Unverified50% confidenceFactExact time
The large Transformer model was trained using only 8 GPUs for 3.5 days, while the small version used 12 hours, 100,000 steps, and 65 million parameters.
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9/11/2026
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UnverifiedA developer posted an experiment on Hacker News showing that a small Transformer model trained in just 1.5 hours outperformed many mainstream LLMs with far larger parameter counts on specific tasks.73% similarUnverifiedA developer used a single RTX PRO 6000 and spent 3.5 days training a 210-million-parameter text-to-image diffusion Transformer (DiT) from scratch on 4.2 million images at 256² resolution.69% similarUnverifiedRT-1（2022年）是基于Transformer的端到端策略，使用包含130k条由13台机器人在17个月内采集的演示轨迹训练66% similarUnverifiedA trained neural network surrogate model can produce approximate results within milliseconds, compressing design iterations that would otherwise take years down to weeks or even days.65% similarUnverifiedAgent tasks require multiple model invocations, and a 12B model on a consumer-grade GPU already takes several seconds per inference, causing the time cost of multi-step tasks to accumulate significantly.64% similar
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