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NVIDIA H100

Accelerators
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NVIDIA's workhorse GPU dominating AI infrastructure

Industry standard for AI training and inference

Metrics

tdp700W
memory gb80
tflops fp161,979
architectureHopper

Score Breakdown

ecosystem96
performance95
availability85

Scoring Methodology

performance45% weight

Raw compute throughput and memory bandwidth

Source: TFLOPS specs, MLPerf benchmarks

availability30% weight

Market supply and cloud instance availability

Source: SemiAnalysis ClusterMAX, cloud pricing pages

ecosystem25% weight

Software stack maturity and framework support

Source: Framework compatibility matrices, developer surveys

Related Signals

B200 Blackwell GPUs Enter Production

Accelerators1d ago

NVIDIA's B200 Blackwell GPUs are shipping to hyperscalers, promising 2.5x performance gains over H100 for AI training workloads.

90

vLLM Adoption Accelerates Across Inference Platforms

Frameworks1d ago

vLLM has become the de facto standard for LLM inference, with major cloud providers and inference platforms adopting it for production deployments.

88

AMD MI300X Gains Enterprise Traction

Accelerators1d ago

AMD's MI300X is seeing increased adoption as enterprises seek alternatives to NVIDIA's supply-constrained GPUs, with 192GB memory enabling larger model deployments.

82

H200 Compatibility Advisory: Framework Updates Required

Accelerators1d ago

NVIDIA H200's 141GB HBM3e memory requires updated CUDA drivers and framework versions. Teams should verify compatibility before migration from H100.

78

Data Sources

Last updated: December 24, 2025