NVIDIA H200 NVL PCIe — Full Datasheet Specifications
All figures from the official NVIDIA H200 NVL PCIe product datasheet. Performance values with sparsity assume 2:4 structured sparse format. TFLOPS figures are peak theoretical rates based on GPU Boost clock.
Source: NVIDIA H200 NVL PCIe Datasheet (DS-10581-001_v01). All trademarks are the property of NVIDIA Corporation.
H200 vs H100 vs A100 PCIe
Source: NVIDIA datasheets. H200 PCIe NVL spec (DS-10581-001_v01), H100 PCIe (DS-10167-001), A100 PCIe 80GB (DS-10010-001).
When to Choose the H200 NVL PCIe
The H200 PCIe is best justified when memory capacity and bandwidth are the primary bottleneck — not raw compute. If model size exceeds 80GB or memory bandwidth limits token throughput, the H200 NVL is the PCIe-format answer.
LLM Inference — 70B+ Models
141GB HBM3e enables single-card inference of 70B parameter models (Llama 3 70B, Mistral 70B) at full precision. No model partitioning or tensor parallelism needed at 70B scale.
Multi-Modal AI
Large vision-language models (LLaVA, Flamingo, CogVLM) require GPU memory beyond 80GB. H200 PCIe handles multi-modal transformers without multi-GPU tensor splitting at moderate batch sizes.
LLM Training
4.8 TB/s memory bandwidth reduces data-starvation bottlenecks in transformer forward and backward passes. Critical for attention head computation and gradient accumulation.
AI Inference Serving
MIG partitioning creates up to 7 isolated 14GB GPU instances for serving multiple concurrent AI models with guaranteed QoS — isolating workloads between tenants or services.
Scientific HPC
67 TFLOPS FP64 Tensor Core performance accelerates molecular dynamics, climate simulation, and computational chemistry workloads that require double-precision accuracy.
Data Analytics
RAPIDS cuDF and cuML leverage HBM3e bandwidth for GPU-accelerated Spark, pandas-equivalent data frames, and ML training on large tabular datasets without CPU bottlenecks.
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