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NVIDIA H100 vs H200: Which GPU for AI Workloads?
Complete comparison of NVIDIA H100 and H200 GPUs — performance, memory, pricing, and use cases.
Overview
The NVIDIA H100 and H200 are both built on the Hopper architecture, but the H200 offers significant improvements in memory capacity (141GB vs 80GB) and bandwidth (4.8 TB/s vs 3.35 TB/s). This comparison helps you choose the right GPU for your AI workloads.
NVIDIA H100
Pros
- Proven at scale (in production since 2023)
- Lower cost per GPU
- Wide software ecosystem support
- 80GB HBM3 sufficient for most workloads
Cons
- Limited memory for very large models
- Lower memory bandwidth than H200
- Being phased out by newer GPUs
Key Specs
ArchitectureHopper
Memory80GB HBM3
Bandwidth3.35 TB/s
FP8 Performance3,958 TFLOPS
FP16 Performance1,979 TFLOPS
TDP700W
InterconnectNVLink 4.0 (900 GB/s)
NVIDIA H200
Pros
- 141GB HBM3e memory — 1.76x more than H100
- 4.8 TB/s bandwidth — 1.43x faster
- 1.9x faster LLM inference
- Better for trillion-parameter models
Cons
- Higher cost per GPU
- Limited availability (newer)
- May be overkill for smaller workloads
Key Specs
ArchitectureHopper (enhanced)
Memory141GB HBM3e
Bandwidth4.8 TB/s
FP8 Performance3,958 TFLOPS
FP16 Performance1,979 TFLOPS
TDP700W
InterconnectNVLink 4.0 (900 GB/s)
Verdict
Choose H100 for cost-effective AI training and inference on models up to 70B parameters. Choose H200 for training and serving very large models (70B+ parameters) where memory capacity and bandwidth are critical. Harch Corp offers both H100 and H200 GPU cloud instances.