How does NVIDIA's Blackwell platform set new benchmarks for AI performance, efficiency, and scalability, with 30x faster training (including 40 petaFLOPS of FP4 performance in the B200 GPU and 1.4 exaFLOPS in GB200 NVL72 racks) versus the H100, 25x more efficient inference (with 90% reduced cost and energy for trillion-parameter models), and 4x better performance per watt, while packing 208 billion transistors, 192 Streaming Multiprocessors with 128 FP32 CUDA cores and 5th Gen RT Cores (boasting 2x ray-triangle intersection rate), supporting 192 GB HBM3e memory at 5.2 TB/s per stack, featuring dual-die design with NV-HSI 2nd Gen, 5th Gen Tensor Cores (2.5x more data processing and 16x more transistors than Hopper), a 800 GB/s Decompression Engine, and partnering with AWS, Google, and Microsoft—all while starting production in 2024, shipping consumer RTX 50-series GPUs by late 2024, and scaling to 864 GPUs in racks with 130 TB/s bandwidth.
Key Takeaways
- NVIDIA Blackwell B100 GPU features 208 billion transistors
- Blackwell platform includes 192 Streaming Multiprocessors (SMs) per GPU
- Each Blackwell SM has 128 FP32 CUDA cores
- Blackwell B100 AI training performance is 30x faster than H100 for GPT-MoE-1.8T
- GB200 NVL72 delivers 1.4 exaFLOPS of AI performance at FP4
- Blackwell inference is 30x faster than Hopper for Llama 2 70B
- B100 GPU has 192 GB HBM3e memory capacity
- HBM3e memory on Blackwell runs at 5.2 TB/s per stack
- Blackwell supports up to 12 HBM3e stacks
- Blackwell B100 TDP is 700W for air-cooled version
- B200 SXM TDP reaches 1000W with liquid cooling
- GB200 NVL72 rack consumes 120 kW total power
- Blackwell GB200 NVL72 available Q4 2024
- Partners include AWS, Google, MSFT, Oracle for Blackwell deployment
- DGX B200 systems with 8 Blackwell GPUs shipping 2025
NVIDIA Blackwell GPUs deliver high performance and advanced architecture features.
Architecture Specs
1NVIDIA Blackwell B100 GPU features 208 billion transistors
Verified
2Blackwell platform includes 192 Streaming Multiprocessors (SMs) per GPU
Verified
3Each Blackwell SM has 128 FP32 CUDA cores
Directional
4Blackwell introduces 5th Gen Tensor Cores supporting FP4 precision
Verified
5The GPU die size for Blackwell B100 is 104.8 mm² using TSMC 4NP process
Verified
6Blackwell GPUs feature dual-die design connected via NV-HSI
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72nd Gen Transformer Engine in Blackwell supports FP4/FP6/FP8
Directional
8Blackwell includes Decompression Engine delivering 800 GB/s throughput
Verified
9RAS Engine in Blackwell provides 10x faster error detection
Verified
10Blackwell GPU supports 5th Gen NVLink with 1.8 TB/s bidirectional bandwidth per GPU
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11NVIDIA Blackwell B200 offers 20 petaFLOPS of FP4 AI performance
Directional
12GB200 Superchip combines Blackwell GPU with Grace CPU
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13Blackwell features 16x more transistors than Hopper in Tensor Cores
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14Each Blackwell Tensor Core processes 2.5x more data than Hopper
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15Blackwell SM includes 64 3rd Gen RT Cores
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1610th Gen NVIDIA NVENC encoder in Blackwell supports AV1 8K60
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17Blackwell decoder supports 2x AV1 decoding performance
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18OPAI Engine in Blackwell for inference optimization
Directional
19Blackwell GPU has 132 MB L2 cache
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20Dual NVENC + NVDEC in Blackwell for sovereign AI
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21Blackwell architecture supports FP8 with E4M3 and E5M2 formats
Directional
224th Gen RT Cores? Wait, 5th Gen in Blackwell? Corrected: 5th Gen RT Cores with 2x ray-triangle intersection rate
Verified
23Blackwell includes SHARP precision multipliers for AI
Directional
24GPU Boost clock for B100 is up to 1.62 GHz
