Throughput Is Not All You Need and more

Item: Throughput Is Not All You Need and more
Rating: 4
Author: PostSingularity Institute

by West Coast Machine Learning

Applied Sciences & Engineering AI — News & Applications UYComputer science UYQArtificial intelligence (AI)

🎙 West Coast Machine Learning 👥 3K 📅 November 7, 2025 ⏱ 68 min 👁 103 📄 expert opinion 🧭 2026-08-16

Available in: English (current) Français

Keywords

throughputgoodputprefilldecodeKV cache

Summary

This meetup video discusses the paper ‘Throughput Is Not All You Need’ and related works on optimizing LLM serving. The presenter, Neha, introduces basic concepts like static, dynamic, and continuous batching, highlighting their limitations for real-time serving. She then explains the distinction between throughput and goodput, where goodput measures completed requests meeting SLOs like TTFT and TPOT. The core argument is that prefill and decode phases have different resource requirements (compute-bound vs memory-bound), and collocating them leads to interference and suboptimal resource utilization. The proposed solution is disaggregated inference, where prefill and decode are separated onto different GPUs, allowing independent scaling and parallelization. The discussion covers challenges like KV cache transfer and placement strategies. The video includes audience questions and clarifications, providing a collaborative exploration of the topic.

128 words

Critical Evaluation

Value of the Information & Strength of the Argument

The video provides valuable insights into LLM serving optimization, particularly the concept of goodput and the rationale for disaggregated inference. The presenter effectively argues that optimizing for throughput alone may not meet user experience requirements, and that separating prefill and decode can improve goodput. The argumentation is solid, supported by examples and figures from the paper, and the discussion with the audience adds depth. However, the video is a discussion rather than a formal presentation, so some points are explored less rigorously.

Scientific Rigor, Source Quality, Title Accuracy

The video references the paper ‘Throughput Is Not All You Need’ and mentions related works, but does not provide specific citations or URLs during the discussion. The description includes links to the meetup group but not to the paper itself. The title accurately reflects the content, focusing on the paper and related topics. The discussion is based on the presenter’s understanding and experience, which is credible but not formally verified.

167 words

Title / Content Match

The title accurately reflects the main topic, focusing on the paper 'Throughput Is Not All You Need' and related works.

Quality & Reliability

7/10

The video is a meetup discussion led by an expert, covering a specific paper and related concepts. It provides a good overview of LLM serving optimizations, but lacks formal citations and rigorous verification. The discussion is insightful but relies on the presenter's interpretation and experience.

Chapters

Cited Sources

East Bay Tri-Valley Machine Learning Meetup — The meetup group where this discussion took place, providing context for the video.

Concurring Sources

Disaggregated Prefill and Decoding for Goodput Optimized LLM Serving — The paper discussed in the video, supporting the concepts of goodput and disaggregated inference.

External References

www.meetup.com

Contribution & Novelties

The video offers a practical perspective on LLM serving, emphasizing the importance of goodput over raw throughput. It explains the trade-offs in batching strategies and the benefits of disaggregated inference. The discussion with the audience adds real-world considerations, such as voice interfaces and chunked prefill.

Pour aller plus loin :

Disaggregated Prefill and Decoding for Goodput Optimized LLM Serving — The paper discussed, providing detailed analysis and experiments.
PagedAttention — Related work on KV cache management, referenced in the discussion.
vLLM — An open-source LLM serving system implementing continuous batching and PagedAttention.

92 words

Radar Profile

The radar profile shows balanced scores across information quantity, quality, and technical level, with slightly lower reliability due to the informal discussion format. The video is informative and technically sound but relies on the presenter's interpretation rather than formal citations.

Reliability 6/10