Specifications: H100 80GB vs GH200 144GB
The H100 80GB and GH200 144GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 80GB to 144GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The GH200 144GB supports newer technologies like FP8 transformer engines, fourth-gen tensor cores, and higher-bandwidth HBM3e or HBM4 memory.
AI Inference Performance
For LLM inference, the GH200 generally achieves 30-120% higher throughput than the H100 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the H100 serves 1,800-2,400 tok/s while the GH200 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the H100 while the GH200 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the H100 versus 18-35ms for the GH200.
Training Throughput Comparison
On training workloads, the GH200 delivers 40-120% higher throughput for typical model sizes. For a 7B model at BF16 mixed precision, per-GPU throughput reaches 3,500-4,500 tok/s on the H100 versus 5,000-8,000 tok/s on the GH200. Model FLOPS utilization (MFU) ranges from 38-48% on the H100 and 42-55% on the GH200. Memory capacity constraints on the H100 require activation checkpointing for models larger than 13B, while the GH200 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The H100 80GB has 80GB VRAM, while the GH200 144GB has 144GB VRAM. At FP16, a 7B model requires ~14 GB for weights, plus KV cache of ~1.5 GB per 128K context per request. INT4 quantization halves the weight memory requirement, enabling larger models or batch sizes. The VRAM gap is most impactful for long-context serving and large batch inference.
Cloud Pricing and TCO
On-demand cloud pricing for the H100 averages $2.50/hr while the GH200 averages $3.00/hr. However, cost-per-token analysis often favors the GH200 by 15-40% for sustained production workloads due to higher throughput. Reserved 12-month contracts provide 30-50% discounts. For a 64-GPU, 3-year TCO, the H100 cluster costs $796K-$796K while the GH200 cluster costs $657K-$1120K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the H100 for: budget-constrained deployments, models under 13B that fit in available VRAM, batch inference workloads where throughput per dollar is secondary to absolute cost, and development/staging environments. Choose the GH200 for: production serving at scale, models larger than 13B parameters, workloads requiring FP8/FP4 precision, long-context inference beyond 128K tokens, and clusters above 128 GPUs where scaling efficiency matters.