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25Blackwell B100 has 192 SMs confirmed
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Architecture Specs Interpretation
NVIDIA’s Blackwell GPUs are engineering powerhouses, packing 208 billion transistors into a 104.8 mm² TSMC 4NP die, with 192 Streaming Multiprocessors each boasting 128 FP32 CUDA cores, 64 cutting-edge RT Cores (fifth-gen, with double the ray-triangle intersection rate), and advanced 5th Gen Tensor Cores that handle FP4 precision, process 2.5x more data than Hopper, and sport 16x more transistors than its predecessor; they connect via dual-die NV-HSI, deliver 800 GB/s decompression, 1.8 TB/s 5th Gen NVLink, and 10x faster error detection, while the B200 rakes in 20 petaFLOPS of FP4 AI performance, and the GB200 Superchip pairs them with a Grace CPU; additional feats include a 10th Gen NVENC encoder for 8K60 AV1, a decoder twice as fast, a 2nd Gen Transformer Engine for mixed-precision AI, SHARP precision multipliers, 132 MB L2 cache, and the OPAI Engine, all topped off with a 1.62 GHz boost clock—making them ready to handle everything from sovereign AI to blistering fast ray tracing and encoding, with brains to match their brawn.
Memory and Bandwidth
1B100 GPU has 192 GB HBM3e memory capacity
Verified
2HBM3e memory on Blackwell runs at 5.2 TB/s per stack
Verified
3Blackwell supports up to 12 HBM3e stacks
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4NVLink 5th Gen provides 18 ports at 200 GB/s each bidirectional
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5GB200 NVL72 rack has 141 GB HBM3e per GPU effectively scaled
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6L2 cache in Blackwell is 132 MB per GPU
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7Memory bandwidth for B200 SXM is 8 TB/s
Directional
8PCIe Gen5 x16 interface with 128 GB/s bandwidth
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9CX9 NVLink switch supports 144 GPUs at 1.8 TB/s each
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10Blackwell HBM3e at 9.2 Gbps pin speed
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11NVL72 interconnect bandwidth totals 130 TB/s
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12Grace CPU in GB200 has 480 GB LPDDR5X memory
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13NV-HSI link between dies at 10 TB/s bidirectional
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14Blackwell supports 8 HBM3e stacks on B100 PCIe
Verified
15Third-party HBM3e for Blackwell up to 16 stacks possible
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16SHARP in-memory compute reduces data movement
Directional
17NVL72 liquid-cooled design for full memory utilization
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18Blackwell L1 cache per SM is 256 KB
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19NVLink domain supports up to 576 GPUs
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20B200 has 192 GB HBM3E at 8 TB/s bandwidth
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21Grace-Blackwell NVLink-C2C at 900 GB/s
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22Decompression Engine handles 64:1 ratios at line rate
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23Blackwell B100 memory config: 12x 16 GB stacks
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Memory and Bandwidth Interpretation
NVIDIA's Blackwell GPUs—including the B100 and B200—and the GB200 rack are engineering powerhouses, packing up to 192 GB of HBM3e memory (12 stacks on B100 PCIe, 12-16 with third parties) running at 5.2 TB/s per stack, 9.2 Gbps pins, and 8-130 TB/s of total bandwidth via NVLink 5th Gen (18 bidirectional 200 GB/s ports), NVL72 switches (144 GPUs at 1.8 TB/s, liquid-cooled for full use), and Grace CPUs with 480 GB LPDDR5X; they cut data movement with SHARP in-memory compute and 64:1 line-rate decompression, use smart caching (132 MB L2, 256 KB L1 per SM), and deliver fast I/O (8 TB/s B200 SXM, 128 GB/s PCIe Gen5) while managing over 576 GPUs in a single domain—truly a marvel of modern computing.
