1MillionGPUs Blackwell B100 That Outperforms the H100: The Secret Behind Elon Musk’s Declaration of 1 Million GPUs
In-Depth Performance Analysis of NVIDIA’s Blackwell B100:
Comparing H100, A100, Google TPU, Tesla Dojo, and Future Prospects for GPT & Grok
With the unveiling of NVIDIA’s next-generation GPU architecture—the Blackwell-based B100—a new benchmark in AI compute performance has been set. In this comprehensive report, we delve into the performance and features of the B100, comparing it with previous generations like the H100 and A100, and analyzing how it stacks up against competitive accelerators such as Google’s TPU and Tesla’s Dojo. We also explore the prospects of integrating the B100 into next-generation AI models like the GPT series or Elon Musk’s Grok model, forecast the successor architecture following Blackwell, examine Musk’s ambitious plan for a supercomputer comprising 1 million GPUs, analyze DeepSeek’s GPU usage, and review product pricing details—all in one exhaustive guide.
NVIDIA Blackwell B100: Performance & Features
Revolutionary Dual-Die Design:
• The NVIDIA B100, built on the Blackwell architecture, is a next-generation data center GPU that introduces an innovative dual-die design—two chips operating as one.
• This design integrates a total of approximately 20.8 billion transistors (roughly 10.4 billion per chip), outpacing the previous Hopper-based H100 by an additional 12.8 billion transistors.
• Manufactured using TSMC’s customized 4nm (4NP) process, the two dies are connected via an ultra-fast interconnect that achieves a chip-to-chip bandwidth of 10TB/s.
• Each B100 GPU is equipped with 192GB of HBM3e memory, providing an enormous total memory capacity and a memory bandwidth of 8TB/s.
Unmatched AI Compute Performance:
• The B100 delivers industry-leading AI performance—according to NVIDIA, its AI processing power is 5× higher than that of the H100.
• In FP8 precision, it achieves a staggering 20 PFLOPS (petaflops) of compute, marking a 2.5× improvement over the Hopper generation; at FP4 precision, it reaches 40 PFLOPS, which is 5× faster than Hopper.
• For context, the FP8 performance of the H100 (with sparsity acceleration) is about 4 PFLOPS. Hence, the B100 provides roughly 5× the FP8 performance of the H100.
• Additionally, the B100 offers up to 1.8 PFLOPS in FP16/BF16 mixed precision (without sparsity) and delivers around 30 TFLOPS of FP64 double-precision performance—adequate for scientific computing tasks.
Balanced Power Efficiency:
• Despite its high performance and dual-die configuration, the B100 maintains power efficiency with a TDP of up to 700W.
• Its high bandwidth and massive memory capacity allow a single GPU to load and process AI models with up to 740 billion parameters—vastly surpassing the previous generation’s capability, which was limited to models in the tens of billions.
• This breakthrough addresses the critical importance of memory capacity and bandwidth for ultra-large models in the post-GPT-4 era.
B100 vs. H100 vs. A100: A Comparative Analysis
Understanding the performance of the NVIDIA B100 necessitates a comparison with its predecessors:
• NVIDIA A100 (2020 Release):
• Built on a 7nm process, the A100 integrates roughly 54 billion transistors and comes with 40GB or 80GB of HBM2e memory.
• It delivers 312 TFLOPS (in FP16 Tensor operations without sparsity) and achieves 1,248 TOPS (tera-operations per second) in INT8 performance.
• The A100 80GB model was originally priced at approximately $15,000–$17,000, while the 40GB model was around $9,000.
• NVIDIA H100 (2022 Release):
• Fabricated on a 4nm process, the H100 boasts over 80 billion transistors and is equipped with 80GB of HBM3 memory.
• NVIDIA claims that the H100 offers a 4× performance boost over the A100 under MLPerf 3.0 benchmarks.
• With its new Transformer Engine, the H100 supports FP8 operations, achieving FP16 performance in excess of 900 TFLOPS and roughly 4 PFLOPS per GPU for FP8 when leveraging sparsity.
