In recent years, there has been remarkable improvement in AI capabilities. Many people own computers or smartphones, and many have experienced generative AI capabilities for free. Improving AI capabilities requires securing a large number of high-performance GPUs.





It is natural that huge investment costs are required, but if no troubles occur, it is a fact that new GPU models with improved processing capabilities appear every year. And it is also a fact that the United States is operating AI businesses at the world top level by securing a large number of GPUs.
From this perspective, I wrote about a niche GPU model.







NVIDIA's B200 chip is an AI-oriented GPU based on the Blackwell architecture, and its supply is tight worldwide.

I will explain the causes in an easy-to-understand way.

First, on the demand side, amid the rapid AI boom, major hyperscalers such as Meta, Microsoft, Google, and Amazon are concentrating large-scale orders.

These companies utilize B200 for training and inference of generative AI and large language models (LLM),
seeking 4 times the performance compared to H100 (20PFLOPS).

It is sold out until the end of 2025, and new orders have a 12-month wait. Small and medium-sized enterprises have difficulty obtaining it, and there are concerns about declining competitiveness.






On the supply side, the delay in transitioning to TSMC's CoWoS-L packaging technology is a major factor.

Production is sluggish from the end of 2024 to the first half of 2025, affected by earthquakes and component shortages (HBM3e memory).

Additionally, since NVIDIA prioritizes production of GB200 (a superchip with multiple B200s), the supply of standalone PCIe versions of B200 is limited.

Geopolitical risks, such as export restrictions due to US-China trade friction, are also disrupting the global supply chain.

As a result, while AI data center investments accelerate in 2025, supply cannot keep up, and tightness is expected to continue for several quarters. This situation symbolizes the growth of the AI industry while highlighting challenges for the entire industry.









Using NVIDIA's B200 chip brings great benefits to AI development.


About the Performance of B200


3 times training performance compared to H100, 15 times inference performance improvement, with 208 billion transistors and 192GB HBM3e memory, it can efficiently handle trillion-parameter level LLMs.

This accelerates breakthroughs in scientific fields such as drug discovery, climate change simulation, and quantum computing. For example, the analysis time for complex molecular structures is significantly shortened, speeding up new drug development.


As a benefit, it can reduce inference costs and power consumption by 25%.

Improved energy efficiency suppresses data center operating costs and enables sustainable AI operations. Additionally, as a possibility, the democratization of real-time generative AI advances.

Chatbots and recommendation systems can be built at low cost, making it easier for small and medium-sized enterprises to launch AI factories. The effect of AI capability improvement is remarkable, with the introduction of FP4 precision doubling the bandwidth, and multimodal learning (integration of text, images, and audio) accuracy improving.

Scalability enhancement with 5th generation NVLink makes collaboration of multiple GPUs smooth, making training of AGI (artificial general intelligence) level models realistic. As a result, it promotes industrial transformation and creates innovative applications in education, healthcare, and entertainment fields. B200 is the key to promoting industrial transformation and expanding the future of AI.







The NVIDIA B300 (Blackwell Ultra) chip in 2026 is highly likely to face supply tightness.

Shipments will ramp up from the end of 2025, but delays in TSMC's production ramp-up (ongoing issues with CoWoS-L transition and aftermath of earthquakes) will affect, and component shortages due to doubled demand for HBM3e memory are also severe.

Analyst forecasts indicate that 80% of FY26 data center revenue of $154.7 billion is Blackwell-related, with GB300 rack shipments revised downward from initial 50-80k to 15-20k, and stockouts for several quarters are certain.

The cause of demand is the continued acceleration of AI investments. Expansion of LLM training scale and continued NVIDIA dependence even with Meta's ASIC/AMD shift, and potential revival of the Chinese market with geopolitical easing.






B300 has 288GB memory　(1.5 times B200's 192GB) and over 8TB/s bandwidth, excellent for large-scale model processing, with FP4 performance of 14PFLOPS (55% improvement over B200's 9PFLOPS).


With high-density design at TDP 1100W, it becomes the foundation for next-generation AGI and expert-level AI, monopolizing large orders from hyperscalers.

