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feat(phase-17/06): SGLang and RadixAttention for prefix-heavy workloads
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Rohit Ghumare
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.caption { font-size: 11px; fill: #555; font-style: italic; }
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</defs>
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<text x="480" y="24" text-anchor="middle" class="title">RadixAttention — KV cache as a tree, scheduler as the hot-branch pinner</text>
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<rect x="40" y="50" width="460" height="490" class="box"/>
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<text x="270" y="72" text-anchor="middle" class="head">the radix tree</text>
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<rect x="210" y="90" width="120" height="30" class="cool"/>
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<text x="270" y="110" text-anchor="middle" class="step">SYSTEM (2000 tok)</text>
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<rect x="210" y="140" width="120" height="30" class="cool"/>
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<text x="270" y="160" text-anchor="middle" class="step">TOOLS (300 tok)</text>
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<line x1="270" y1="120" x2="270" y2="140" stroke="#1a1a1a" stroke-width="1.2"/>
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<rect x="80" y="200" width="110" height="30" class="cold"/>
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<text x="135" y="220" text-anchor="middle" class="step">DOC_A (500)</text>
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<rect x="220" y="200" width="110" height="30" class="cold"/>
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<text x="275" y="220" text-anchor="middle" class="step">DOC_B (500)</text>
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<rect x="360" y="200" width="110" height="30" class="cold"/>
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<text x="415" y="220" text-anchor="middle" class="step">DOC_C (500)</text>
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<line x1="270" y1="170" x2="135" y2="200" stroke="#1a1a1a" stroke-width="1.2"/>
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<rect x="60" y="260" width="60" height="22" class="hot"/>
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<text x="90" y="276" text-anchor="middle" class="small">Q_1</text>
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<rect x="130" y="260" width="60" height="22" class="hot"/>
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<text x="160" y="276" text-anchor="middle" class="small">Q_2</text>
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<rect x="200" y="260" width="60" height="22" class="hot"/>
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<text x="230" y="276" text-anchor="middle" class="small">Q_3</text>
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<rect x="270" y="260" width="60" height="22" class="hot"/>
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<text x="300" y="276" text-anchor="middle" class="small">Q_4</text>
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<rect x="340" y="260" width="60" height="22" class="hot"/>
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<text x="370" y="276" text-anchor="middle" class="small">Q_5</text>
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<rect x="410" y="260" width="60" height="22" class="hot"/>
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<text x="440" y="276" text-anchor="middle" class="small">Q_6</text>
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<rect x="60" y="310" width="420" height="70" class="box"/>
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<text x="270" y="330" text-anchor="middle" class="step">new request: SYSTEM + TOOLS + DOC_B + Q_7</text>
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<text x="270" y="350" text-anchor="middle" class="small">walk the tree : SYSTEM reuse, TOOLS reuse, DOC_B reuse</text>
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<text x="270" y="366" text-anchor="middle" class="small">allocate blocks only for Q_7 (60 tokens, 4 blocks)</text>
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<rect x="60" y="390" width="420" height="60" class="cool"/>
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<text x="270" y="414" text-anchor="middle" class="step">prefill cost : 60 tokens instead of 2860</text>
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<text x="270" y="432" text-anchor="middle" class="small">on prefix-heavy RAG : up to 6.4x SGLang over vLLM</text>
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<rect x="60" y="460" width="420" height="70" class="hot"/>
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<text x="270" y="482" text-anchor="middle" class="step">the eviction policy</text>
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<text x="270" y="500" text-anchor="middle" class="small">branch-level LRU : evict whole leaves</text>
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<text x="270" y="518" text-anchor="middle" class="small">keeps cache shape matched to tree shape</text>
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<rect x="520" y="50" width="400" height="490" class="box"/>
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<text x="720" y="72" text-anchor="middle" class="head">cache-aware scheduling</text>
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<rect x="540" y="90" width="360" height="70" class="cold"/>
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<text x="720" y="112" text-anchor="middle" class="step">FCFS is wrong for prefix-heavy traffic</text>
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<text x="720" y="130" text-anchor="middle" class="small">serves requests in arrival order</text>
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<text x="720" y="146" text-anchor="middle" class="small">evicts hot branches before they are reused</text>
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<rect x="540" y="170" width="360" height="80" class="cool"/>
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<text x="720" y="192" text-anchor="middle" class="step">depth-first dispatch</text>
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<text x="720" y="210" text-anchor="middle" class="small">prefer requests rooted at the running branch</text>
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<text x="720" y="226" text-anchor="middle" class="small">keep the hot branch resident; stream siblings</text>