• Market prices for the H100 have surged due to high demand, with some regions quoting around ¥5,430,000 (approximately $36,300) and average prices in the U.S. near $30,000; PCIe variants are slightly less expensive, though still above $20,000.
Given these comparisons, the B100 represents a generational leap—its performance is expected to be up to 10× higher than that of the A100, considering the H100 is already 4× faster than the A100. The B100’s strength in low-precision deep learning computations (FP8/FP4) will dramatically accelerate training and inference speeds for state-of-the-art deep learning models based on large-scale matrix operations.
The B200 and Other Variants
• Alongside the B100, NVIDIA has introduced a higher-end model known as the B200, also based on the Blackwell architecture and dual-die design.
• While its basic architecture is similar to the B100, the B200 features higher clock speeds and fully activated cores, delivering up to 30% improved performance.
• For instance, in FP4 operations, the B100 achieves 7 PFLOPS (dense), whereas the B200 reaches around 9 PFLOPS; similarly, for FP8 operations, the B100 delivers 3.5 PFLOPS (dense) versus 4.5 PFLOPS for the B200.
• In an 8-GPU HGX server configuration, eight B100s provide a total of 56 PFLOPS in FP8 and 112 PFLOPS in FP4, while eight B200s can reach 72 PFLOPS (FP8) and 144 PFLOPS (FP4).
• Both models support NVLink 5 and NVSwitch 4, enabling 1.8TB/s of inter-GPU communication bandwidth—crucial for efficient cluster configurations.
• Although there has been no official mention of PCIe card variants or consumer models of the B100/B200, there is speculation that the next-generation GeForce RTX 50 series may also leverage Blackwell architecture, hinting at potential spin-offs in the gaming and workstation sectors.
Competitive Analysis: Google TPU vs. Tesla Dojo
Google TPU (TPU v4 / v5)
• Google’s TPU is an ASIC designed specifically for deep learning training, employing large-scale matrix operation units to achieve high energy efficiency.
• TPU v4, launched in 2020, boasts a 10× performance increase over its third-generation predecessor, scalable via TPU pods.
• Academic reports indicate that, on a system scale, TPU v4 is 1.2–1.7× faster than NVIDIA’s A100 and consumes 1.3–1.9× less power. Note that these comparisons were made against the A100, and direct comparisons with the newer H100 have not been made.
• NVIDIA CEO Jensen Huang has countered by noting that since the H100 offers 4× the performance of the A100, TPU v4’s advantages would vanish in the H100 era.
• Public specifications for TPU v4 reveal approximately 275 TFLOPS per chip in BF16 operations, 32GB of HBM memory per chip, and 1.6TB/s memory bandwidth. TPU pods with over 2,048 TPU v4 chips deliver exascale performance for training giant models like Google’s PaLM.
• Mentions of TPU v5 and TPU v5P suggest further speed improvements (up to 2.8× faster than v4), underscoring Google’s continuous efforts to outpace NVIDIA.
• However, TPUs are available only via Google Cloud and are not sold as standalone hardware, limiting their ecosystem support compared to NVIDIA GPUs and their CUDA platform.
Tesla Dojo
• Tesla Dojo is a custom supercomputer developed by Tesla to accelerate AI training for its self-driving systems.
• At its core is the D1 chip, an AI-specific processor manufactured on a 7nm process that integrates 354 training nodes (compute units) per chip, delivering approximately 362 TFLOPS of performance in BF16 and CFP8 operations.
• This performance is comparable to, or slightly exceeds, the FP16 performance (312 TFLOPS) of an NVIDIA A100, achieved by densely integrating specialized compute units on a single die.
• Dojo organizes 25 D1 chips into a “training tile” that achieves roughly 9 PFLOPS in BF16/CFP8 performance. A rack constructed from a 6×6 fabric of 36 tiles is assembled into Tesla’s Dojo supercomputer (dubbed ExaPOD).
• According to Tesla AI Day 2022, a fully completed Dojo ExaPOD is designed to deliver approximately 1.1 EFLOPS theoretically.