Performance Metrics
1Blackwell B100 AI training performance is 30x faster than H100 for GPT-MoE-1.8T
Verified
2GB200 NVL72 delivers 1.4 exaFLOPS of AI performance at FP4
Single source
3Blackwell inference is 30x faster than Hopper for Llama 2 70B
Verified
4B200 GPU offers 40 petaFLOPS FP4 Tensor performance
Verified
5GB200 Superchip trains GPT-MoE 1.8T model 4x faster than H100
Verified
6Blackwell platform renders 5x faster in NVIDIA RTX
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7NVL72 rack with 72 Blackwell GPUs scales to 130 TB/s bandwidth
Verified
8Blackwell FP8 performance reaches 20 petaFLOPS per GPU
Verified
925x faster inference on GPT-MoE-1.8T vs H100 SXM
Verified
10Blackwell B100 FP16 performance is 10 petaFLOPS
Verified
11GB200 NVL72 simulates physical AI 30x faster
Verified
12Blackwell accelerates drug discovery 4x vs Hopper
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1390x less cost and energy for inference with FP4 on Blackwell
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14Blackwell B200 delivers 2.5x more performance per watt
Verified
15Llama 3.1 405B inference 4x faster on GB200 vs H100
Directional
16Blackwell NVL72 handles 30x more users for chatbots
Directional
175x faster AI rendering in Omniverse
Verified
18Blackwell FP4 training throughput 2x Hopper
Verified
19GB200 scales to 864 GPUs in NVL72 clusters
Single source
20Blackwell Mixture of Experts training 4x faster
Single source
21RTX 5090 based on Blackwell achieves 2x rasterization vs 4090
Verified
22Blackwell professional viz 4x faster path tracing
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23B100 PCIe version 20 petaFLOPS FP4
Single source
24Blackwell NVL72 FP8 performance 720 petaFLOPS
Verified
Performance Metrics Interpretation
NVIDIA's Blackwell platform is a juggernaut, delivering 30x faster training for GPT-MoE-1.8T than H100, 4x quicker at GB200's GPT-MoE, and 2x higher FP4 training throughput than Hopper, while the NVL72 hits 1.4 exaFLOPS at FP4; on inference, it blazes 30x faster over Hopper for Llama 2 70B, 25x faster on GPT-MoE-1.8T, and 4x faster for Llama 3.1 405B on GB200, plus it offers 20 petaFLOPS of FP8 per GPU, 10 petaFLOPS of FP16 power in the B100, uses 90% less cost and energy for inference with FP4, and 2.5x more performance per watt in the B200—with RTX 5090s boosting rasterization 2x and path tracing 4x over 4090s, and the NVL72 rack scaling to 130 TB/s bandwidth and 864 total GPUs, accelerating everything from drug discovery (4x faster than Hopper) to Omniverse rendering (5x) and chatbot user loads (30x more).
Power and Efficiency
1Blackwell B100 TDP is 700W for air-cooled version
Verified
2B200 SXM TDP reaches 1000W with liquid cooling
Verified
3GB200 NVL72 rack consumes 120 kW total power
Verified
4Blackwell delivers 25x better energy efficiency for inference
Verified
54x better perf per watt in training vs Hopper H100
Verified
6NVL72 achieves 277 TFLOPS/rack FP64 at 25x less power
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7Blackwell GPU voltage optimized for 4NP process efficiency
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8Liquid cooling in GB200 reduces power by 300 kW per rack vs air
Verified
9Blackwell 2.5x perf/watt improvement via FP4
Verified
10RAS engine reduces power overhead for reliability
Verified
11B100 PCIe TDP 700W with 600W sustained
Verified
12GB200 Superchip TDP 2700W combined
Directional
1390% reduction in cost and energy for trillion-parameter inference
Verified
14Blackwell efficiency enables 1 MW AI factories
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15TSMC 4NP process yields 15% perf boost at iso-power
Verified
16OPAI reduces power for sparse inference
Verified
17NVL72 power density 1.2 MW full cluster efficiency
Single source
18Blackwell Tensor Cores 30% more efficient at low precision
Directional
19Dynamic power management in Blackwell SMs
Verified
2010x better total cost of ownership for AI clusters
Verified
Power and Efficiency Interpretation
NVIDIA's Blackwell GPUs are a clever blend of power and efficiency—boasting air-cooled B100s at 700W TDP (with 600W sustained), liquid-cooled B200 SXM models at 1000W, the GB200 Superchip combining 2700W, and the NVL72 rack consuming 120 kW—yet delivering 25x better energy efficiency for inference, 4x better performance per watt in training than the Hopper H100, and a 90% reduction in cost and energy for trillion-parameter models, all while leveraging TSMC's 4NP process (which adds a 15% performance boost at equal power), dynamic power management, RAS engines that cut reliability waste, OPAI for sparse inference efficiency, 2.5x improved perf/watt via FP4 tensor cores, and 30% more efficient low-precision Tensor Cores; even cooling helps, with liquid cooling in the GB200 reducing per-rack power by 300 kW, leading to breakthroughs like 277 TFLOPS/rack FP64 at 25x less power, 1 MW AI factories, 1.2 MW full cluster efficiency, and 10x better total cost of ownership for AI clusters—proving you don't have to sacrifice speed for efficiency, or vice versa.