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<text x="720" y="242" text-anchor="middle" class="small">approximates radix depth-first traversal</text>
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<rect x="540" y="260" width="360" height="120" class="dsk"/>
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<text x="720" y="282" text-anchor="middle" class="step">numbers (2026)</text>
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<text x="720" y="302" text-anchor="middle" class="small">Llama 3.1 8B H100 ShareGPT 1K :</text>
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<text x="720" y="318" text-anchor="middle" class="small">SGLang ~16,200 tok/s vs vLLM ~12,500 (+29%)</text>
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<text x="720" y="336" text-anchor="middle" class="small">prefix-heavy RAG : up to 6.4x</text>
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<text x="720" y="352" text-anchor="middle" class="small">voice cloning : 86.4% prefix-cache hit rate</text>
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<text x="720" y="368" text-anchor="middle" class="small">production : 50-99% depending on template discipline</text>
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<rect x="540" y="390" width="360" height="140" class="hot"/>
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<text x="720" y="412" text-anchor="middle" class="step">the gotcha — prompt ordering</text>
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<text x="720" y="432" text-anchor="middle" class="small">[system, tools, context] ≠ [system, context, tools]</text>
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<text x="720" y="450" text-anchor="middle" class="small">tree sees two distinct paths</text>
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<text x="720" y="466" text-anchor="middle" class="small">6.4x disappears, back to vLLM throughput</text>
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<text x="720" y="488" text-anchor="middle" class="step">engineer's lever : fix the template</text>
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<text x="720" y="506" text-anchor="middle" class="small">immutable first (system, tools, schemas)</text>
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<text x="720" y="520" text-anchor="middle" class="small">user input last; real case 7% to 74% in one change</text>
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</svg>
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|
After Width: | Height: | Size: 6.3 KiB |
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"""Toy RadixAttention scheduler — stdlib Python.
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Simulate an SGLang-style radix-tree KV cache plus two schedulers:
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FCFS : naive first-come first-served
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CACHE_AWARE : depth-first dispatch on hottest branch
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Also show how scrambled prompt ordering collapses hit rate. Pedagogical
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constants — the shape matches the published numbers, not the absolute
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latencies.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from collections import defaultdict
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import random
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KV_BUDGET_BLOCKS = 160 # small budget so eviction bites under FCFS
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BLOCK_TOKENS = 16
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def token_count(seg: str) -> int:
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if seg == "SYSTEM":
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return 2000
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if seg.startswith("DOC_"):
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return 500
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if seg.startswith("Q_"):
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return 60
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if seg == "TOOLS":
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return 300
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return 100
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@dataclass
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class Request:
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rid: int
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segments: list[str]
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class RadixCache:
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"""Represent the tree as a dict: path_tuple -> blocks (last_used)."""
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def __init__(self, budget_blocks: int = KV_BUDGET_BLOCKS):
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self.budget = budget_blocks
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self.used = 0
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self.time = 0
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# key: tuple of segments. value: (blocks, last_used)
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self.nodes: dict[tuple[str, ...], list[int]] = {}
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def walk(self, segments: list[str]) -> int:
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"""Return number of tokens that are already cached at the longest matching
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prefix, bumping last_used along the path."""
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reused = 0
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self.time += 1
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for i in range(1, len(segments) + 1):
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key = tuple(segments[:i])
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if key in self.nodes:
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reused += token_count(segments[i - 1])
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self.nodes[key][1] = self.time
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else:
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break
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return reused
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def insert(self, segments: list[str]) -> None:
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"""Insert any missing segments on the path, evicting LRU leaves if over budget."""
