The NVIDIA H100 and H200 are both high-performance GPUs based on NVIDIA’s Hopper architecture, designed for AI, high-performance computing (HPC), and data-intensive workloads. However, the H200 introduces several key upgrades over the H100, particularly in memory and performance efficiency. Here’s a detailed breakdown of the differences:
1. Memory Specifications
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- H100:
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- Memory Type: HBM3
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- Memory Capacity: 80 GB (SXM variant) or 94 GB (PCIe variant, though less common)
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- Memory Bandwidth: 3.35 TB/s
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- H100:
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- H200:
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- Memory Type: HBM3e (an enhanced version of HBM3)
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- Memory Capacity: 141 GB (SXM variant)
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- Memory Bandwidth: 4.8 TB/s
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- H200:
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- Difference: The H200 offers nearly double the memory capacity (141 GB vs. 80 GB) and a significant bandwidth increase (4.8 TB/s vs. 3.35 TB/s, a 1.4x improvement). This makes the H200 much better suited for memory-intensive tasks like training large language models (LLMs) or running complex simulations, as it can store and process larger datasets directly on the GPU without relying on slower system memory.
2. Performance
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- H100:
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- FP8 Tensor Performance: Up to 3,958 TFLOPS
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- FP16 Tensor Performance: Up to 1,979 TFLOPS
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- FP64 Tensor Performance: Up to 60 TFLOPS
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- Transformer Engine: Optimized for mixed-precision (FP8/FP16) AI workloads, offering up to 9x faster training and 30x faster inference compared to the prior A100.
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- H100:
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- H200:
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- FP8 Tensor Performance: Up to 3,958 TFLOPS (same as H100)
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- FP16 Tensor Performance: Up to 1,979 TFLOPS (same as H100)
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- FP64 Tensor Performance: Up to 60 TFLOPS (same as H100)
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- Transformer Engine: Enhanced version with better optimization for LLMs, delivering up to 2x faster inference on models like Llama 2 70B compared to the H100.
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- H200:
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- Difference: While raw computational performance (TFLOPS) remains identical across FP8, FP16, and FP64 precisions, the H200’s improved memory and bandwidth enable real-world performance gains. For example, NVIDIA claims the H200 achieves up to 45% better performance in generative AI and HPC benchmarks, and up to 110x faster results in specific HPC tasks (e.g., MILC quantum simulations) compared to CPUs, outpacing the H100 thanks to its superior data throughput.
3. Energy Efficiency
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- H100: Power consumption around 700W (SXM variant), with strong efficiency for its performance class but no specific energy reduction highlights.
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- H200: Also around 700W (SXM variant), but NVIDIA states it uses up to 50% less energy per inference workload for LLMs compared to the H100, due to faster processing and reduced idle time.
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- Difference: The H200 is more energy-efficient for certain tasks (like LLM inference), lowering the total cost of ownership (TCO) by up to 50% over its lifetime. This is critical for data centers running large-scale AI models continuously.
4. Architectural Enhancements
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- H100: Introduced the Hopper architecture with features like:
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- Transformer Engine for AI acceleration.
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- Multi-Instance GPU (MIG) for partitioning.
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- NVLink 4.0 with 900 GB/s bandwidth for multi-GPU setups.
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- Asynchronous execution for overlapping compute and data transfers.
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- H100: Introduced the Hopper architecture with features like:
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- H200: Builds on the same Hopper architecture but adds:
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- HBM3e memory (first GPU to use it), improving memory speed and capacity.
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- Optimized Tensor Cores and Transformer Engine for better handling of larger, more complex AI models.
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- Enhanced compatibility with newer AI frameworks and distributed training setups.
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- H200: Builds on the same Hopper architecture but adds:
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- Difference: The H200 refines the H100’s architecture rather than overhauling it, focusing on memory and efficiency upgrades to meet the growing demands of next-gen AI workloads.
5. Use Cases and Performance Impact
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- H100: Excellent for current AI training, inference, and HPC tasks. It’s widely adopted for workloads like GPT-3 training or scientific simulations, balancing performance and cost.
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- H200: Tailored for next-generation AI and HPC, particularly excelling in:
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- Generative AI: Up to 2x faster inference on LLMs (e.g., Llama 2 70B).
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- HPC: Faster processing of large datasets (e.g., 1.7x H100 performance in mixed HPC workloads).
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- Memory-Intensive Tasks: Ideal for models exceeding hundreds of billions of parameters, thanks to its larger VRAM and bandwidth.
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- H200: Tailored for next-generation AI and HPC, particularly excelling in:
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- Difference: The H200 is a step ahead for cutting-edge applications, while the H100 remains a robust choice for slightly less demanding or cost-sensitive workloads.
6. Availability and Cost
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- H100: Released in 2022, widely available now, with pricing starting around $29,000 (though it can reach $120,000 in full server configs).
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- H200: Announced in November 2023, shipping began in Q2 2024. It’s expected to carry a premium price due to its enhancements, though exact costs vary by vendor and configuration.
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- Difference: The H200 is newer and more expensive, aimed at organizations needing top-tier performance, while the H100 offers better value for current deployments.
Summary
The H200 is an evolution of the H100, not a complete redesign. Its standout differences are the HBM3e memory (141 GB vs. 80 GB), higher bandwidth (4.8 TB/s vs. 3.35 TB/s), and improved efficiency/performance for large-scale AI and HPC tasks. If you’re working with massive models or need future-proofing, the H200 is the better pick. For existing workloads or budget constraints, the H100 still delivers exceptional power. Both are beasts, but the H200 pushes the boundaries further for the AI-driven future.