Nvidia Blackwell GPUs dominate benchmark AI | Hardware | Hardware definition and examples | Cpu vs gpu vs ram | Turtles AI

NVIDIA’s new Blackwell GPUs set outstanding records in MLPerf v4.1 benchmarks for AI training, with significant gains over the previous Hopper architecture. The performance improvements are accompanied by new technologies that improve efficiency in processing complex workloads.

Key points:

• Blackwell outperforms Hopper by up to 2.2 times in AI training benchmarks.
• Blackwell GPUs handle complex AI workloads with improved HBM3e memory and superior processing capacity.
• The Nyx AI supercomputer has set seven world records using the Blackwell architecture.
• NVIDIA’s roadmap calls for the evolution of Blackwell with the Blackwell Ultra platform in 2025, followed by Rubin in 2026.

NVIDIA recently revealed outstanding results of its new Blackwell GPUs in MLPerf v4.1 benchmarks, achieving significantly higher performance than previous Hopper-based models. The data show a performance increase of up to 2.2 times in AI training workloads, solidifying Blackwell as the ideal choice to address the growing demand for computing power in next-generation AI data centers. The success of Blackwell GPUs is not limited to mere speed, but also extends to their ability to handle complex workloads with greater efficiency and lower resource consumption than their predecessors. In particular, the Nyx AI supercomputer, built on DGX B200 architectures, shattered seven world records in various benchmarks, including pre-training and fine-tuning large language models (LLMs) such as GPT-3 175B and Llama 2 70B.

The new Blackwell architecture introduces an advanced set of technologies that optimize the use of Tensor Cores, the hearts of AI computation, by improving the handling of complex mathematical operations such as matrix multiplications, which are critical in deep learning algorithms. Another key element is the adoption of high-bandwidth HBM3e memory, which enables high performance with fewer GPUs. For example, the GPT-3 175B benchmark, which required 256 GPUs with Hopper, was run on only 64 Blackwell GPUs with no loss in performance per processing unit. This breakthrough allows NVIDIA to reduce operating costs and improve the power efficiency of its solutions, making Blackwell even more attractive to large data centers.

Despite the great success of Blackwell, NVIDIA’s roadmap is already looking to the future, with plans for a further leap forward in performance with the launch of Blackwell Ultra in 2025. This new platform, based on a B300 architecture, will offer a significant increase in available memory (up to 288 GB of HBM3e) and computing power, continuing the evolution of a market that demands increasingly powerful and scalable solutions. Next, in 2026, will come Rubin, a new generation of chips that will adopt HBM4 memory to further push the limits of performance in AI. NVIDIA’s efforts are not limited to improving the chips, but also extend to their rapid deployment on a large scale, with systems already ready to support AI workloads in data centers around the world.

These developments confirm NVIDIA’s position as a leader in the industry, not only in manufacturing GPUs, but also in creating integrated solutions that optimize performance on a large scale. The future of AI computing is set to be dominated by these advanced architectures, which lay the foundation for the next evolutionary leap in AI.

With the entry of Blackwell and the imminent future of Blackwell Ultra and Rubin, NVIDIA continues to redefine expectations for the industry, setting new standards for performance and efficiency in AI data centers.