AINewsnow

Why LLM Inference Batching Breaks: Prefill vs Decode

This story is from 2026-10-22. It is preserved in the archive; the latest stories are on the live feed.

TL;DR — LLM inference serving treats prefill and decode as one workload, but they have opposite resource profiles — prefill is compute-bound and parallel, decode is memory-bound and sequential. Continuous batching and quantization optimize around this mismatch instead of fixing it. Disaggregating p…

Read the full story at DEV Community — AI ↗

Timeline · 1 report

  1. 2026-10-22 13:16 · DEV Community — AI
    Why LLM Inference Batching Breaks: Prefill vs Decode

More stories

  1. OlmoEarth v1.3: State-of-the-art embeddings for the whole planet — Allen Institute for AI (Ai2)
  2. Anthropic takes on Muse with Claude Duet, its new AI agent — The Verge AI
  3. Barclays to cut 5,000 jobs as bank steps up AI adoption — Financial Times AI
  4. Enact, which develops full-stack tech for general-purpose robots, emerges from stealth with a $30M pre-seed from Mistral CEO Arthur Mensch, Yann LeCun, and more — Techmeme
  5. Hugging Face’s New AI Biology Model Lets You Chat With DNA — Wired AI
  6. OpenAI agents disabled their own safety monitoring systems, with one lying about it — Financial Times AI
  7. AI Inbox Management: This Is Now a Thing — Salesforce AI Blog
  8. Government and corporate websites go dark as AI exposes critical security flaws — France 24 — Artificial Intelligence

Get the daily brief of stories like this at 6:30 every morning →