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Large Language Model (LLM) inference consists of two distinct phases - prefill phase which processes the input prompt and decode phase which generates output tokens autoregressively.
Clipper: A { \{ Low-Latency } \} online prediction serving system
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Scaling laws for neural language models
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Mikel Artetxe, Shruti Bhosale, Naman Goyal, Todor Mihaylov, Myle Ott, Sam Shleifer, Xi Victoria Lin, Jingfei Du, Srinivasan Iyer, Ramakanth Pasunuru, Giri Anantharaman, Xian Li, Shuohui Chen, Halil Akin, Mandeep Baines, Louis Martin, Xing Zhou, Punit Singh Koura, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Mona Diab, Zornitsa Kozareva, and Ves Stoyanov · 2022
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Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer · 2022
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Reiner Pope, Sholto Douglas, Aakanksha Chowdhery, Jacob Devlin, James Bradbury, Anselm Levskaya, Jonathan Heek, Kefan Xiao, Shivani Agrawal, and Jeff Dean · 2022
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Emergent abilities of large language models
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Gptq: Accurate post-training quantization for generative pre-trained transformers, 2023
Elias Frantar, Saleh Ashkboos, Torsten Hoefler, and Dan Alistarh · 2023
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Orca: A distributed serving system for Transformer-Based generative models
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Tri Dao · 2023
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Qlora: Efficient finetuning of quantized llms, 2023
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https://aws.amazon.com/codewhisperer/
Amazon codewhisperer
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https://claude.ai
Anthropic claude
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https://www.bing.com/chat
Bing ai
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https://character.ai
Character ai
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Retentive network: A successor to transformer for large language models, 2023
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Fast distributed inference serving for large language models, 2023
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