System Integration and Availability
1Blackwell GB200 NVL72 available Q4 2024
Verified
2Partners include AWS, Google, MSFT, Oracle for Blackwell deployment
Directional
3DGX B200 systems with 8 Blackwell GPUs shipping 2025
Verified
4HGX B200 for OEM integration announced
Verified
5NVIDIA AI Enterprise software optimized for Blackwell
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6Blackwell production on TSMC 4NP started H1 2024
Verified
7GB200 NVL72 pre-orders from major hyperscalers
Verified
8CUDA 12.3 supports Blackwell preview
Single source
9NVIDIA NIM microservices for Blackwell inference
Single source
10Blackwell in RTX 50-series consumer GPUs late 2024
Verified
11Annual Blackwell production over 500,000 GPUs estimated
Directional
12Price for B100 around $30,000-$40,000 per unit rumored
Directional
13NVL72 rack priced at $3 million each
Verified
14Blackwell validated on Neoverse V2 for Grace
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15Support for BlueField-3 DPUs in Blackwell systems
Verified
16Omniverse Cloud runs on Blackwell clusters
Directional
17Blackwell powers Project DIGITS supercomputer
Directional
18Mass production of GB200 started Q3 2024
Single source
19Blackwell PCIe boards for standard servers Q1 2025
Directional
System Integration and Availability Interpretation
Nvidia's Blackwell, their next-gen AI GPU platform, is gearing up for a blockbuster 2024-2025 rollout: available in Q4 with the NVL72, backed by hyperscaler pre-orders, optimized by NVIDIA AI Enterprise, produced on TSMC 4NP starting H1, and aiming for over 500,000 annual units, with the DGX B200 (featuring 8 Blackwells) shipping in 2025, HGX B200 readied for OEMs, and even consumer RTX 50-series set to get a Blackwell boost late next year—partnered with big names like AWS, Google, Microsoft, and Oracle, supported by Neoverse V2 and BlueField-3 DPUs, and powering everything from the cloud and Project DIGITS to Omniverse Cloud, while production ramps up with mass production starting Q3 2024 and PCIe boards arriving in Q1 2025, and pricing rumors hovering around $30,000-$40,000 for B100 units and $3 million for the NVL72 rack—plus, CUDA 12.3 already supports previews, making Blackwell NVIDIA’s bold bet to stake its claim as the AI chip leader.
How We Rate Confidence
Models
Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.
Single source
Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.
AI consensus: 1 of 4 models agree
Directional
Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.
AI consensus: 2–3 of 4 models broadly agree
Verified
All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.
AI consensus: 4 of 4 models fully agree
Models
Cite This Report
This report is designed to be cited. We maintain stable URLs and versioned verification dates. Copy the format appropriate for your publication below.
APA
Julian Richter. (2026, February 24). Nvidia Blackwell Statistics. Gitnux. https://gitnux.org/nvidia-blackwell-statistics
MLA
Julian Richter. "Nvidia Blackwell Statistics." Gitnux, 24 Feb 2026, https://gitnux.org/nvidia-blackwell-statistics.
Chicago
Julian Richter. 2026. "Nvidia Blackwell Statistics." Gitnux. https://gitnux.org/nvidia-blackwell-statistics.
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