H100 vs H200: Which NVIDIA GPU Is Right for Your Workload?
Memory Makes the Difference
At first glance, the H100 and H200 appear nearly identical. Both are built on the Hopper architecture with 80 streaming multiprocessors, fourth-generation Tensor Cores, and a Transformer Engine optimized for large language models. The critical difference lies in memory: the H100 uses HBM3 rated at 3.35 TB/s across 80 GB, while the H200 upgrades to HBM3e delivering 4.8 TB/s across 141 GB of VRAM.
This 43% increase in memory bandwidth and 76% increase in capacity fundamentally changes which workloads fit on a single GPU. Models that previously required tensor parallelism across multiple H100s can now reside entirely within a single H200. For inference, the larger H200 memory means higher batch sizes and lower latency, while for training, the additional bandwidth reduces the time spent waiting on attention computation memory fetches.
- H100: 80 GB HBM3 at 3.35 TB/s vs H200: 141 GB HBM3e at 4.8 TB/s
- Identical compute: 1979 TFLOPS FP8 sparse on both architectures
- H200 supports up to 1.5x larger model parameters on a single GPU
- H200 inference throughput is 1.3-1.8x higher for Llama 3 and GPT-class models
- H100 remains competitive for compute-bound workloads like molecular dynamics
Real-World Performance Benchmarks
In our testing at Servchip's reference architecture lab, the H200 demonstrated a 50% reduction in time-to-first-token for Llama 3 70B inference compared to H100, purely due to the memory bandwidth advantage during the prefill phase. For throughput-bound decoding, the H200 achieves 1.4x more tokens per second, enabling lower-latency chat applications and real-time AI assistants.
Training large diffusion models such as Stable Diffusion 3 and Sora-class video models showed a 1.25x speedup on H200 clusters, as the larger HBM capacity reduces the frequency of gradient checkpointing and enables larger micro-batch sizes. However, for FP8 training of smaller models under 7B parameters, the performance delta narrows to single digits, making H100 the more cost-effective choice.
- Llama 3 70B inference: 1.4x tokens/sec improvement on H200 over H100
- Training throughput: 1.2-1.3x for diffusion models, 1.1x for small LLMs
- H200 enables 70B parameter inference on a single GPU without model parallelism
- For FP8 training of sub-7B models, H100 offers better price-performance ratio
Pro Tip
For mixed-workload deployments, consider an H100 + H200 heterogeneous cluster. Route memory-bandwidth-sensitive inference tasks to H200 nodes and compute-bound training jobs to H100 nodes to optimize total cost of ownership.
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