Name two production KV-cache eviction policies and what each prioritizes

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When generation pushes the KV cache past its memory budget at long context, the server must evict cache entries to keep going. Name two production-grade eviction policies, describe what each keeps and what each drops, and note the bookkeeping cost of each.

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When a long generation pushes the KV cache past available memory, the server must drop some cached K, V entries to keep generating. Two production policies dominate: **1. Sliding-window eviction**, keep the most recent W tokens; drop anything older. Bookkeeping is essentially free (a circular buffer or moving pointer). The cost: anything older than W is gone, so long-range references die. This is implicit in models trained with sliding-window attention like Mistral 7B and is the simplest fallback for stretched context. **2. Heavy-hitter eviction (H2O)**, score each cached token by how much attention weight it has received historically. Keep the top-k highest-scored tokens plus a recent local window; drop the rest. Bookkeeping requires per-token running scores updated each step. The win: better quality preservation at the same cache budget because semantically important tokens (entities, topic anchors) survive. Production often blends these. **StreamingLLM** keeps a few 'attention sink' tokens at the very start of the sequence (because of attention-sink phenomena where the first few positions absorb disproportionate weight, dropping them collapses the softmax) plus a recent window. The trade-off is quality vs bookkeeping cost: sliding-window is cheap, heavy-hitter is smarter, hybrids try to get both.

Two policies cover most production deployments. **Sliding-window eviction.** Keep only the last W tokens of K, V; drop the oldest as new tokens are added. Implementation is a circular buffer or a moving start-pointer, essentiallyzero bookkeeping cost. The quality cost is that anything beyond W is unreachable for the rest of the generation. Used implicitly by Mistral 7B's sliding-window attention and as the default fallback in vLLM and SGLang for context-bounded serving. **Heavy-hitter eviction (H2O style).** Track per-token attention weight accumulated over recent decode steps. At eviction time, keep the top-k highest-scored tokens plus a small local window of the most recent tokens; drop the rest. Bookkeeping is a per-token running score updated every step. The quality win is substantial: semantically important tokens (entities, topic anchors, structural markers) survive eviction even when they are far back in the sequence, while uninformative filler is dropped. **Hybrids in production.** **StreamingLLM** is the canonical example: keep the first few 'attention sink' tokens permanently (they absorb disproportionate softmax mass and dropping them destabilizes the distribution) plus a recent window. **Adaptive KV eviction** in some 2025-2026 serving stacks combines heavy-hitter scoring with chunked drops to reduce per-step overhead. The practical choice is a quality vs bookkeeping trade. Use sliding-window when you need cheap and predictable; use heavy-hitter when long-range references matter and quality at extreme context is the binding constraint.

Quick reference

TL;DR

Two production policies: sliding-window (keep last W, free bookkeeping) and heavy-hitter (keep tokens with highest accumulated attention weight, per-token score tracking).

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Easy to grasp

Picture a chef's prep station that can only hold 20 ingredients at a time. As new ingredients come in, old ones have to go. The simple rule is 'whatever has been sitting longest, throw it out',that is sliding-window. The smarter rule is 'whatever the recipes keep reaching for, keep it; whatever has been ignored, throw it out',that is heavy-hitter. The smarter rule needs the chef to keep a tally of how often each ingredient gets used, but it preserves the important stuff better. Production kitchens often blend the two: always keep a few staples like salt at the start, and otherwise rotate the recent and the popular.

Key concepts

KV cache Sliding window attention H2O eviction Attention sink StreamingLLM

Detailed answer & concept explanation

~7 min read

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example. Click to expand.

Everything you need to truly understand this topic: intuition, mechanics, step by step explanation, code, formulas, and worked example.

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Quick scan · 2–4 min

Everything important, quickly.

Name sliding-window eviction (keep last W, free bookkeeping) and heavy-hitter eviction (keep top-k by attention score, per-token bookkeeping). Cite StreamingLLM as the hybrid and explain attention sinks. Close with the production decision rubric: bounded context vs streaming vs extreme long context.

Real-world examples

Real products, models, and research that use this idea.

Mistral 7B: sliding-window attention with W = 4096 is the architectural form of sliding-window eviction.
H2O (NeurIPS 2023): the canonical heavy-hitter eviction paper; showed near-lossless quality at 20% cache size on long-context QA benchmarks.
StreamingLLM (Xiao et al., 2024): attention-sink + recent-window hybrid, deployed widely in 2024-2026 long-context serving stacks.
DeepSeek V4 multi-head latent attention: not eviction but related, compresses KV per token to shrink the budget pressure that eviction tries to address.
vLLM and SGLang in 2026: support sliding-window and StreamingLLM-style hybrids as configurable serving policies.

Interviewer follow-ups

What an interviewer would ask next. Try answering before peeking at the approach.

QWhy does dropping the first few tokens collapse the attention distribution in long generations?

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QHow does heavy-hitter eviction interact with grouped-query attention (GQA) where K and V are shared across query heads?

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QWhen does KV quantization beat eviction at the same memory budget?

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Don't say this

Red flags and common mistakes that signal junior thinking. Click to expand.

Most common mistake

Treating KV cache eviction as a pure capacity problem with one right answer. Quality varies by eviction policy because not all cached tokens are equally useful for future generation.

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60-second cheat sheet

The night-before-the-interview bullets. Scan these on the way to the call.

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References

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