nvdia

From the data breakdown, let's see which component is the most valuable. Below, we analyze the latest GB200 setup with 72 NVIDIA cards, breaking it down into parts to assess the value of each.
Currently, a complete NVIDIA GB200 setup with 72 cards is approximately 20-30 million RMB. Let's take the midpoint, calculating at 25 million RMB; this is the composition of the GB200 setup with 72 cards.
72 B200 GPUs, 36 Grace CPUs, 36 GB200 Superchip compute nodes, liquid-cooled rack with NVLink/NVSwitch interconnects, and external InfiniBand/Ethernet networking.
B200 GPU compute chip/package, excluding HBM, estimated at 8 million, accounting for 32%, with 72 B200 cores holding significant value, including GPU die, advanced packaging, substrate, etc.

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Hello traders & market watchers!
After an aggressive rally powered by the global AI boom, Nvidia (NVDA) is now moving through a consolidation phase near key technical support.
The stock recently climbed to around $236.54 before retracing toward the $214 region, where buyers are attempting to stabilize price action.
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➤ Daily Chart Breakdown
Despite short-term weakness, the broader trend structure remains bullish on higher timeframes.
✔︎ Current Market Snapshot
➜ Recent peak: $236.54
➜ Current trading area: $214.29
➜ Intraday range: $213.67 – $218.45
➜ Previous session close: $214.28
◆ Important Technical Zones
➤ Near-Term Support: $203 – $213
➤ Stronger Demand Area: $181 – $190
➤ Resistance Range: $225 – $236.54
Recent candles show increased volatility as momentum cools after a sharp upward move, but long-term buyers still appear active.
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◆ Major Factors Influencing NVDA
① AI Demand Remains Extremely Strong
Nvidia continues benefiting from expanding AI infrastructure spending, especially through its dominant data center business.
② Competition in the AI Chip Space
Rising competition from firms like Huawei and other semiconductor companies is becoming a growing discussion point across global markets.
③ Investor Confidence Still Elevated
Strong earnings growth, ongoing innovation, and corporate buybacks continue supporting long-term sentiment around the company.
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At the moment, NVDA appears to be experiencing a normal correction within a larger bullish trend tied to AI adoption worldwide.
However, traders should still remain cautious because volatility in high-growth tech stocks can increase quickly during market rotations.
✔︎ Reminder:
This content is for educational purposes only and should not be considered financial advice. Always perform your own analysis and manage risk responsibly.
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Do you think NVDA will reclaim new highs soon, or is a deeper pullback still possible?
➜ Share your technical targets and market outlook below
$NVDA
#NVDIA #NVDA #OndoFinanceFounderPassesAway #RENDER4MonthHighAIDemand

Following thoughts regarding ROI concern for AI weighted co's like $OPENAI $ANTHROPIC and XAI etc have been bothering me for couple of days so i thought to write in a thought and opinion fashion. Hope it make sense.
Thought. Is AI model race about better model for better task and cheaper model for cheaper task? The model alone is the product ?
Opinion. The actual product should be orchestration system that puts the model inside a very capable harness and pairs the model with a lot of tools. The tools could be bringing in valuable context that exist within your enterprise uniquely that could serve unique set of customers. So focus should be on optimization of best performance grades rather than how many tokens engineers spent.
Thought. What if USA AI models could work just like how linux powers android as a open source model or like open source Chinese models?
Opinion. If $NVDAB DG x spark that puts GB 10 GPU chip with unified memory in one local device eg a laptop or a PC could be embedded to locally Enterprise administered server.
Thought. Wall Street has started to make their move whether these heavy weighted AI model co's could be able to generate real revenue on all that $750 billion + Capex and will the gap between chinese open source model and frontier models will become more wide or it gets narrow?
Opinion. What matters is taking all these capabilities and grounding them in the context that will power small enterprises and businesses and letting them work on deployment, harness and orchestration. The gap between open source and frontier model will likely continue till ROI signals otherwise.
Thought. Why should non-tech or small company host their own AI model?
Opinion. General public perception of AI is negative and we are not going to do good if AI is locked in frontier labs or concentrated among large co's that can afford to pay for inference for these tokens. The logical way for AI to work is people should be able to control the way it runs, what model they run, what knowledge it has access to and shouldn't worry about it going offline just becuse of a vulnerability in data center. Local compute can solve these problems regarding insane costs, data sovereignty and flexibility using #NVDIA DGX Spark hardware which unifies the memory across CPU and GPU inside that local device unlike a data center where memory b\w CPU and GPU is not unified. That will also eliminate power bottleneck of running a data center by just utilizing few hundred watts to run it in a local device and it could be locally administered. Token spent could be scaled exponentially across people without building more data centers.
Thought. If more developers, companies and governments etc do desk site computing/ Local computing than infrastructure buildout is not necessary?
Opinion. Yes that infrastructure buildup won't be necessary.
Thought. Why AI is not making revenue?
Opinion. Because of limited compute and limited power. What if we use power available to us all the time and build power efficient structure at the chip level that unifies memory across CPU and GPU. People could be using AI instead of performing tasks manually because compute run time becomes the computer aka AI becomes the operating system just like what Linux did to all Android users since its also open source.
Thought. Why USA doesn't support open source as strategically as it does for chips and data centers?
Opinion. Lack of guardrails and very high risk of AI hallucinations. Android is powered by Linux and it's open source still that doesn't bother Android users because it allows people to patch vulnerabilities pretty fast. AI success depends on its large scale use and that depends on its affordability just like Android.
Thought. Alex karp of #Palantir have warned that Enterprises should be careful of sharing their secret sauce with AI co's for their own survival. How can we protect our data while using AI?
Opinion. Unique proprietary data protection is only possible if we have the control over privacy and sovereignty if we make AI models uniquely design to serve 1 company locally. We do not want our local devices to be given access to our personal files, images and data and start surveillance of ourselves. That is why cost element will also be justified with privacy.
These thoughts and opinions are only for educational purpose.