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vLLM Semantic Router

Build yourMixture-of-Models

System-level intelligence for heterogeneous LLM inference

Mixture-of-Models
Why Semantic Router

Intelligent multi-model routing

Deploy fast, route by signal, and keep every decision observable.

Signal-driven

16signal families

Classify intent, safety, and domain signals — then route each request to the right model.

Drop-in

1OpenAI-compatible API

Deploy as Envoy ExtProc or local vllm-sr. No client changes for existing integrations.

Observable

12routing algorithms

From rules to reinforcement learning — every routing decision is configurable and measurable.

How it integrates

Route queries to the right model

Route each request to the best model pool through one OpenAI-compatible API.

RoutingQuery 1 Mistral
Incoming queries
  • Query 1Sending
  • Query 2
  • Query 3
  • Query 4
  • Query 5
  • Query 6
vLLM Semantic Router
System Level Intelligencemodel id: vllm-sr/auto
  1. 01Signal layerDetect
  2. 02Projection layerCoordinate
  3. 03Decision engineMatch
  4. 04AlgorithmsOptimize
  5. 05PluginsEnforce
Security & policyObservability & replay
Model pools
Closed models
  • Claude
    Claude
  • OpenAI
    ChatGPT
  • Gemini
    Gemini
Open models
  • Mistral
    MistralQuery 1
  • DeepSeek
    DeepSeek
  • Zhipu
    GLM

Heterogeneous models

Architecture

Unify heterogeneous inference.

Unify a fragmented model landscape across four dimensions.

Explore how it works
DimensionFragmented todayWith vLLM SR
Models
Fragmented today

Models specialize in different work.

With vLLM SR

Compose personalized model paths.

Compute
Fragmented today

GPUs, accelerators, edge, and cloud coexist.

With vLLM SR

Route across heterogeneous compute.

Location
Fragmented today

Inference spans edge, private, and cloud.

With vLLM SR

Keep data within its boundaries.

Preference
Fragmented today

“Best” changes by user and workload.

With vLLM SR

Make every preference executable.

Signals16

16 signal families across heuristic and learned detectors, from knowledge base routing to history-aware reasks.

Selection12

12 routing strategies spanning rules, latency heuristics, reinforcement learning, and ML selection.

Papers18

18 research papers spanning routing, systems, safety, and multimodality.

Industry voices

The shift is already underway.

System-level optimizationMark PapermasterAMD CTO & EVP
We’ve become system optimizers, still optimizing every component, but then equally looking at how you optimize how each of the pieces come together.SourcetheCUBE at RAISE Summit · 2026

Routing Blueprint

From signal to model path.

Explore how the architecture extracts signals, composes decisions, and executes the selected path.

Shannon Mapping

Structural mapping from communication theory to the routing pipeline.

The user request is the raw source message before encoding.

Signal intelligence

Intelligence before generation.

Purpose-built encoders extract intent, context, safety, and modality before a generative model is selected.

Signal surfaces

Sequence classification, token labeling, embeddings, and reranking collapse into one system-intelligence layer.

Input
"Is machine learning related to AI?"
Tokenizer
[CLS]IsmachinelearningrelatedtoAI?[SEP]
Embedding
Token Emb
Segment Emb
Position Emb
h₀ = Σ
Encoder Block
×N
ATTNMulti-Head Attention
NORMAdd & Norm
FFNFeed-Forward
NORMAdd & Norm
Signals
CLS
Sentence-Level (CLS Token)[CLS] → Linear Head → "computer science"TaskType: SEQ_CLS
DomainJailbreakFact-checkFeedbackModality
BIE

Bi-Encoder Embeddings

Independently encode queries and candidates into dense vectors for similarity search and semantic caching.

XCE

Cross-Encoder Learning

Joint cross-attention scoring of query-candidate pairs for high-precision reranking.

CLS

Classification

Domain, jailbreak, PII and fact-check classification across 14 MMLU categories via ModernBERT with LoRA.