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for i in range(1, len(segments) + 1):
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key = tuple(segments[:i])
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if key in self.nodes:
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continue
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blocks = (token_count(segments[i - 1]) + BLOCK_TOKENS - 1) // BLOCK_TOKENS
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while self.used + blocks > self.budget and self._evict_one():
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pass
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self.nodes[key] = [blocks, self.time]
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self.used += blocks
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def _evict_one(self) -> bool:
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leaves = [k for k in self.nodes if not any(
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other != k and other[: len(k)] == k for other in self.nodes)]
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if not leaves:
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return False
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victim = min(leaves, key=lambda k: self.nodes[k][1])
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self.used -= self.nodes.pop(victim)[0]
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return True
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def simulate(requests: list[Request], scheduler: str) -> dict:
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cache = RadixCache()
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if scheduler == "CACHE_AWARE":
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branch_count: dict[tuple[str, ...], int] = defaultdict(int)
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for r in requests:
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for i in range(1, len(r.segments) + 1):
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branch_count[tuple(r.segments[:i])] += 1
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def score(r: Request) -> int:
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return max(branch_count[tuple(r.segments[:i])] * sum(
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token_count(s) for s in r.segments[:i])
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for i in range(1, len(r.segments) + 1))
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order = sorted(requests, key=score, reverse=True)
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else:
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order = list(requests)
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saved = 0
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total = 0
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for r in order:
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prompt_tokens = sum(token_count(s) for s in r.segments)
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total += prompt_tokens
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reused = cache.walk(r.segments)
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saved += reused
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cache.insert(r.segments)
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return {
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"hit_rate": saved / total if total else 0,
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"saved": saved,
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"total": total,
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"reqs": len(requests),
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}
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def workload_rag(n: int = 80, docs: int = 4, seed: int = 1) -> list[Request]:
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rng = random.Random(seed)
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reqs = []
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for i in range(n):
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doc = f"DOC_{rng.randrange(docs)}"
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q = f"Q_{i}"
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reqs.append(Request(i, ["SYSTEM", "TOOLS", doc, q]))
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rng.shuffle(reqs)
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return reqs
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def workload_scrambled(n: int = 80, docs: int = 4, seed: int = 1) -> list[Request]:
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"""Prompts reorder [SYSTEM, TOOLS, DOC] randomly. Tree cannot share the prefix."""
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rng = random.Random(seed)
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reqs = []
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for i in range(n):
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doc = f"DOC_{rng.randrange(docs)}"
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q = f"Q_{i}"
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prefix = ["SYSTEM", "TOOLS", doc]
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rng.shuffle(prefix)
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reqs.append(Request(i, prefix + [q]))
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rng.shuffle(reqs)
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return reqs
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def report(label: str, res: dict) -> None:
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print(f"{label:44} hit_rate={res['hit_rate']:6.1%} "
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f"saved={res['saved']:>6}/{res['total']:<6} tok reqs={res['reqs']}")
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def main() -> None:
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print("=" * 88)
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print("TOY RADIX CACHE — cache hit rate across schedulers and orderings")
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print("=" * 88)
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rag = workload_rag()
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report("RAG workload | FCFS", simulate(rag, "FCFS"))
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report("RAG workload | CACHE_AWARE", simulate(rag, "CACHE_AWARE"))
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scrambled = workload_scrambled()
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report("RAG scrambled prefix | FCFS", simulate(scrambled, "FCFS"))
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report("RAG scrambled prefix | CACHE_AWARE", simulate(scrambled, "CACHE_AWARE"))
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print()
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print("=" * 88)
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print("KEY FINDING")
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print("-" * 88)
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print(" Fixed ordering + cache-aware scheduler : hit rate clears 80% on RAG.")
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print(" Scrambled prefix order : hit rate collapses — the tree cannot find shared paths.")
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print(" Real cases: 7% -> 74% hit rate by moving dynamic content out of the prefix.")
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if __name__ == "__main__":
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main()
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# SGLang and RadixAttention for Prefix-Heavy Workloads
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> SGLang treats the KV cache as a first-class, reusable resource stored in a radix tree. Where vLLM schedules requests FCFS (first-come, first-served), SGLang's cache-aware scheduler prioritizes requests with longer shared prefixes — effectively a depth-first radix traversal so hot branches stay resident in HBM. On Llama 3.1 8B with ShareGPT-like 1K prompts, SGLang hits ~16,200 tok/s to vLLM's ~12,500, a ~29% edge. On prefix-heavy RAG workloads the advantage reaches 6.4x. On voice-cloning-shaped workloads cache hit rate cleared 86%. Deployed on 400,000+ GPUs in 2026 across xAI, LinkedIn, Cursor, Oracle, GCP, Azure, AWS. The gotcha is that the 6.4x number evaporates when prefix ordering is inconsistent — ordering is the engineer's lever.
