English(EN) Full fine-tune vs LoRA vs QLoRA on one L40S: Qwen2.5-1.5B, memory, speed and held-out loss, measured

LoRA 在速度和内存使用方面优于 QLoRA 和全量微调

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 15:15

最近在 L40S GPU 上对 Qwen2.5-1.5B-Instruct 模型进行了三种微调方法的基准测试：全量微调、LoRA 和 QLoRA。全量微调需要最多的内存，在使用 4 的微批次大小时消耗了 36.9 GiB。LoRA 比全量微调更快，内存使用更少，为 14.5 GiB，并实现了略低的留出损失。出人意料的是，QLoRA 比其他两种方法都慢，并且比 LoRA 使用了更多的内存。 AI

影响为基于内存和速度限制选择小型模型的微调方法提供了实用指导。

排序理由基准测试结果，比较了特定模型的不同微调技术。[lever_c_demoted from research: ic=1 ai=1.0]

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Newsworthiness bucket: Tool; 基准测试结果，比较了特定模型的不同微调技术。[lever_c_demoted from research: ic=1 ai=1.0]
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dev.to — LLM tag TIER_1 English(EN) · Runcrate · 2026-10-28 15:15

Full fine-tune vs LoRA vs QLoRA on one L40S: Qwen2.5-1.5B, memory, speed and held-out loss, measured

<p>"Use LoRA, it needs far less memory" and "use QLoRA if LoRA doesn't fit" are the standard advice for fine-tuning on one GPU. I wanted to see what each one costs on a small model, where the weights are not the problem, so I rented an L40S (48 GB) on Runcrate today (2026-10-28) …

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Full fine-tune vs LoRA vs QLoRA on one L40S: Qwen2.5-1.5B, memory, speed and held-out loss, measured

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