How do I compare H100 and H200 performance?

The NVIDIA H200 outperforms the H100 primarily due to its superior memory capacity (141 GB HBM3e vs. 80 GB HBM3) and bandwidth (4.8 TB/s vs. 3.35 TB/s), delivering up to 45% higher throughput in AI inference benchmarks like Llama 2 70B (31,712 tokens/s offline vs. 22,290) and 37% in server scenarios. Compute performance in FP8, FP16, and TF32 remains similar at around 3,958 TFLOPS, 1,979 TFLOPS, and 989 TFLOPS respectively, making H200 ideal for memory-intensive LLM training and inference on Cyfuture AI's GPU clusters. To compare on Cyfuture AI, select H100 or H200 clusters via their GPU-as-a-Service dashboard for workload-specific benchmarks and scalable deployment.?

Key Specifications Comparison

Both H100 and H200 GPUs share the NVIDIA Hopper architecture with identical core compute specs, but H200 excels in memory for handling larger models without partitioning. H200's 76% more VRAM and 43% higher bandwidth reduce latency in generative AI and HPC tasks, enabling up to 2x faster LLM inference and 110x HPC gains over prior generations.?

Cyfuture AI offers both in high-performance clusters for AI/ML workloads, allowing seamless testing of these specs.?

Performance Benchmarks Breakdown

In MLPerf Inference v4.0 on Llama 2 70B (8x GPUs), H200 achieves 42.4% higher offline throughput (31,712 vs. 22,290 tokens/s) and 37.3% in server scenarios (29,526 vs. 21,504 tokens/s), driven by memory advantages. vLLM benchmarks show H200 at 124.93 req/s and 17,434 tok/s total throughput vs. H100's 110.42 req/s and 15,416 tok/s. For HPC, H200 delivers ~17% better performance than H100 in simulations, doubling A100 results.?

Cyfuture AI's GPU clusters with H100/H200 optimize these gains for deep learning, inference-as-a-service, and scalable analytics, minimizing TCO through efficient resource allocation. Real-world tests confirm H200's edge in long-sequence models like Llama 3.2 90B, fitting entirely in its VRAM.?

Cyfuture AI Deployment Benefits

Cyfuture AI integrates H100 and H200 in customizable GPU clusters, supporting AI training, fine-tuning, and inference without upfront hardware costs. Users access via flexible plans for variable workloads, leveraging NVLink for multi-GPU scaling and MIG for secure partitioning (up to 7 instances at 16.5 GB each on H200). This setup ensures peak efficiency for LLMs, data analytics, and simulations, with H200 preferred for memory-bound tasks.?

Choose H100 for cost-sensitive general AI; opt for H200 on Cyfuture AI for 1.5-2x throughput in large models, reducing cluster size needs.?

Conclusion
Cyfuture AI empowers users to compare and deploy H100 vs. H200 effectively through its GPU-as-a-Service, highlighting H200's memory-driven superiority for cutting-edge AI while H100 remains robust for balanced workloads—all backed by scalable, reliable infrastructure. Select based on model size and bandwidth demands for optimal performance.?

Follow-up Questions & Answers

• What workloads benefit most from H200 on Cyfuture AI?
  Large LLMs (e.g., Llama 70B+), long-context inference, and HPC simulations, where 141 GB VRAM handles massive datasets without offloading.?
• How does pricing compare for H100 vs. H200 clusters?
  H200 costs more due to specs but offers better value via higher utilization; Cyfuture AI provides flexible hourly/monthly plans—check dashboard for quotes.?
• Can I benchmark H100/H200 myself on Cyfuture AI?
  Yes, spin up on-demand clusters with tools like MLPerf or vLLM for direct comparison, supported by high-speed NVLink interconnects.?
• Is H200 available in PCIe form on Cyfuture AI?
  Primarily SXM for clusters, but NVL (PCIe) options exist with 141 GB/4.8 TB/s; confirm via Cyfuture AI support for configurations.