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**Type:** Learn
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**Languages:** Python (stdlib, toy radix-tree cache + cache-aware scheduler)
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**Prerequisites:** Phase 17 · 04 (vLLM Serving Internals), Phase 14 (Agentic RAG)
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**Time:** ~75 minutes
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## Learning Objectives
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- Diagram RadixAttention: how prefixes are stored in a radix tree and how KV blocks are shared across sequences rooted at the same branch.
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- Explain cache-aware scheduling and why FCFS is wrong for prefix-heavy traffic.
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- Compute expected speedup for a workload given prefix-cache hit rate and prompt length distribution.
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- Name the prompt-ordering discipline that makes the 6.4x number real vs a lost upside.
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## The Problem
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Classic serving treats each request's prompt as opaque. Even when 5,000 RAG requests all start with the same 2,000-token system prompt plus same retrieval preamble, vLLM prefills that 2,000-token prefix 5,000 times. The GPU does the same work over and over.
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The observation: prompts in agentic and RAG workloads share long prefixes almost always. System prompt, tool schemas, few-shot examples, retrieval headers, conversation history — all repeat across requests. If you stored the KV cache for that prefix once and reused it, you would not prefill it again.
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RadixAttention does exactly this. Tokens are indexed in a radix tree; each node owns KV blocks for the token sequence on its path from root. A new request walks the tree: any node whose token matches re-uses that node's KV blocks. Prefill cost becomes proportional to the "new" suffix, not the full prompt.
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The challenge is scheduling. If two requests share a 2,000-token prefix and a third shares only 200 tokens of the same prefix, you want to serve the two long-shared requests together so the long prefix stays in HBM. FCFS does the opposite — it serves whoever arrived first, potentially evicting the hot branch before the next long-prefix request hits.
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## The Concept
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### The radix tree as a KV index
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A radix tree (compact trie) stores token sequences. Each node owns a token range and the KV blocks computed for that range. Children extend the sequence one or more tokens.
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```
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root
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|- "You are a helpful assistant..." (2,000 tokens, 124 KV blocks)
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|- "Context: <doc A>..." (500 tokens, 31 blocks)
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|- "Question: Alice..." (80 tokens, 5 blocks)
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|- "Question: Bob..." (95 tokens, 6 blocks)
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|- "Context: <doc B>..." (520 tokens, 33 blocks)
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```
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A new request comes in with system prompt + "Context: <doc A>" + "Question: Carol". The scheduler walks: system prefix matches (124 blocks reused), doc-A branch matches (31 blocks reused), then allocates fresh blocks only for "Question: Carol" (4 blocks). Prefill cost: 4 blocks of new tokens. Without the tree: 160 blocks. ~40x savings on prefill.
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### Cache-aware scheduling
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Radix-tree-backed reuse is pointless if the cache churns. Two key policies:
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1. **Depth-first dispatch**. When picking the next request from the queue, prefer requests rooted at the same branch as the current running set. This keeps the hot branch pinned.
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2. **LRU at branch level, not block level**. Evict whole branches (starting from shortest-used leaves) rather than individual blocks, so cache shape matches radix shape.
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FCFS violates both. A request sharing 2,000 tokens sits behind a request sharing 50, then the 2,000-token branch gets evicted to admit the 50-token one.
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### Benchmark numbers you should memorize
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||||
|
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- Llama 3.1 8B, H100, ShareGPT 1K prompts: SGLang ~16,200 tok/s vs vLLM ~12,500 (~29% edge).
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- Prefix-heavy RAG (same system + same doc, varying question): up to 6.4x on SGLang.
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- Voice cloning workloads: 86.4% prefix-cache hit rate.
|
||||
- Production hit rates across SGLang customers: 50-99% depending on prompt discipline.
