Verified90% confidenceFactTime unknown
The 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.
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90%
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Long-term
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6/1/2026
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VerifiedFine-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.78% similarUnverifiedLarge-scale pretraining often requires thousands of high-end GPUs (such as NVIDIA H100) running continuously for months, with the training cluster's power consumption reaching tens of megawatts.77% 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 minutes68% similarUnverifiedIn the training of hundred-billion-parameter large models, inter-node communication overhead can account for 30%–50% of total training time.68% similar
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