AI training efficiency: From Throughput to Goodput

Pretraining a modern large language model (LLM), often with ~100B parameters or more, typically involves thousands of accelerators and massive token corpora, running for days to months. At that scale,

Sector: Electronic Labour | Confidence: 99%
Source: https://thenextweb.com/news/ai-training-efficiency-from-throughput-to-goodput

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Council (3 models): The AI training landscape shifts from prioritizing raw computational throughput to optimizing 'goodput'—the quality and reliability of model outputs. This transition reframes the economic value of electronic labour, driving demand for specialized infrastructure, regulatory scrutiny, and new insurance products to mitigate risks like 'badput.' Finance and real infrastructure sectors adapt by valuing efficiency and reliability over sheer scale, while the industry matures with metrics that emphasize deployable AI over computational effort.
Cross-sector: Finance, Insurance, Real Infrastructure

  ? How are organizations defining and quantifying 'goodput' in practical, measurable terms across diverse AI applications?
  ? What new infrastructure and resource allocation strategies emerge to optimize for 'goodput' rather than raw computational throughput?
  ? How do existing financial models for AI infrastructure valuation adjust to goodput-based metrics?

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