prefill-decode

Key Points:

  • Disaggregating prefill and decode moves heavy prompt processing to one set of machines and token-by-token generation to another, reducing resource contention and making outputs smoother.
  • The trade-off is a slightly longer time to first token, but responses are steadier, with fewer stutters in long answers and better performance under concurrent load.
  • This change highlights that deployment and infrastructure choices—how models are run—matter as much as model size for everyday AI reliability and user experience.
Good morning, this is Haru, and today is 2025-08-22; on this day in history, the first photograph of Earth from space was transmitted back in 1966, a quiet reminder of how far technology has carried us, and with that thought, let’s turn to today’s news in AI.

Prefill and decode, LLMs

If you’ve ever felt that modern AI systems are a bit like impatient colleagues—eager to answer your question but constantly pausing mid-sentence—you’re not far off. This week, Perplexity, one of the more ambitious players in the AI space, announced a technical update that could make those pauses less awkward. The company has been experimenting with something called “disaggregated prefill and decode,” a behind-the-scenes adjustment that may sound obscure but has very real implications for how smoothly large language models (LLMs) respond to us.

Prefill decode and deployment

At its core, the news is about separating two stages of how an AI generates text. When you type a prompt into a chatbot, the system first has to digest it (that’s the “prefill” stage), and then it begins producing words one by one (the “decode” stage). The problem is that these two jobs don’t play nicely when they share the same hardware. Prefill is heavy lifting—it chews through thousands of tokens at once—while decode is more delicate, handling just one token at a time. When they run together on the same machine, prefill tends to hog resources, leaving decode sluggish and uneven.

AI infrastructure and decode

Perplexity’s solution is simple in concept: split them up. By assigning prefill tasks to one set of machines and decode tasks to another, each can do its job without tripping over the other. The trade-off is that this separation adds a slight delay before you see the very first word appear on your screen—the so-called “time to first token.” But once that initial wait is over, responses flow more steadily and predictably. In practice, this means fewer stutters in long answers and better performance when many users are asking questions at once.

LLMs and deployment

This development fits neatly into a broader trend in AI infrastructure: companies are learning that raw model size isn’t everything. The way these models are deployed—the plumbing beneath the surface—matters just as much for user experience. Over the past year we’ve seen various strategies emerge: some focus on compressing models so they run faster on smaller devices; others, like Perplexity’s approach here, focus on distributing workloads across specialized systems to maximize efficiency. It’s part of an ongoing shift from simply building bigger brains for machines toward designing smarter environments for those brains to operate in.

AI experience and performance

For professionals who use AI tools daily—whether drafting reports, analyzing data, or brainstorming ideas—this kind of improvement might feel subtle but significant. A smoother conversation with an AI assistant means less distraction and more trust in its reliability. And while most users won’t know what “KV caches” or “RDMA transfers” are (nor should they need to), they will notice when their digital helper feels less jittery and more responsive under pressure.

LLMs and AI infrastructure

So where does this leave us? Perhaps with a reminder that progress in AI isn’t always about dazzling new features or headline-grabbing breakthroughs. Sometimes it’s about careful engineering choices that make our interactions feel just a little more natural. As we watch these systems evolve day by day, maybe the real question isn’t how smart they’ll become—but how gracefully they’ll learn to keep pace with us without losing their breath mid-sentence.

As we wrap up, it feels clear that progress in AI often comes not from flashy leaps but from thoughtful refinements like this, reminding us that smoother, steadier interactions can quietly shape how naturally these tools fit into our daily rhythm and support us with less friction.

Term Explanations

Prefill: The stage where the model reads and processes your whole prompt so it can build the context it needs — think of it as the model doing the heavy-duty thinking before it starts speaking.

Decode: The stage when the model generates the response word by word (or piece by piece); it’s a more delicate, step‑by‑step process that relies on what the prefill prepared.

KV caches: Short for “key-value caches” — a temporary memory that stores parts of the model’s prior work so it can produce the next words faster without redoing all the heavy calculations.

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