• Since 2023, Tesla has been running Dojo partially to train its Autopilot neural networks and plans to invest around $1 billion in Dojo over 2024–2025 to expand its capacity.
• Notably, despite developing Dojo, Tesla continues to operate large NVIDIA GPU clusters. In 2021, Tesla built a supercomputer with 5,760 A100 GPUs (720 nodes × 8 GPUs) achieving 1.8 EFLOPS FP16 performance, and in 2023, they unveiled an expanded cluster with 10,000 of the latest H100 GPUs, estimated to deliver roughly 39.5 EFLOPS in FP8 performance.
• Elon Musk has commented that “if NVIDIA can supply enough GPUs, we might not even need Dojo,” highlighting that GPU supply remains a bottleneck. This suggests that Dojo is as much about reducing AI compute costs and overcoming supply constraints as it is about achieving unparalleled performance.
Performance Comparison:
• While Tesla’s first-generation D1 chip may lack the memory capacity and versatility of traditional GPUs, it is expected to deliver exceptional efficiency for dedicated workloads (such as Tesla’s autonomous driving vision models).
• In contrast, NVIDIA’s B100 offers broad applicability across various AI tasks, supported by a robust software ecosystem that can deliver immediate performance gains.
• As TPU v4 and Dojo are tailored for specific workloads within Google and Tesla, respectively, NVIDIA’s B100 is likely to have a more extensive impact across the wider AI research and industrial sectors.
Prospects for B100 in GPT & Grok Series
The emergence of next-generation GPUs is stirring up significant interest in how they will influence the development of large language models (LLMs). Leading AI projects like OpenAI’s GPT series and Elon Musk’s xAI Grok series are at the forefront of this trend, where the choice and scale of GPUs used for training profoundly affect both performance and development speed.
GPU Usage in the GPT Series
• GPT-3 (175B):
Released in 2020, GPT-3 with 175 billion parameters famously employed roughly 10,000 NVIDIA V100 GPUs for training over a 15-day period, performing an estimated 3.14×10^23 FLOPs.
• GPT-4 (~1T?):
Launched in 2023, GPT-4’s exact parameter count remains undisclosed (estimated in the hundreds of billions to a trillion), but OpenAI reportedly used approximately 25,000 NVIDIA A100 GPUs for continuous training, consuming about 2.15×10^25 FLOPs—roughly 70× the computational scale of GPT-3.
• GPT-5 and Beyond:
While not officially confirmed, industry insiders expect OpenAI to develop GPT-5 or a comparable multimodal model with significantly more parameters and data, potentially requiring 5× the compute of GPT-4. If GPT-5-level models are trained post-2025, the advent of NVIDIA’s B100 GPUs, with their 5× performance potential over the A100, could be a major boon. For example, if GPT-4 training with 25,000 A100s took 3 months, theoretically a similar task with 25,000 B100s could either reduce the training time by a factor of 5 or enable training of a much larger model in the same timeframe. Actual deployment will depend on B100 supply and infrastructure upgrade schedules, but if OpenAI adopts them rapidly, B100 clusters of 100,000–200,000 units could power GPT model training by around 2025. OpenAI will, however, likely validate the platform carefully to ensure stability and software optimization.
xAI Grok Series and B100
• xAI, led by Elon Musk since its 2023 inception, has developed its own language model series under the name Grok.
• Grok-1, 2: Limited details exist, but Musk has hinted via Twitter (now X) that Grok-2 was trained using thousands to millions of GPUs. Early reports suggest xAI secured around 10,000 NVIDIA GPUs in early 2023, which allowed rapid development of models comparable to OpenAI’s GPT-3.5.
• Grok-3: Released in February 2025, Grok-3 is positioned as a challenger to GPT-4 in terms of performance, having employed an astonishing 100,000 NVIDIA H100 GPUs in training. xAI built a supercomputer cluster called Colossus in Memphis, Tennessee, dedicated to training Grok-3. One estimate suggests that the energy consumed during Grok-3’s pre-training equaled about 7% of the power produced by a nuclear reactor in one month. Grok-3’s compute power is said to be 10× that of its predecessor, and Musk has hailed it as “one of the smartest AIs on Earth.”