While B200 is suited for inference and scientific computing, B300 is specialized for training with double scale, and superior ROI (return on investment) due to NVLink enhancement.
Price over $400,000 but increased flexibility with SXM Puck modularization, and supply chain redesign evokes premium demand. As a result, tightness exceeding B200's 2025 sell-out will occur, further boosting the growth of the AI ecosystem.








NVIDIA's B300 chip (Blackwell Ultra) brings revolutionary benefits to AI development.

Compared to B200, FP4 performance 1.5 times (over 15PFLOPS), attention performance doubled, and 288GB HBM3e memory enables processing of ultra-large-scale models (over trillion parameters).

This makes AI inference 11 times faster (compared to Hopper), training 4 times faster, realizing real-time AI reasoning (e.g., video generation 30 times faster).
As a benefit, energy efficiency improves, improving power consumption per TPS (tokens/second) by 5 times, significantly reducing data center operating costs.


Possibilities expand with the construction of AI factories. Immediate responses become possible for medical genetic analysis, financial predictive analysis, and e-commerce intelligent agents, enhancing productivity across industries.


The effect of AI capability improvement is remarkable, with model accuracy improved by test-time scaling, and scalability more than doubled with NVLink enhancement. As a result, AGI-level advanced reasoning AI becomes accessible, accelerating social transformation. This chip is the key to further illuminating the future of AI.

Causes of Global Shortage and Demand for B200

・Hyperscalers (Meta, MS, Google, Amazon) monopolize with massive orders ・4 times performance of H100 for explosive speed in AI training and inference ・TSMC's CoWoS-L transition delay + earthquakes + HBM shortage ・NVIDIA prioritizes GB200 production → shortage of standalone B200 ・US-China friction disrupts supply chain

Points

"Everyone wants it too much but can't make enough" state continues until end of 2025

Benefits and Possibilities of Using B200

・Training 3 times, inference 15 times faster ・192GB large-capacity memory handles trillion-parameter level LLMs with ease ・Drug discovery and climate simulation become dramatically faster ・Inference cost & power 25% reduction → data center savings ・Real-time generative AI becomes usable even for small and medium enterprises ・Multimodal (image + audio + text) accuracy skyrockets ・AGI (human-level AI) development becomes realistically closer

Points

"AI becomes fast, cheap, and smart" dream chip with all three

Demand Outlook for B300 in 2026

・Shipments start at end of 2025 but expected immediate sell-out due to production ramp delay ・288GB memory (1.5 times B200), over 8TB/s bandwidth ・Training specialized, optimal for creating next-generation AGI ・Hyperscalers place massive orders saying "must have B300" ・Price over $400,000 but outstanding ROI ・Potential demand explosion with revival of Chinese market

Points

"Even those satisfied with B200 will definitely want B300 once they see it" level

Benefits and Possibilities of B300 (Future Developments)

・5 times improved energy efficiency (TPS/MW) for cost reduction ・Real-time processing like video generation 30 times faster ・Medical (genetic analysis) ・Finance (prediction) ・Innovation in e-commerce (AI agents) ・AI factory for multi-user simultaneous support, low-latency services

Points

"Toward a world where AI is active in every scene of daily life"

　Rank 1st　　United States

Estimated installed units (B200 GPU)　Approx 2,500,000 - 3,000,000 units　　70-80%

- AWS: Project Ceiba with 20,000+ units (400 exaflops AI cluster, Q4 2025 deployment)
- Microsoft Azure: Over 1 million units (DGX B200/GB200 based, for AI training)
- Google Cloud: 800,000 units (TPU integrated)
- ANL (Argonne National Lab): Solstice with 100,000 units (1,000 exaflops, DOE science project)
- CoreWeave/Lambda: Hundreds of thousands units (CSP expansion).

- Driver: Hyperscaler-led AI investments (OpenAI/Meta/Google).

Challenges: Energy consumption (1,000W/GPU), shortage of liquid-cooled data centers.






　Rank 2nd　　Taiwan

Estimated installed units (B200 GPU)　Approx 10,000 units (in operation)　　0.3%

- Foxconn (Hon Hai): 10,000 units (AI factory supercomputer, for research/startups, Q3 2025 completion)
- NYCU (National Yang Ming Chiao Tung University): Initial DGX B200 introduction (hundreds of units, AI research platform).

- Driver: TSMC/NVIDIA production collaboration, semiconductor ecosystem.

