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So looks Meta AI is not going well and their chip purchases from nvda Broadcom and amd are causing a surplus . If meta cuts their spending on AI , will that cause panic in the AI trade ?

Even meta itself has been using external sources for compute. I think it was google decision to ration compute quota on meta that triggered meta decision to do the so call selling compute resource externally the decision is long term and the thought by meta probably was that they want to build their cloud business to get rid of the reliance on third party entirely ultimately. By making it a business segment justified the huge amount of capex to be spent there in future.

Two implications. One - competitions among big tech companies are not stopping and it forced meta to go for this capital intensive project to ensure and secure its own compute infrastructure longer term. Two - Meta thinks there is enough appetite out there to swallow the excess compute resources they said they have currently and future new ones.

Both implications are great for hardware makers.

valuedge.app valuation lab

I’m sharing both screenshots because this is exactly why NVDA is hard to underwrite right now.

Fundamentally, the numbers are ridiculous. NVIDIA is no longer just “growing fast.” It is doing $80B+ per quarter, with Data Center making up the overwhelming majority of revenue and gross margins still around the mid-70s.

That is why I understand the bull case.

If AI infrastructure spending keeps scaling, and if NVIDIA keeps anything close to this margin profile, the stock can still make sense even after the huge move.

But the technical picture is not screaming “easy entry” either.

The chart I’m looking at shows:

• 1Y return still positive
• price below the 50-day trend
• RSI around neutral
• MACD showing negative momentum
• bullish divergence showing up, but not full confirmation yet

So to me, the current NVDA setup is basically:

Valuation says there may be upside. Timing says it still needs proof.

The fair-value read I’m looking at shows NVDA around 46% below estimated fair value, but I don’t think that should be treated as a simple buy signal. The real question is whether the assumptions behind that fair value are durable.

For me, the whole NVDA debate comes down to one thing:

Are current AI infrastructure economics becoming NVIDIA’s new normal, or are we extrapolating a shortage cycle at peak margins?

Bull case:

1. AI capex keeps expanding
2. Blackwell/Rubin demand stays strong
3. Networking and systems increase NVIDIA’s platform control
4. Margins stay structurally higher than old semiconductor cycles
5. Data Center becomes more like infrastructure toll-road economics

Bear case:

1. Hyperscalers eventually optimize spend
2. Custom silicon takes more share
3. Margins normalize faster than expected
4. China/export restrictions remain a drag
5. The market has already priced in years of perfect execution

I’m not bearish on NVDA. I’m just trying to separate valuation thesis from timing signal.

Curious how this sub sees it:

Is NVDA actually undervalued here if the AI capex cycle continues, or does the stock need a cleaner technical setup before adding?

https://preview.redd.it/igw7akae53bh1.png?width=1208&format=png&auto=webp&s=99e5924201b273333cefef93010f57935282d168

Nvidia announced on Thursday that it will trade computing power to artificial intelligence startups for a cut of future profits.

The revenue-sharing model represents a way for startups to gain access to Nvidia's chips, servers and infrastructure and bypass the token bottleneck that has created an expensive barrier to entry.

The plan is for AI firms to share product and cloud-based revenue with the chipmaking giant.

Nvidia cited a pair of companies out of Australia as its first two customers under the arrangement.

I have compiled the moats Nvidia currently has, at this point, my count is 7 total. This type of analysis, and the long term advantages of the business, are logical things to base long term investment decisions on. Short term price action is not.

1.) CUDA's 4–5 million developers, 20+ years of optimized libraries, and deep integration into every major AI framework create a self-reinforcing platform that takes at minimum 18 months to escape — and most enterprises never try.

2.) Nvidia's NIM, NeMo, and CUDA-X software stack creates enterprise stickiness that survives hardware refresh cycles and generates recurring revenue competitors cannot quickly replicate.

3.) Nvidia's NVLink, InfiniBand, and Spectrum-X networking are co-designed with its GPUs into a single integrated "AI factory" system, meaning swapping the chip also requires re-architecting the network.

4.)Nvidia's preferential TSMC CoWoS packaging and SK Hynix HBM3E allocations give it production delivery timelines through 2027 that well-capitalized rivals — including AMD — cannot yet match at scale.

5.) Nvidia's Omniverse, Isaac, and Cosmos platforms extend its CUDA ecosystem lock-in into physical AI — robots, autonomous vehicles, and industrial systems — creating a new TAM that runs on infrastructure competitors have not yet built.

6.)Nvidia's consistent two-year hardware cadence with full backward software compatibility means hyperscalers plan their infrastructure around Nvidia's cycle, making switching a multi-year planning disruption rather than a procurement decision.

7.) Thirty years of accumulated GPU architecture talent, concentrated under a founder-CEO with a track record of on-time delivery, creates an execution edge that cannot be hired away fast enough to matter.

Please use this thread to discuss what's on your mind, news/rumors on NVIDIA, related industries (but not limited to) semiconductor, gaming, etc if it's relevant to NVIDIA!

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CPU CEOs are claiming the CPU vs GPU ratio in inference data centers will hit 1:1 or even higher. Is this just marketing, or is it actually happening? Would love to hear from people who actually configure data centers, is this info strictly a Wall Street whale secret?

If a 1:1 ratio is real, market caps should reflect it. NVDA is near $5T. Does that mean INTC+AMD combined should also be $5T (~$2.5T each, implying 2-3x upside from here)? Is Wall Street actually pricing them on this assumption?

Here’s my take: For inference tools like Codex/Claude/Cursor, the CPU acts as a traffic cop for orchestration, but the heavy lifting is still done by GPUs/ASICs. Does orchestration really require a 1:1 ratio? As GPUs evolve, couldn't they just absorb these tasks internally, reverting us back to a 1:8 CPU:GPU ratio?

Plus, hyperscalers already complain about GPU costs. If they have to match every expensive GPU with an equally expensive CPU, CapEx doubles. They can't possibly want that.

TL;DR: Why does inference need so many CPUs, can GPUs replace them, and is the 1:1 ratio actually happening or just CPU maker spin?

https://preview.redd.it/k7bet9z8fcah1.png?width=1530&format=png&auto=webp&s=d27dec13b408560279d1692ee9ec0d8f858f35f5

Lisa Su is taking Leather Jacket's lunch money and buying $AMD stock with the proceeds.

u/warm-spot2953 what cope is next?

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"Oh noes!" to all the "Google TPU gonna overtake Nvidia GPU" doomerism