• Future Plans for Grok-4 and Beyond:
xAI plans to further expand the Colossus supercomputer. Musk has announced ambitions to scale from 200,000 GPUs to ultimately 1 million GPUs. Achieving a 1 million GPU supercomputer would not only be the largest in the world by current standards but would also entail an investment estimated at around $25–30 billion (roughly 33–40 trillion KRW). Musk’s vision is that this immense investment will secure unprecedented compute power (potentially reaching nearly 10 EFLOPS with future B100 or Rubin GPUs), catapulting AI capabilities to new heights.
In this context, the arrival of the B100 will be a key variable for xAI. While Grok-3 currently runs on H100s, if B100s enter mass production by late 2025, xAI could adopt them for next-generation Grok-4 or Grok-5 training. Maintaining close ties with NVIDIA for GPU procurement, xAI may leverage the superior performance of the B100 to either substitute a portion of the planned 1 million GPUs with fewer units while achieving the same performance or to build even more advanced models. For instance, replacing 100,000 H100s with 100,000 B100s could theoretically offer a 5× speed improvement, potentially allowing xAI to surpass OpenAI in performance if a 100,000–200,000 B100 cluster is deployed for training Grok-4 or Grok-5.
Of course, such scenarios depend on actual B100 supply and production schedules. NVIDIA’s official roadmap hints that its next-generation successor to Blackwell might appear in late 2025 to early 2026, meaning that by the time xAI reaches its 1 million GPU target, even next-generation GPUs (based on the Rubin architecture, for example, the R100) could be considered. However, in the near term, B100 remains the strongest option for 2025–2026, making it the core tool for training next-generation ultra-large AI models in both GPT and Grok series.
Future Outlook: The Rubin Architecture as the Successor to Blackwell
NVIDIA has historically updated its data center GPU architectures on a roughly biennial cycle—Ampere (A100) → Hopper (H100) → Blackwell (B100). Now, industry insiders are already discussing the next generation under the codename “Rubin,” named after astronomer Vera Rubin, which is expected to power NVIDIA’s future AI GPUs.
Key Features and Anticipated Improvements of the Rubin Architecture:
• Finer Process Technology:
Reports suggest that the Rubin generation GPU (codenamed R100) will utilize TSMC’s 3nm process (N3). Compared to the 4nm process of the Blackwell B100, this move to a finer process is expected to enhance power efficiency and transistor density, aiming to deliver higher performance at the same power draw—a critical priority given that B100s already consume nearly 700W.
• Expanded Chiplet Design:
Observers predict that Rubin GPUs will adopt a quad (4)-chiplet design, often described as a “4× reticle design,” which would pack more silicon area into one package to maximize performance. NVIDIA’s current B100 already connects two dies using CoWoS-L packaging; Rubin is expected to further evolve this technology by efficiently linking four dies and significantly increasing inter-die bandwidth.
• Next-Generation Memory (HBM4):
The upcoming HBM4 memory technology is likely to debut on Rubin GPUs. Reports indicate that the R100 may feature an 8-Hi HBM4 stack (replacing the current 6-Hi HBM3e), which would drastically boost both memory capacity and bandwidth. Although the HBM4 standard is not yet finalized, advancements in memory integration suggest that each stack could offer 32GB or more and faster I/O speeds, potentially increasing total memory per GPU to over 256GB and surpassing 10TB/s bandwidth.
• Integration with Grace CPU:
NVIDIA is also developing its ARM-based data center CPU, Grace, to work in tandem with its GPUs. In the Rubin generation, an integrated module (codenamed GR200 or similar) combining Grace and Rubin could be released, allowing tighter CPU-GPU integration via a silicon interposer. This integration would reduce latency and boost bandwidth between CPU, memory, and GPU—critical for accelerating massive data loads during training of giant AI models.
