memory – Andrew Fairless, Ph.D.

What I Read: GPU Computing

By Andrew Fairless on March 4, 2025November 16, 2024

https://blog.codingconfessions.com/p/gpu-computing What Every Developer Should Know About GPU ComputingAbhinav UpadhyayOct 18, 2023 “GPUs have become incredibly important because of their pervasive use in deep learning. Today, it is essential forContinue readingWhat I Read: GPU Computing

What I Read: Transformers Inference Optimization

By Andrew Fairless on January 27, 2025October 19, 2024

https://astralord.github.io/posts/transformer-inference-optimization-toolset Transformers Inference Optimization ToolsetAleksandr SamarinOct 1, 2024 “Large Language Models are pushing the boundaries of artificial intelligence, but their immense size poses significant computational challenges. As these models grow,Continue readingWhat I Read: Transformers Inference Optimization

What I Read: Ring Attention

By Andrew Fairless on July 8, 2024April 23, 2024

https://coconut-mode.com/posts/ring-attention Ring Attention ExplainedKilian Haefeli, Simon Zirui Guo, Bonnie Li10 Apr 2024 “Context length in Large Language Models has expanded rapidly…. What if we we could use multiple devices toContinue readingWhat I Read: Ring Attention

What I Read: speed up code, Numba

By Andrew Fairless on June 5, 2024April 15, 2024

https://pythonspeed.com/articles/slow-numba The wrong way to speed up your code with Numbaby Itamar Turner-Trauring21 Mar 2024 “If your NumPy-based code is too slow, you can sometimes use Numba to speed itContinue readingWhat I Read: speed up code, Numba

What I Read: binary vector search

By Andrew Fairless on June 4, 2024April 15, 2024

https://blog.pgvecto.rs/my-binary-vector-search-is-better-than-your-fp32-vectors My binary vector search is better than your FP32 vectorsBinary Vector Search: The 30x Memory Reduction Revolution with Preserved AccuracyCe GaoMar 22, 2024 “…binary vector search… shows promise inContinue readingWhat I Read: binary vector search

What I Read: 1-bit LLMs, 1.58 Bits

By Andrew Fairless on May 14, 2024March 11, 2024

https://arxiv.org/abs/2402.17764 The Era of 1-bit LLMs: All Large Language Models are in 1.58 BitsShuming Ma, Hongyu Wang, Lingxiao Ma, Lei Wang, Wenhui Wang, Shaohan Huang, Li Dong, Ruiping Wang, JilongContinue readingWhat I Read: 1-bit LLMs, 1.58 Bits

What I Read: Forgetting Can Help AI Learn

By Andrew Fairless on April 29, 2024March 1, 2024

https://www.quantamagazine.org/how-selective-forgetting-can-help-ai-learn-better-20240228/ How Selective Forgetting Can Help AI Learn BetterAmos Zeeberg2/28/24 10:38 AM “Erasing key information during training results in machine learning models that can learn new languages faster and moreContinue readingWhat I Read: Forgetting Can Help AI Learn

What I Read: Scaling ChatGPT, Engineering Challenges

By Andrew Fairless on April 18, 2024March 1, 2024

https://newsletter.pragmaticengineer.com/p/scaling-chatgpt Scaling ChatGPT: Five Real-World Engineering ChallengesGergely OroszFeb 20, 2024 “Just one year after its launch, ChatGPT had more than 100M weekly users. In order to meet this explosive demand,Continue readingWhat I Read: Scaling ChatGPT, Engineering Challenges

What I Read: Helping AI See

By Andrew Fairless on January 23, 2024November 20, 2023

https://www.quantamagazine.org/the-computing-pioneer-helping-ai-see-20231024/ The Computing Pioneer Helping AI SeeSusan D’AgostinoOctober 24, 2023 ““Patches of color and brightness require us to connect what we’re seeing now to our memory…”… machines see what isContinue readingWhat I Read: Helping AI See

What I Read: LLM Agents

By Andrew Fairless on August 17, 2023July 9, 2023

https://lilianweng.github.io/posts/2023-06-23-agent/ LLM Powered Autonomous AgentsLilian WengJune 23, 2023 “Building agents with LLM (large language model) as its core controller is a cool concept…. it can be framed as a powerfulContinue readingWhat I Read: LLM Agents

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