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- Deployed on 400,000+ GPUs in 2026.
|
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|
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### The ordering gotcha
|
||||
|
||||
The 6.4x number relies on consistent prompt-template ordering. If your client constructs prompts as `[system, tools, context, history, question]` in some requests and `[system, context, tools, history, question]` in others, the tree cannot find the shared prefix. What looks like a shared prefix to a human is two distinct sequences to the radix tree.
|
||||
|
||||
Engineer's lever: your prompt template is a cache key. Fix the order. Put everything immutable (system, tools, schemas) first. Put retrieval context next. Put user question last. Do not interleave dynamic content into the prefix.
|
||||
|
||||
Real case from the research: moving dynamic content out of the cacheable prefix took one deployment from 7% to 74% cache hit rate in one change.
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||||
|
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### Where RadixAttention wins and loses
|
||||
|
||||
Wins:
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||||
- RAG (same retrieval preamble, varying question).
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- Agents (same tool schemas, varying query).
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- Chat with long system prompt.
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- Voice / vision workloads with repeated preambles.
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|
||||
Loses (returns to vLLM-level throughput):
|
||||
- Single-shot generation with unique prompts (code completion, open-ended chat without system prompt).
|
||||
- Dynamic prompts where every request interleaves unique content into the prefix.
|
||||
|
||||
### Why this is a scheduler problem, not just a kernel problem
|
||||
|
||||
You can implement KV reuse as a kernel trick. SGLang's insight is that reuse only pays if the scheduler keeps the hot branch resident. A naive "reuse if available" policy will churn the cache under mixed load. The radix-tree-indexed scheduler is what turns the kernel trick into a 29% production edge.
|
||||
|
||||
### Interplay with vLLM
|
||||
|
||||
The two systems are not strict competitors. In 2026 vLLM added prefix caching (`--enable-prefix-caching`) and a cache-aware router (vLLM Router in Rust). The gap closed but did not fully disappear — SGLang's whole stack is radix-first; vLLM grafted it on. For workloads dominated by prefix reuse, SGLang remains the default. For general-purpose serving without strong prefix patterns, vLLM remains equal or better.
|
||||
|
||||
## Use It
|
||||
|
||||
`code/main.py` implements a toy radix-tree KV cache plus a scheduler with two policies: FCFS and cache-aware. Runs the same workload through both, reports prefix-cache hit rate and throughput delta. Then runs a "scrambled ordering" workload to show the 6.4x collapse.
|
||||
|
||||
## Ship It
|
||||
|
||||
This lesson produces `outputs/skill-radix-scheduler-advisor.md`. Given a workload description (prompt-template shape, retrieval pattern, number of concurrent tenants), it produces a prompt-ordering prescription and a go/no-go for SGLang adoption.
|
||||
|
||||
## Exercises
|
||||
|
||||
1. Run `code/main.py`. Compare FCFS and cache-aware on the same workload. Where does the delta come from — prefill savings, decode savings, or queue delay?
|
||||
2. Modify the workload so prompts randomly permute `[system, tools, context]`. Re-run. What happens to hit rate? Why?
|
||||
3. Compute the HBM cost of keeping a 2,000-token system prompt resident as one radix branch on Llama 3.1 8B. Compare to the cost of a 16-sequence batch without prefix reuse.
|
||||
4. Read the SGLang RadixAttention paper. Explain in three sentences why tree-shaped LRU eviction beats block-shaped LRU under prefix-heavy load.
|
||||
5. A customer reports only 8% cache hit rate. Name three likely causes and the diagnostic you would run for each.
|
||||
|
||||
## Key Terms
|
||||
|
||||
| Term | What people say | What it actually means |
|
||||
|------|----------------|------------------------|
|
||||
| RadixAttention | "the SGLang thing" | KV cache indexed as a radix tree so shared prefixes reuse blocks |
|
||||
| Radix tree | "compact trie" | Tree where each node owns a token range and its KV blocks |
|
||||
| Cache-aware scheduler | "hot-branch-first" | Scheduler that prefers requests sharing the resident branch |
|
||||
| Prefix-cache hit rate | "how much of your prompt was free" | Fraction of prompt tokens served from reused KV blocks |
|
||||
| FCFS | "first-come first-served" | Default scheduling that breaks prefix locality |
|
||||
| Branch-level LRU | "evict the leaf" | Eviction policy matched to radix shape |
|
||||
| Prompt template ordering | "the cache key" | The prompt's component order determines what the tree can share |
|
||||
| System prompt pinning | "resident prefix" | Keep the immutable system portion pinned to avoid eviction thrash |
|
||||
|
||||
## Further Reading
|
||||
|
||||
- [SGLang GitHub](https://github.com/sgl-project/sglang) — source and docs.
