
DNA/RNA/Protein Treatments: SynCon could potentially be extended beyond vaccines to design DNA, RNA, or protein-based treatments for various diseases. This could involve:
Gene Therapy: Using synthetic DNA or RNA to correct genetic defects or introduce therapeutic genes.
Protein Engineering: Designing synthetic proteins with specific functions, such as targeting cancer cells or delivering drugs.
Putting it all Together
Here's how these technologies might potentially work together:
Quantum Simulation: CUDA-Q and IonQ's quantum computers could simulate the interactions of DNA, RNA, or proteins with drug candidates or other molecules.
AI Inference: AlphaFold3 or similar AI models could analyze the simulation data to infer properties of the DNA/RNA sequences or predict the effects of different treatments.
SynCon Design: This information could then be used to guide the design of SynCon-based DNA/RNA/protein treatments, optimizing them for efficacy and safety.
Challenges and Considerations
Technology Maturity: Quantum computing and generative AI are still evolving fields. Integrating these technologies for drug discovery and development will require further advancements and overcoming challenges in scalability, accuracy, and interpretability.
Data Requirements: Training AI models and performing accurate quantum simulations require large and high-quality datasets.
Ethical Considerations: Developing and deploying these technologies responsibly and ethically is crucial.
Potential Benefits
Accelerated Drug Discovery: This combination of technologies could significantly accelerate the drug discovery process by providing a more efficient way to simulate and analyze molecular interactions.
Personalized Medicine: It could enable the development of personalized treatments tailored to an individual's genetic makeup and specific disease characteristics.
New Therapeutic Approaches: It could lead to new therapeutic approaches based on DNA, RNA, or protein-based therapies, potentially addressing diseases that are currently difficult to treat.
In Summary
The integration of CUDA-Q, IonQ quantum simulation, AlphaFold3, and SynCon represents a fascinating convergence of quantum computing, AI, and biotechnology. While still in its early stages, this combination of technologies holds immense potential for revolutionizing drug discovery and development, leading to more effective and personalized treatments for various diseases."
DiscoverGold
2 일 전
Nvidia Says New Chip on Track After Forecast Disappoints
By: Bloomberg News | November 21, 2024
Nvidia Corp. assured investors that its new product lineup can maintain the company’s artificial intelligence-fueled growth run, though the rush to get the chips out the door is proving more costly than expected.
Speaking after the release of quarterly results, Chief Executive Officer Jensen Huang said that Nvidia’s highly anticipated Blackwell products will ship this quarter amid “very strong” demand. But the production and engineering costs of the chips will weigh on profit margins, and Nvidia’s sales forecast for the current period didn’t match some of Wall Street’s more optimistic projections.
That brought a tepid reaction from investors, who had bid up Nvidia shares almost 200% this year heading into the earnings report. After that dizzying rally, which turned the chipmaker into the world’s most valuable company, anything but a blowout quarter was bound to be a disappointment. The shares fell about 1% in premarket trading, paring earlier declines.
Nvidia predicted fiscal fourth-quarter sales of about $37.5 billion. While the average analyst estimate was $37.1 billion, projections ranged as high as $41 billion.
“The guidance seems to show lower growth, but this may be Nvidia being conservative,” said Alvin Nguyen, an analyst at Forrester Research Inc. “Short term, there is no worry about AI demand. Nvidia is doing everything they should be doing.”
The company’s biggest moneymaker is its accelerator chip, which helps develop artificial intelligence models by bombarding them with data. Since OpenAI’s ChatGPT chatbot debuted in 2022, a frenzy of AI services has created insatiable demand for the product.
Wall Street has been closely watching the launch of Blackwell, the latest entry in that category, which is faster and has an improved ability to link up with other semiconductors. Manufacturing challenges have slowed the rollout, and Nvidia warned again of supply constraints on Wednesday. Demand for the products is expected to exceed supply for several quarters.
“Critical questions around Blackwell’s production ramp and customer concentration remain key concerns,” Emarketer analyst Jacob Bourne said in a note. “There’s little room for execution missteps in 2025.”
Huang said that Blackwell is now in “full production,” and there’s still an appetite for Hopper, the previous design. “Blackwell is now in the hands of all of our major partners,” he said during the conference call.
But the switch to Blackwell has taken a toll on profitability. The company’s gross margin, which measures the percentage of sales remaining after deducting the cost of production, will dip to as low as 73% this quarter from 75% in the previous period. The figure is expected to rebound when the new products reach larger-scale production, and the economics are more favorable.
When asked whether Nvidia’s gross margin could be back in the mid-70s by the middle of next year, Chief Financial Officer Colette Kress said that’s a reasonable assumption. Nvidia remains far above its peers in this category: Its nearest rival, Advanced Micro Devices Inc., has a gross margin that’s 20 percentage points narrower. Intel Corp.’s isn’t even half of Nvidia’s total.
Nvidia’s growth over the past two years has been staggering. Its sales are poised to double for a second year in a row, and it now notches more money in profit than it used to generate in total revenue.
Nvidia’s revenue rose 94% to $35.1 billion in the fiscal third quarter, which ended Oct. 27. Excluding certain items, profit was 81 cents a share. Analysts had predicted sales of about $33.25 billion and earnings of 74 cents a share.
Nvidia’s biggest division, the data center unit, saw revenue double from a year earlier to $30.8 billion. That beat Wall Street estimates.
But networking revenue within that unit declined sequentially, and the business is more dependent than ever on a small group of customers: cloud service providers. That cohort, which includes companies such as Microsoft Corp. and Amazon.com Inc.’s AWS, accounted for 50% of data center revenue, up from 45% in the prior period.
Investors want that number to go down, to show that the use of AI is spreading across the economy.
Other recent earnings reports have given strong signals for AI. Nvidia customers, including Microsoft, Amazon and Meta Platforms Inc., have reaffirmed their commitment to spend heavily on AI infrastructure.
Nvidia has only missed analysts’ estimates on quarterly revenue once in the past five years. And it has exceeded expectations by as much as 20% in recent periods, creating a high bar for its performance.
Its data center division alone now has more revenue than rivals Intel and AMD have in total, combined. Net income this year is on course to exceed revenue at Intel, a business that was the chip industry’s biggest company for decades.
Nvidia made its name by selling graphics processors, but discovered that the technology also has applications for AI. Its chips help software models during the training process, when they learn to recognize and respond to real-world inputs. Nvidia’s components are also used in systems that then run the software, a stage known as inference, and help power services such as ChatGPT.
The Santa Clara, California-based company has rapidly expanded its product lineup to include networking, software and services, as well as fully built-out computer systems. Huang is traveling the world lobbying for a broader adoption of his technology and trying to spread its use by corporations and government agencies.
“The age of AI is upon us and it’s large and diverse,” Huang said.
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