In a new blog post, NVIDIA has shared how Blackwell GPUs are going to add more performance to the research segment which includes Quantum Computing, Drug Discovery, Fusion Energy, Physics-based simulations, scientific computing, & more.
When the architecture was originally announced at GTC 2024, the company showcased some big numbers but we have yet to get a proper look at the architecture itself. While we wait for that, the company has more figures for us to consume.
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NVIDIA also sheds light on the double-precision of FP64 (Floating Point) capabilities of its Blackwell GPUs which are rated at 30% more TFLOPs than Hopper. A single Hopper H100 GPU offers around 34 TFLOPs of FP64 compute and a single Blackwell B100 GPU offers around 45 TFLOPs of compute performance. Blackwell mostly comes in the
GB200 Superchip which includes two GPUs along with the Grace CPU so that's around 90 TFLOPs of FP64 compute capabilities. A single chip is behind the AMD MI300X and MI300A Instinct accelerators which offer 81.7 & 61.3 TFLOPs of FP64 capabilities on a single chip.
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NVIDIA quickly shifts gears and brings us AI performance once again where its Blackwell GB200 GPU platform once again reigns supreme with a 30x gain over H100 in GPT (1.8 Trillion Parameter). The GB200 NVL72 platform enables up to 30x higher throughput while achieving 25x higher energy efficiency and 25x lower TCO (Total Cost of Operation). Even putting the GB200 NVL72 system against 72 x86 CPUs yields an 18x gain for the Blackwell system and a 3.27x gain over the GH200 NVL72 system in Database Join Query.
But NVIDIA isn't stopping any time soon as the company is anticipated to start production of its
next-gen Rubin R100 GPUs by as early as late 2025 and the initial details sound insane.