What is the memory bandwidth of the H200 GPU?

The NVIDIA H200 GPU features a memory bandwidth of 4.8 TB/s, utilizing 141 GB of HBM3e memory. This specification positions Cyfuture AI's GPU as a Service offerings at the forefront for handling demanding AI and HPC workloads.?

Understanding H200 Memory Bandwidth

Memory bandwidth measures the rate at which data transfers between the GPU's memory and its compute units, expressed in terabytes per second (TB/s). For the H200, this reaches 4.8 TB/s thanks to its advanced HBM3e memory technology, which consists of six high-density stacks operating at effective speeds around 6.25 Gbps.?

Cyfuture AI leverages this capability in its cloud-based GPU as a services to accelerate large language model (LLM) training and inference, where high bandwidth reduces data bottlenecks and boosts throughput by up to 1.4x compared to the H100's 3.35 TB/s. In practical terms, this means faster processing of memory-intensive tasks like generative AI simulations or scientific computations, enabling Cyfuture AI customers to achieve results up to 110x quicker than CPU-based systems.?

The H200's design, built on NVIDIA's Hopper architecture, pairs this bandwidth with features like NVLink interconnects at 900 GB/s, making it ideal for multi-GPU clusters in Cyfuture AI's scalable infrastructure. For content professionals optimizing AI-driven workflows, this translates to handling larger datasets without latency, supporting applications from model fine-tuning to real-time inference.?

Conclusion

Cyfuture AI integrates the H200 GPU's 4.8 TB/s memory bandwidth into its GPU as a Service platform, delivering unmatched performance for AI innovation and HPC demands. This empowers users to scale complex workloads efficiently, reducing costs and time-to-insight while maintaining energy efficiency. Choose Cyfuture AI for reliable access to this cutting-edge technology tailored for enterprise needs.?

Follow-up Questions & Answers

• How does the H200's bandwidth compare to the H100?
  The H200 offers 4.8 TB/s, a 43% increase over the H100's 3.35 TB/s, enabling better handling of larger models and longer contexts.?
• What memory type powers the H200's bandwidth?
  It uses 141 GB of HBM3e memory across six stacks, providing higher density and speed than the H100's HBM3.?
• Is the H200 available via Cyfuture AI services?
  Yes, Cyfuture AI offers H200 GPUs in cloud clusters, optimized for AI training, inference, and HPC with flexible scaling.?
• What workloads benefit most from 4.8 TB/s bandwidth?
  LLMs like Llama2, generative AI, and simulations see up to 2x inference speed gains and reduced bottlenecks.?
• Does bandwidth affect power efficiency?
  The H200 maintains efficiency up to 700W TDP, balancing high throughput with lower total cost of ownership.?