Efficiency Imperative - Case Supporting QCOM DC Inference Model..............................................
In yesterday’s post the below statement was inadvertently omitted, which makes an even stronger case for QCOM data center inference solutions in both new builds and retrofits to existing data centers.
Over the past several weeks a number of companies have seen their share prices show “nice” gains from articles mentioning partnerships / associations with AI outfits (Broadcom / Open AI being the latest).
Per normal- QCOM is always dismissed as irrelevant --- which is far from the truth and which no one appears to understand, YET.
Also below is a WSJ article and Copilot Q&A that further makes the QCOM case, with astounding metrics.
In QCOMs upcoming Q4 earnings call CA should set the record straight and explain QCOM’s data center high efficiency / low power solutions for both new builds and retrofitting existing DCs .
CA- discuss this- attempt to "make them understand" --- Lets get the share price up where it belongs!!!!!
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Efficiency Imperative: The Case For Qualcomm’s Win-Win Inference Model
The global surge in AI adoption is driving exponential demand for inference workloads and internet traffic—yet the electrical infrastructure required to support this growth is strained, expensive, and unevenly distributed. Power grids in major markets face capacity bottlenecks, aging substations, and soaring upgrade costs. Meanwhile, electricity rates continue to climb, placing a growing financial burden on enterprises, governments, and consumers alike.
Qualcomm’s high-efficiency, low-power inference architecture directly addresses this crisis. By operating at just 150W per rack—less than half the draw of competing GPU-based systems—Qualcomm enables air-cooled deployments that avoid costly HVAC retrofits and grid expansions. The result: up to $30,000 in annual TCO savings per rack, driven by reduced electricity usage, lower cooling overhead, deferred infrastructure upgrades, and bundled software orchestration.
While Qualcomm’s solution includes premium silicon, orchestration software, and integration services, these upfront costs are substantially offset by the TCO savings over a 10-year payback period. In retrofit and sovereign deployments alike, Qualcomm is the only vendor offering a drop-in, PCIe-based inference solution that delivers sovereign-grade throughput without sovereign-grade power demands—a win-win for operators and the public alike.
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To further support QCOMs case, the WSJ has published several articles featuring Broadcom’s data center initiatives, including this one today.. .
Why Broadcom’s Bet on OpenAI Is a Big Risk
Broadcom is hitching its future to Sam Altman’s vision
By Asa Fitch- Oct. 14, 2025 5:30 am ET
Why Broadcom’s Bet on OpenAI Is a Big Risk
Snip from the above article >>>
The scale of OpenAI’s deals is remarkable: together, they would involve filling data centers with chips and other computing infrastructure that consume 26 gigawatts of electricity—an amount that would dwarf what New York City uses at peak demand in the summer. And yet that is only about a tenth of what Chief Executive Sam Altman recently told employees he wanted to build in the next eight years.
Q- I asked Copilot to put the above in context (
A - snip hear with full answer way below.
>>>>> Full Copilot answer
A- That 26-gigawatt figure is jaw-dropping on its own—but when you realize it represents just 1/10th of Sam Altman’s eight-year vision, the scale becomes almost surreal. Let’s unpack it:
What Does 26 Gigawatts Mean?
- New York City peak demand: NYC, one of the most energy-intensive cities in the world, peaks around 12–13 gigawatts on the hottest summer days.
- 26 gigawatts is twice that—enough to power 20 million U.S. homes simultaneously.
- This is just for AI infrastructure—not a national grid, not a metro area, but data centers running AI workloads.
Why “1/10th” Is So Alarming
If 26 GW is only 10%, Altman’s full-scale ambition implies:
- 260 gigawatts of AI compute infrastructure—more than the entire current electricity generation capacity of Japan.
- It would exceed the total U.S. data center power consumption today by more than 10x.
- It’s equivalent to building dozens of nuclear power plants or hundreds of utility-scale solar farms just to feed AI.
Strategic Implications
- AI as a new industrial sector: This scale of energy demand puts AI on par with steel, chemicals, or transportation in terms of infrastructure footprint.
- Grid transformation: Utilities, regulators, and governments will need to rethink grid design, siting, and energy sourcing.
- Geopolitical leverage: Whoever controls the supply chain for AI chips, power, and cooling at this scale could shape global tech dominance.
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