• Performance and Release Timeline:
According to analyst Mitch Kou, the first Rubin-based GPU (R100) is targeted for mass production in Q4 2025, with initial shipments to large cloud providers starting in early 2026 and broader availability in the latter half of 2026. While exact performance figures have not been disclosed, the “generational leap” promised by Rubin suggests improvements of 2–3× over the B100. However, breakthroughs in power efficiency and memory bottlenecks will be essential, and Rubin may also feature specific accelerator optimizations (e.g., improved TF32/FP8 optimization and a more efficient Transformer Engine 2.0).
In conclusion, the upcoming Rubin generation—following the Blackwell B100—is expected to redefine AI computing through a combination of finer process technology, expanded chiplet integration, and next-generation memory improvements. These next-gen GPUs will likely become the mainstream workhorses for AI research post-2026, powering future GPT-6, Grok-5, and other ultra-large models with unprecedented compute capabilities.
Analyzing Elon Musk’s 1 Million GPU Supercomputer Vision
As mentioned in the Grok series discussion, Elon Musk has openly declared his ambitious plan to build an AI supercomputer consisting of 1 million GPUs. This section examines the background and significance of that vision.
xAI’s Colossus Supercomputer:
• xAI, founded by Elon Musk in 2023, has already trained Grok-3 using 100,000 H100 GPUs on a supercomputer cluster named Colossus located in Memphis, Tennessee, with plans to expand to around 200,000 GPUs soon.
• Even at this scale, xAI’s system ranks among the world’s most powerful AI platforms. The ultimate goal of 1 million GPUs represents an unprecedented scale.
• Computing Power:
If 1 million H100 GPUs were deployed, based on an approximate 4 PFLOPS FP8 performance per H100, the combined system could theoretically deliver over 4 exaFLOPS (EFLOPS) of compute power. This far exceeds the performance of current top supercomputers like Frontier (1.1 EFLOPS in FP64) and represents an all-time high in dedicated AI compute performance. With future GPUs like the B100 or Rubin-based models, a 1-million GPU cluster might even approach 10 EFLOPS.
• Cost Implications:
Acquiring and operating 1 million GPUs involves staggering costs. Considering an H100 is estimated at around $25,300 per unit, 1 million units would cost approximately $25–30 billion. When factoring in expenses for power infrastructure, cooling, labor, and maintenance, total investments could exceed $50 billion. For context, the annual capital expenditure of major cloud providers often hovers around $10 billion, making xAI’s project a monumental single initiative.
• Necessity & Utilization:
Musk argues that next-generation AI must be significantly larger and smarter than current models like ChatGPT or Grok-3, necessitating a several-fold increase in computational power. He emphasizes that improving AI performance hinges on two pillars: model scale and data volume. With high-quality data in short supply, leveraging massive amounts of self-generated synthetic or real-world data (e.g., Tesla’s autonomous driving videos) becomes critical. To process such enormous datasets, computational resources must overcome bottlenecks—hence the push for maximum GPU deployment. In short, the 1-million GPU supercomputer isn’t mere bravado; it’s a practical manifestation of Musk’s AI philosophy: “the one who computes the most wins.”
• Technical Challenges:
Integrating 1 million GPUs into a single cluster poses significant technical hurdles. Distributed training algorithms have been validated on scales of tens of thousands of nodes, but synchronizing and efficiently communicating across 1 million nodes introduces entirely new challenges. NVIDIA’s current solutions such as NVSwitch and InfiniBand HDR/NDR enable multi-terabyte-per-second communications for clusters of hundreds to thousands of GPUs, but scaling to 1 million requires unprecedented network topology design, software optimization, and fault tolerance. Fortunately, NVIDIA has been gathering experience in mega-cluster implementations through collaborations with Microsoft, Meta, and others, and xAI is likely tuning its own software stack to handle this scale. With efficient parallelization (combining model, data, and pipeline parallelism) and intelligent task scheduling, the potential of 1 million GPUs can be harnessed at high efficiency.