|
||||
- [SGLang documentation](https://sgl-project.github.io/) — RadixAttention and scheduling details.
|
||||
- [SGLang paper — Efficiently Programming Large Language Models (arXiv:2312.07104)](https://arxiv.org/abs/2312.07104) — the design reference.
|
||||
- [LMSYS blog — SGLang with RadixAttention](https://www.lmsys.org/blog/2024-01-17-sglang/) — benchmark numbers and scheduler rationale.
|
||||
- [vLLM — Prefix Caching](https://docs.vllm.ai/en/latest/features/prefix_caching.html) — vLLM's own radix-like implementation, for comparison.
|
||||
+30
@@ -0,0 +1,30 @@
|
||||
---
|
||||
name: radix-scheduler-advisor
|
||||
description: Advise on SGLang adoption and prompt-ordering discipline for prefix-heavy workloads that want RadixAttention's cache reuse.
|
||||
version: 1.0.0
|
||||
phase: 17
|
||||
lesson: 06
|
||||
tags: [sglang, radixattention, prefix-caching, scheduler, prompt-ordering]
|
||||
---
|
||||
|
||||
Given a workload description (prompt-template shape, retrieval pattern, conversation length, number of concurrent tenants, hardware), produce an SGLang / RadixAttention adoption advisory.
|
||||
|
||||
Produce:
|
||||
|
||||
1. Workload fingerprint. Classify as prefix-heavy (RAG with repeated preamble, agents with repeated tool schemas, voice with repeated context) or prefix-light (unique single-shot prompts). Name the shared prefix length and the repetition rate.
|
||||
2. Prompt-ordering audit. Walk the current prompt template top to bottom. Flag any dynamic content interleaved into the immutable section. Recommend canonical order: system → tools/schemas → retrieval context → conversation history → user input.
|
||||
3. Expected hit rate. From workload fingerprint, estimate achievable cache hit rate. General chat 10-30%. RAG with consistent template 60-85%. Voice/vision with fixed preamble 80-95%.
|
||||
4. SGLang vs vLLM decision. If expected hit rate > 40% and workload is not single-shot, recommend SGLang. If < 30%, vLLM with `--enable-prefix-caching` is simpler. If 30-40%, run both on a sample and pick.
|
||||
5. Rollout plan. 48-hour shadow benchmark on SGLang with current prompt template. Log hit rate. Fix prompt-ordering issues. Re-benchmark. Ship if hit rate clears target.
|
||||
|
||||
Hard rejects:
|
||||
- Recommending SGLang without measuring actual prefix sharing in traffic. Refuse.
|
||||
- Claiming the 6.4x number without citing workload shape. The number is workload-specific.
|
||||
- Ignoring prompt-ordering discipline. The template is the cache key; without it the scheduler cannot help.
|
||||
|
||||
Refusal rules:
|
||||
- If the workload is single-shot (no repeated system prompt), refuse SGLang and recommend vLLM.
|
||||
- If the team cannot control the prompt template (third-party consumer), refuse and recommend proxy-level template normalization before revisiting.
|
||||
- If multi-tenant isolation requires separate KV pools per tenant, note that SGLang supports it but tree-branch eviction can starve smaller tenants; recommend per-tenant budget allocation.
|
||||
|
||||
Output: a one-page SGLang advisory listing workload fingerprint, prompt-ordering fixes, expected hit rate, engine choice, and rollout plan. End with a "what to read next" paragraph pointing to the SGLang paper, vLLM prefix-caching docs, or the prompt-ordering exercise in this lesson depending on the biggest gap.
|
||||
Reference in New Issue
Block a user