從 DeepSpeed 到 FSDP 再切換回來：使用 Hugging Face Accelerate 實現無縫分散式訓練
Original: From DeepSpeed to FSDP and Back Again with Hugging Face Accelerate
In the era of large language models (LLMs), the VRAM of a single GPU is often insufficient to hold models with tens of billions of…
本文探討如何利用 Hugging Face Accelerate 在 DeepSpeed 與 PyTorch FSDP 兩大分散式訓練框架之間無縫切換。這兩者皆是解決大模型（LLM）顯存不足的關鍵技術。透過 Accelerate 的高度抽象化，開發者無需修改核心訓練代碼，僅需調整設定檔即可自由切換，並針對不同硬體環境進行效能微調。文章也分析了兩者的優缺點與適用場景，為 AI 工程師提供實用的架構選擇指南。
In the era of large language models (LLMs), the VRAM of a single GPU is often insufficient to hold models with tens of billions of parameters. To overcome this bottleneck, Microsoft's DeepSpeed (specifically its ZeRO technology) and PyTorch's native FSDP (Fully Sharded Data Parallel) have emerged as the two most widely adopted distributed training solutions today. Both share the same core idea: sharding model parameters, gradients, and optimizer states across multiple GPUs.
Free shows the 3-line summary; Pro unlocks the full deep summary (~300 words) so you never have to click through.
See Pro plans →Want the original English / full article?
Read on Hugging Face Blog →Summaries are AI-generated; the original article is authoritative.