In the accelerating race of artificial intelligence, NVIDIA once again steps up as a driving force with the release of their groundbreaking NVIDIA HGX™ H200. This power-packed entrant is more than just a technological marvel; it’s a transformative leap for AI training, harnessing the robust NVIDIA Hopper™ architecture. The NVIDIA H200 Tensor Core GPU, the pulsing heart of the platform, boasts advanced memory capabilities that redefine the processing of gargantuan data sets for generative AI applications and high performance computing (HPC) tasks.
The introduction of NVIDIA H200 marks a historic milestone as it’s the inaugural GPU to integrate HBM3e memory — a leapfrogging enhancement in speed and size that’s primed to turbocharge generative AI and vast language algorithms, propelling scientific progress in HPC domains. Equipped with 141GB of memory that operates at a blistering 4.8 terabytes per second, the NVIDIA H200 doesn’t just outpace its predecessor, the NVIDIA A100; it skyrockets nearly doubling its capacity and offering a bandwidth magnification of 2.4 times.
Joining forces with the upper echelons of server production and cloud computing giants, systems powered by the NVIDIA H200 are queued to revolutionize the tech landscape, starting shipments in the second quarter of 2024. Ian Buck, NVIDIA’s leading light in hyperscale and HPC, encapsulates the vision: to engender intelligence through generative AI and HPC applications, it is imperative to channel colossal data streams efficiently at unmatched velocities leveraging expansive, rapid GPU memory. By enriching the industry’s pinnacle AI supercomputing platform with NVIDIA H200, solving the perplexities of the world’s most critical challenges just accelerated into a swifter dimension. Join us, as we delve into the era where the interplay between data and speed shapes our future, right here, on our blog.
What this means for the future of AI?
The NVIDIA HGX™ H200, based on the NVIDIA Hopper™ architecture, features the NVIDIA H200 Tensor Core GPU with advanced memory to handle massive amounts of data for generative AI and high-performance computing workloads. The introduction of the H200 is expected to lead to further performance leaps, including nearly doubling inference speed on large language models compared to its predecessor, the H100. The H200 is the first GPU to offer HBM3e, which provides faster, larger memory to fuel the acceleration of generative AI and large language models, as well as advancing scientific computing for HPC workloads. This advancement in processing power could accelerate progress in AI capabilities by:
- Allowing for the training of larger and more complex AI models.
- Enabling faster inference speed on large language models.
- Advancing scientific computing for HPC workloads.
The H200-powered systems are expected to begin shipping in the second quarter of 2024, and will be available from global system manufacturers and cloud service providers
Conclusion
Nvidia’s new H200 chip represents a major advancement in hardware capabilities for training sophisticated AI models. With its enormous processing power, massive memory, and advanced architecture, the H200 enables companies and researchers to train AI models of unprecedented size and complexity. This could drive leaps forward in many AI applications across industries.
While still carrying a high price tag, the H200 delivers improved performance and efficiency over previous solutions for premier AI training. Its lower energy consumption also reduces the operating costs of AI workloads. With Nvidia’s full-stack AI software ecosystem supporting deployment, the H200 makes state-of-the-art model training more attainable.
When the H200 becomes available in 2024 from system manufacturers and cloud providers, it will put exceptionally powerful AI training abilities into more hands. There is no doubt the H200 will equip organizations to develop game-changing AI that drives innovation, efficiency, and insights. By empowering the training of sophisticated models, Nvidia’s new chip shapes the future trajectory of AI capabilities across the board.