In summary, if realized, Elon Musk’s 1-million GPU vision would constitute a historic mega-project in AI, providing an overwhelming infrastructure advantage over competitors like OpenAI and Google. However, it would require solving enormous financial and technical challenges, as well as navigating geopolitical issues such as export regulations on advanced AI chips.
DeepSeek and the Chinese GPU Procurement Bypass
Meanwhile, in China, despite U.S. export restrictions preventing direct import of NVIDIA’s latest AI GPUs (such as the H100), there have been indications of efforts to procure these chips via alternate routes to develop ultra-large AI models. A notable example is the startup DeepSeek.
Overview and GPU Usage of DeepSeek:
• Founded in 2023, DeepSeek is a Chinese AI startup that originated as an AI research project within the High-Flyer hedge fund in the Chinese financial sector.
• In 2021, High-Flyer preemptively purchased around 10,000 A100 GPUs for AI trading applications, and based on this acquisition, DeepSeek was spun off to pursue broader AI model development.
• In 2024, DeepSeek unveiled its colossal language model, DeepSeek V3, boasting 67.1 billion parameters. Remarkably, it claims that this model was trained in just 2 months using only 2,048 H800 GPUs. The H800 is a version of the H100 with reduced bandwidth designed to comply with Chinese export restrictions, yet it maintains similar compute power to the H100. DeepSeek did not disclose exactly how it managed to train such a huge model so rapidly on a limited number of GPUs, but compared to Meta’s training of Llama3 (405 billion parameters) over 54 days on about 16,000 H100s, DeepSeek claimed an 11× improvement in GPU time efficiency. This has led some to speculate that DeepSeek might be utilizing more powerful hardware that has not been publicly disclosed.
• In January 2025, Bloomberg and other outlets reported that the U.S. government was investigating whether DeepSeek had illegally acquired NVIDIA GPUs. Allegedly, DeepSeek set up a shell company in Singapore to bypass U.S. controls and smuggle tens of thousands of H100 GPUs into China. Notably, NVIDIA’s accounting data revealed that its sales via Singapore surged from 9% to 22% within two years, suggesting the existence of a hidden route for H100 sales to China. The U.S. Department of Commerce and the FBI are reportedly investigating these allegations, while NVIDIA claims that it fully complies with regulations and that the sales increase is a “bill-to effect” related to regional reselling.
• According to analyses from independent media, DeepSeek is estimated to possess about 50,000 Hopper-generation GPUs, a mix of legally procured H800s (around 10,000 units), H100s secured prior to sanctions or via unofficial channels (about 10,000 units), and others likely being regulated versions such as the H20. (The H20, a downgraded Chinese-specific version of the Hopper, is said to have been produced in excess of 1 million units in 2024.) These GPUs are reportedly shared between High-Flyer and DeepSeek, and are employed in diverse applications from AI trading to large-scale model research.
Ultimately, the DeepSeek case highlights the fierce international competition for technological supremacy in AI—and the ways in which China is circumventing restrictions to secure advanced GPUs and develop models on par with GPT-4. While Elon Musk has expressed a desire for fair competition without Chinese hardware, the reality is that cutting-edge GPUs continue to flow into China through global supply chains. This situation, while generating substantial revenue for NVIDIA (in the tens of billions), also brings significant geopolitical risks. In the future, top-tier GPUs such as the Blackwell B100 or the Rubin R100 might follow similar distribution routes, ensuring that international regulation and technology leakage remain pressing issues.
Summary of Key Product Pricing Information
Below is an overview of the estimated market prices for major AI hardware products (based on market estimates from 2023–2025):
• NVIDIA A100 40GB: Approximately $8,000–$10,000 (with a list price of $6,999 at launch, increasing due to high demand).
• NVIDIA A100 80GB: Approximately $15,000–$17,000 (a higher-priced model due to its larger memory capacity; second-hand prices are somewhat lower).
