Introduction
Our mCloud platform utilises NVIDIA H200 GPUs - based on the NVIDIA Hopper architecture - which is the first GPU to offer 141 gigabytes (GB) of HBM3e memory at 4.8 terabytes per second (TB/s). That’s nearly double the capacity of the NVIDIA H100 GPU with 1.4X more memory bandwidth.
The H200’s larger and faster memory accelerates generative AI and LLMs, while advancing scientific computing for HPC workloads with better energy efficiency and lower total cost of ownership.
Features
Learn about the next massive leap in accelerated computing with the NVIDIA Hopper™ architecture. Hopper securely scales diverse workloads in every data center, from small enterprise to exascale high-performance computing (HPC) and trillion-parameter AI—so brilliant innovators can fulfill their life's work at the fastest pace in human history.
With the introduction of H200, energy efficiency and TCO reach new levels. This cutting-edge technology offers unparalleled performance, all within the same power profile as the H100 Tensor Core GPU. AI factories and supercomputing systems that are not only faster but also more eco-friendly deliver an economic edge that propels the AI and scientific communities forward.
The NVIDIA H200 NVL is the ideal choice for customers with space constraints within the data center, delivering acceleration for every AI and HPC workload regardless of size. With a 1.5X memory increase and a 1.2X bandwidth increase over the previous generation,customers can fine-tune LLMs within a few hours and experience LLM inference 1.8X faster.
Memory bandwidth is crucial for HPC applications, as it enables faster data transfer and reduces complex processing bottlenecks. For memory-intensive HPC applications like simulations, scientific research, and artificial intelligence, the H200’s higher memory bandwidth ensures that data can be accessed and manipulated efficiently, leading to 110X faster time to results.
In the ever-evolving landscape of AI, businesses rely on large language models to address a diverse range of inference needs. An AI inference accelerator must deliver the highest throughput at the lowest TCO when deployed at scale for a massive user base. The H200 doubles inference performance compared to H100 GPUs when handling large language models such as Llama2 70B.
Conclusion
Our mCloud platform, built on robust OpenStack architecture, now offers the ability to integrate powerful NVIDIA GPUs directly into your virtual machines through GPU passthrough technology. This allows virtual machines to access the full capabilities of a physical GPU as if it were directly attached to the system, bypassing the hypervisor’s emulation layer and providing near-native performance.
Enhance your cloud capabilities with GPU acceleration by integrating dedicated GPUs into your mCloud virtual machines and take your computing to the next level.
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