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Posted: 2025-04-13 17:45:10 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-04-13 17:46:21 UTC
Verified By
Rollup News
The article discusses the under-appreciated need for more compute for inference on foundation models, especially in agentic workflows where LLMs are prompted repeatedly. It highlights the importance of fast token generation and the decreasing costs of both training and inference due to improvements in semiconductors and algorithms.
The demand for compute for inference on foundation models is significant and often underestimated.
Fast token generation is crucial for efficient agentic workflows.
Training and inference costs are rapidly decreasing, benefiting application builders and AI agentic workflows.
Slower token generation can be a bottleneck in taking better advantage of existing foundation models.
Running evaluations (evals) can be slow and expensive.