• NVIDIA H100 (80GB, SXM5): Approximately $25,000–$35,000. Initially priced in the high $20k range, prices have soared with the 2023 AI boom, reaching over $30k in some regions; PCIe versions are slightly cheaper but still start above $20,000. Cloud providers offer rental rates around $2.5–$10 per hour.
• NVIDIA B100: Estimated unit price of $30,000–$40,000+ (pre-release estimate). Equipped with cutting-edge HBM3e in a dual-die design, its price is expected to be similar to or higher than the H100, with potential early supply constraints leading to premium pricing.
• NVIDIA B200: Estimated to cost over $40,000. As a higher-end product than the B100, it is anticipated to be prioritized for a select group of HPC customers; although official pricing is undisclosed, it will likely command a higher price based on its performance improvements over the B100.
• Google TPU v4: Not sold as a product; available through Google Cloud. Rental for an entire TPU v4 pod is estimated at tens of thousands of dollars per hour, with a single TPU v4 board (a 4-chip module) valued at over $10,000.
• Tesla Dojo D1 Chip: Price not disclosed. Tesla uses the Dojo system exclusively for internal purposes. However, given Tesla’s stated objective of lower cost relative to GPUs for equivalent performance, the unit price of the D1 chip+board is expected to be less than that of the A100 or H100. Considering Tesla’s planned $1 billion investment in Dojo by 2024, each tile (grouping of chips) might cost several hundred thousand dollars while delivering hundreds of PFLOPS.
• NVIDIA H800 (China-specific): Approximately ￥200,000 (estimated price in China, roughly 36 million KRW). While its performance is on par with the H100, its interconnect is limited, resulting in a slightly lower price. However, due to premium pricing in covert Chinese transactions, the actual selling price might be higher.
• NVIDIA H20 (China-specific): Not disclosed. This is a further downgraded version of the H800 for the Chinese market. While mass-produced in China, official pricing is unavailable (estimates suggest it may be priced similarly to the A100).
Please note that these prices are subject to market fluctuations. With continued high demand driven by the AI boom, even the secondary market prices for GPUs have occasionally exceeded new unit prices. While prices may stabilize post-2025 with the introduction of the B100 and increased competition from AMD and Intel, for now, securing AI chips remains synonymous with massive investments.
Conclusion
NVIDIA’s Blackwell B100 is emerging as a game-changer in the era of massive AI compute. With up to 5× the performance improvement over the H100 and breakthrough memory capacity and bandwidth enhancements that can support ultra-large models far beyond the tens of billions of parameters processed by previous generations, the B100 is set to become a core infrastructure component for next-generation AI models. Although specialized chips such as Google TPU and Tesla Dojo are also entering the market, NVIDIA’s B100—backed by its broad ecosystem and versatile applicability—remains difficult to surpass in the short term.
Leading AI projects like OpenAI’s GPT series and xAI’s Grok are expected to evolve even faster, leveraging the next-generation GPUs to accelerate innovation in model training and inference. Particularly, Elon Musk’s vision of a supercomputer built from 1 million GPUs could dramatically reshape the AI performance curve. Conversely, as seen with cases like DeepSeek, fierce international competition and regulatory circumvention in the AI hardware space mean that securing AI chips will continue to be a complex challenge involving technology, policy, and strategic factors.
Ultimately, the adage “better GPUs create stronger AI” remains true for now. The advent of NVIDIA’s Blackwell B100 and its successors like the Rubin architecture will provide the AI industry with unprecedented opportunities and challenges, potentially widening the gap in AI capabilities among companies and nations. AI researchers and industry leaders must closely monitor these hardware roadmaps while continuing to innovate in model architecture and efficiency to unlock meaningful, transformative outcomes. In the face of intense competition, these technological advancements will ultimately pave the way for more powerful and beneficial AI systems.
- NVIDIA Blackwell B100 vs. H100: A Game Changer for AI? – Tom’s Hardware
- Elon Musk’s 1 Million GPU Vision: How Blackwell B100 Fits In – The Verge
- The Future of AI Chips: NVIDIA’s Blackwell B100 and Its Impact – AnandTech
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