Nvidia Blackwell Statistics

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.

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.

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).

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.

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.