使用 DeepSpeed 與 Accelerate 實現極速 BLOOM 模型推理
Original: Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate
BLOOM is a massive open-source multilingual model with 176 billion parameters. Running BLOOM at FP16 precision requires at least 352 GB of…
Hugging Face 釋出技術指南，針對 1760 億參數的開源巨型模型 BLOOM 提供高效推理方案。透過結合 DeepSpeed-Inference 的張量並行（Tensor Parallelism）與 Accelerate 的彈性部署，解決了超大模型需要超高 VRAM 的痛點。文章提供具體 PyTorch 腳本與基準測試，展示如何在多卡環境下將推理延遲降至最低。
BLOOM is a massive open-source multilingual model with 176 billion parameters. Running BLOOM at FP16 precision requires at least 352 GB of video memory (VRAM), which is far beyond what a single GPU can handle. To help developers and researchers deploy this model efficiently, Hugging Face detailed two primary PyTorch inference optimization approaches:
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