Edge 410: Learn About Virtual Token Counter: A Novel Method that Address One of the Major Challenges LLM Serving
Was this email forwarded to you? Sign up here Edge 410: Learn About Virtual Token Counter: A Novel Method that Address One of the Major Challenges LLM ServingCreated by UC Berkeley and Stanford University, VTC introduced a fairness in LLM serving scheduling
Imagine the following scenarios in an LLM application:
Should the requests from both clients follow be served by the same LLM resources. The answer seems obviously no as the second client requires much less resources than the first client. However, today’s LLM infrastructures do not differentiate between the two types of requests. This is come to be known as fair serving as is the subject of a fascinating paper by a list of rock star researchers that includes UC Berkeley’s Joseph Gonzalez and Ion Stoica as well as researchers from Stanford University and Duke University. Current LLM serving systems rely on a commonly used method for handling incoming requests based on the First-Come-First-Serve (FCFS) approach. However, this method is not without its problems. ... Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Making agents remember beyond the context window. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Plus a tremendous activity in funding activity in generative AI startups. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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Edge 406: Inside OpenAI's Recent Breakthroughs in GPT-4 Interpretability
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A new method helps to extract interpretable concepts from large models like GPT-4. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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