LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale

Authors: Tim Dettmers, Mike Lewis, Younes Belkada, Luke Zettlemoyer (2022)

arXiv: 2208.07339

Domains

Inference

TLDR (English)

Reveals "emergent outliers" in large model activations and proposes mixed-precision solution. Core work behind bitsandbytes library, first enabling 175B models to fit in 8 A100s.

TLDR（中文）

揭示大模型激活中的"emergent outliers"，并提出混合精度方案。bitsandbytes 库背后的核心工作，让 175B 模型第一次能塞进 8 卡 A100。

Appears in These Articles

• KV Cache 与量化：让大模型跑得更快
• KV Cache and Quantization: Making Large Models Faster

Co-cited Papers

These papers appear in the same articles as this one

• QLoRA: Efficient Finetuning of Quantized LLMs Tim Dettmers et al. (2023)
• GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers Elias Frantar et al. (2022)
• Efficient Memory Management for Large Language Model Serving with PagedAttention Woosuk Kwon et al. (2023)
• AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration Ji Lin et al. (2023)

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