NVIDIA A100 与 H100：2026 年 LLM 训练的全面比较
📄 中文摘要
在训练或微调大型语言模型（LLM）时，选择合适的 GPU 是一个重要决策。NVIDIA 的 A100 和 H100 是行业标准，但哪一款更适合特定需求？该指南详细比较了 A100 和 H100 的技术差异、实际性能和成本影响，帮助用户根据具体使用场景做出选择。A100 提供 40GB 和 80GB 的内存，FLOPS 达到 312 TFLOPS（FP32），功耗在 250-400W 之间；而 H100 则配备 80GB 内存，FLOPS 高达 990 TFLOPS（FP32），功耗在 350-700W 之间。通过这些关键指标的对比，用户可以更清晰地了解两者的优缺点。
📄 English Summary
NVIDIA A100 vs H100: The Complete Comparison for LLM Training in 2026
Choosing the right GPU is a crucial decision when training or fine-tuning a Large Language Model (LLM). NVIDIA's A100 and H100 are industry standards, but determining which one suits specific needs is essential. This guide breaks down the technical differences, real-world performance, and cost implications of both GPUs. The A100 offers 40GB and 80GB memory options, with 312 TFLOPS (FP32) performance and power consumption ranging from 250-400W. In contrast, the H100 features 80GB of memory, achieving 990 TFLOPS (FP32) and consuming 350-700W of power. By comparing these key metrics, users can better understand the advantages and disadvantages of each option.
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数据源: OpenAI, Google AI, DeepMind, AWS ML Blog, HuggingFace 等