Specifications: GH200 144GB vs H100 80GB
The GH200 144GB and H100 80GB represent different generations of GPU architecture for AI workloads. Memory capacity ranges from 144GB to 80GB, with significant differences in memory bandwidth, compute throughput (FP8/FP16/TF32), NVLink connectivity, and TDP. The H100 80GB 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 H100 generally achieves 30-120% higher throughput than the GH200 depending on model size and batch configuration. For a 7B model at FP8 with continuous batching, the GH200 serves 1,800-2,400 tok/s while the H100 reaches 3,000-5,000 tok/s. For 70B models, tensor parallelism across 4 GPUs is needed on the GH200 while the H100 may serve the same model with 2 GPUs due to higher VRAM. Prefill latency for 4K tokens ranges from 35-55ms for the GH200 versus 18-35ms for the H100.
Training Throughput Comparison
On training workloads, the H100 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 GH200 versus 5,000-8,000 tok/s on the H100. Model FLOPS utilization (MFU) ranges from 38-48% on the GH200 and 42-55% on the H100. Memory capacity constraints on the GH200 require activation checkpointing for models larger than 13B, while the H100 accommodates larger models without checkpointing.
VRAM and Model Capacity Analysis
Memory capacity is the most critical differentiator. The GH200 144GB has 144GB VRAM, while the H100 80GB has 80GB 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 GH200 averages $3.00/hr while the H100 averages $2.50/hr. However, cost-per-token analysis often favors the H100 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 GH200 cluster costs $440K-$663K while the H100 cluster costs $691K-$1006K including hardware, power, cooling, and maintenance.
Which GPU Should You Choose?
Choose the GH200 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 H100 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.