Verified70% confidenceFactExact time
Fine-tuning requires hundreds to thousands of high-quality training samples, hours of GPU compute time, and ongoing maintenance costs, and the entire process must be repeated whenever knowledge is updated.
4
Sources
70%
Confidence
Long-term
Relevance
7/13/2026
First Seen
Sources
零基础理解RAG：检索增强生成原理与应用场景详解
Related Claims
VerifiedThe pre-training process of large language models requires thousands of high-end GPUs running in parallel for months, with costs typically ranging from tens of millions to hundreds of millions of dollars.78% similarUnverifiedLarge reasoning models require the equivalent of approximately 1 billion hours of training data to achieve comparable programming ability, a figure derived by converting the token volume consumed during pre-training at human reading speed.74% similarUnverifiedThe computational gap between pre-training and fine-tuning can be thousands of times, with pre-training consuming thousands of GPU hours while fine-tuning takes hours or minutes74% similarUnverifiedLarge model training cycles last from several months to over a year, with costs starting in the millions of dollars, and once training is complete the knowledge base is fixed and cannot be updated in real time.73% similar
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