Description
NVIDIA H200 Tensor Core GPU , the successor to the H100, optimized for AI, HPC, and large-scale data center workloads :
NVIDIA H200 Key Specifications
Feature Specification
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Architecture Hopper (Refresh, same as H100 but with HBM3e memory )
GPU Die GH100 (TSMC 4N process)
FP32 Performance ~67 TFLOPS (similar to H100)
Tensor Cores 4th-gen (Hopper), supports FP8, TF32, FP64
Memory 141GB HBM3e (up from 80GB HBM3 in H100)
Memory Bandwidth 4.8 TB/s (vs. 3.35 TB/s on H100) – 50% faster
Memory Interface 5120-bit (same as H100 but upgraded to HBM3e)
NVLink 4.0 900 GB/s (same as H100, scales to 8 GPUs)
PCIe Version Gen5 x16 (128 GB/s bidirectional)
TDP 700W (SXM5 version) / 350W-400W (PCIe version)
Form Factors SXM5 (for HGX/DGX servers) / PCIe 5.0 (standard servers)
LLM Performance 2x faster than H100 in inference (e.g., Llama2-70B, GPT-4 scale models)
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* Key Improvements Over H100
1. HBM3e Memory
– 141GB capacity (vs. 80GB on H100) → Handles larger AI models without CPU offloading.
– 4.8 TB/s bandwidth (vs. 3.35 TB/s) → Reduces bottlenecks in memory-heavy workloads.
2. Optimized for LLM Inference
– Benchmarks show 2x throughput vs. H100 for models like Llama2-70B and GPT-4 .
– Better context window handling (e.g., 128k+ tokens).
3. Backward Compatibility
– Works in existing HGX H100 systems (no hardware changes needed).
– Same SXM5/PCIe 5.0 form factors as H100.
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Use Cases
– Large Language Models (LLMs) : GPT-4, Gemini, Claude-scale training/inference.
– HPC : Climate modeling, quantum simulation.
– Recommendation Systems : Real-time AI for hyperscalers (AWS, Azure, Google Cloud).
* H200 vs. H100 vs. H100 NVL
Feature H200 H100 H100 NVL |
|——————-|—————|—————|—————-|
Memory | 141GB HBM3e | 80GB HBM3 | 188GB HBM3 |
Bandwidth | 4.8 TB/s | 3.35 TB/s | 3.35 TB/s |
NVLink | 900 GB/s | 900 GB/s | 1.8 TB/s* |
Best For | LLM Inference | General AI | Giant LLMs |
*(H100 NVL has dual-GPU NVLink but slower memory bandwidth per GPU.)*



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