Challenges: Durable design for earthquake risks.



　Rank 3rd　　South Korea

Estimated installed units (B200 GPU)　Approx 5,000 - 10,000 units (planned/partial operation)　　0.2-0.3%

- Government-led: Over 50,000 units planned (mainly mixed with H100, estimated 5,000-10,000 B200, sovereign cloud/AI factory)
- Samsung/SK Group/Hyundai: Thousands of units (AI manufacturing/research factories, Q2 2025 debut). - PYLER (AdTech): Hundreds of units (real-time video analysis, 30x performance improvement)
- Seoul National University: 4,000 units network access (mixed with H200, transitioning to B200).

- Driver: Semiconductor industry (Samsung HBM supply), government AI strategy.

Challenges: Shift to NVIDIA to reduce dependence on Huawei alternatives.



　Rank 4th　　Japan

Estimated installed units (B200 GPU)　Approx 4,000 - 10,000 units (in operation/planned)　　0.1-0.3%

- Sakura Internet: 10,000 units (government subsidy "Koukaryoku" cloud, HGX B200 system, March 2025-2026 deployment)
- SoftBank: 4,000+ units (DGX B200 SuperPOD, world-class AI cluster). - Tokyo University of Technology: <100 units (2 exaflops AI supercomputer)
- AIST (National Institute of Advanced Industrial Science and Technology): ABCI-Q expansion (2,000 units mixed with H100, adding B200).

- Driver: National AI projects (subsidies over 1.3 billion yen), earthquake/climate research.

Challenges: Power supply constraints (dependence on renewable energy).



　Rank 5th　　Germany

Estimated installed units (B200 GPU)　Approx 1,000 - 5,000 units (initial/planned)　　<0.1%

- Deutsche Telekom/NVIDIA: 10,000 units planned (industrial AI cloud, for Siemens/Ansys, 2025 construction-2026 completion, initial B200 1,000-5,000 units)
- EU sovereign AI initiative: Pilot introduction (hundreds of units, manufacturing projects).

- Driver: EU manufacturing support (Industry 4.0), sovereign data protection.

Challenges: Delays in GDPR-compliant data migration.




　Rank 6th　　Other Europe (Netherlands/France/Spain/United Kingdom)

Estimated installed units (B200 GPU)　Approx 2,000 - 5,000 units (distributed/initial)　　<0.1%

- Netherlands (TU/e Eindhoven): Initial DGX B200 adoption (hundreds of units, AI research)
- France (Scaleway): Thousands of units (AI cloud, late 2025)
- Spain/United Kingdom: Via Oracle EU/UK Government Cloud (hundreds of units, Blackwell services)
- EU overall: Global Scale data centers (US/EU selected, pilot clusters).

- Driver: EU AI Act compliant sovereign infrastructure investments.

Challenges: Cross-border data transfer regulations.





　Rank 7th　　Australia

Estimated installed units (B200 GPU)　Approx 500 - 1,000 units (initial)　　<0.05%

- Oracle Australian Government Cloud: Hundreds of units (Blackwell services, late 2025)
- NEXTDC data center: Small-scale trials (transitioning from H200).

- Driver: Government cloud expansion, climate model research.

Challenges: Delays due to geographical isolation.




　Rank 8th　　China

Estimated installed units (B200 GPU)　Approx 100 - 500 units (under restrictions/variants)　　<0.01%

- B20/B30A variants (export regulation compliant, performance-limited B200, via Inspur Q2 2025 debut)
- Transition from Huawei alternatives: High non-NVIDIA dependence (Ascend chip 5-20% yield).


- Driver: Domestic AI self-reliance.

Challenges: US export regulations (TPP limit 600 TFLOPS, 1/7.5 of standard B200).





　Rank 9th　　UAE/Indonesia/Singapore (Others)

Estimated installed units (B200 GPU)　Approx 500 - 1,000 units (distributed)　　<0.05%

- UAE (MorphwareAI): Hundreds of units (Abu Dhabi AI expansion)
- Indonesia (Indosat): Sovereign cloud initial (hundreds of units)
- Singapore (Singtel): Via Yotta/Shakti Cloud (trials)

- Driver: Emerging market AI investments.

Challenges: Immature infrastructure.








About NVIDIA's B200・B300 Performance, Demand, and Benefits



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