ATT

Full Attention

Bidirectional attention across tokens and sentences, with full context instead of causal masking.

2DM

2DMSE

Adjust embedding layers and dimensions at inference time to trade compute for accuracy on the fly.

MRL

MRL

Truncate embedding vectors to any dimension without retraining to balance accuracy and speed per request.

Quick start

Install locally in one line.

One supported local path. Copy the installer, run it, then open the dashboard. The supported first-run path is a single installer that sets up the CLI and local serve flow on macOS and Linux.

Local install pathOne command to start
  1. Install the CLI

    macOS / Linux

    Downloads the installer, prepares Docker, and writes vllm-sr to your PATH.

    shell
    curl -fsSL https://vllm-sr.ai/install.sh | bash
    Remove local install

    Removes ~/.local/share/vllm-sr and ~/.local/bin/vllm-sr. Stop any running serve session first.

    remove
    rm -rf ~/.local/share/vllm-sr && rm -f ~/.local/bin/vllm-sr

Installs into ~/.local/share/vllm-sr, writes ~/.local/bin/vllm-sr, and keeps Windows on the manual pip flow in the docs.

How it works

One router, three use cases

See how signals, policies, and models connect for every request.

Universal compatibility

One router, any deployment

Run on gateways, Kubernetes, or locally — with 16 signal families for every request.

Research

Papers behind the router.

Research threads that trace the router's evolving ideas across safety, multimodality, orchestration, and system design.

2026 / PaperPOSITION PAPER

vLLM Semantic Router: Signal Driven Decision Routing for Mixture-of-Modality Models

vLLM Semantic Router Team

arXiv Technical Report

We introduce vLLM Semantic Router, a signal-driven decision routing framework for Mixture-of-Modality deployments that composes heterogeneous signals into deployment-specific routing policies across cost, privacy, latency, and safety constraints.

2026 / PaperVISION PAPER

The Workload-Router-Pool Architecture for LLM Inference Optimization: A Vision Paper from the vLLM Semantic Router Project

Huamin Chen, Xunzhuo Liu, Bowei He, Fuyuan Lyu, Yankai Chen, Xue Liu, Yuhan Liu, Junchen Jiang

arXiv Technical Report

We synthesize the project’s recent routing, fleet, multimodal, and governance results into the Workload-Router-Pool (WRP) architecture, connecting signal-driven routing to a full-stack inference optimization framework and outlining future research directions across workload, router, and pool design.

2026 / Paper

Visual Confused Deputy: Exploiting and Defending Perception Failures in Computer-Using Agents

Xunzhuo Liu, Bowei He, Xue Liu, Andy Luo, Haichen Zhang, Huamin Chen

arXiv Technical Report

We formalize the visual confused deputy as a security failure mode in computer-using agents and introduce a dual-channel guardrail that independently checks click targets and action reasoning before execution.

2026 / Paper

Outcome-Aware Tool Selection for Semantic Routers: Latency-Constrained Learning Without LLM Inference

Huamin Chen, Xunzhuo Liu, Junchen Jiang, Bowei He, Xue Liu

arXiv Technical Report

We introduce Outcome-Aware Tool Selection (OATS), an offline embedding refinement method that improves semantic-router tool ranking under single-digit millisecond CPU budgets without adding serving-time model inference.

2026 / Paper

Adaptive Vision-Language Model Routing for Computer Use Agents

Xunzhuo Liu, Bowei He, Xue Liu, Andy Luo, Haichen Zhang, Huamin Chen

arXiv Technical Report

We propose Adaptive VLM Routing (AVR), which estimates action difficulty and routes computer-use agent steps to the cheapest model that still satisfies a target reliability threshold.

2026 / Paper

98× Faster LLM Routing Without a Dedicated GPU: Flash Attention, Prompt Compression, and Near-Streaming for the vLLM Semantic Router

Xunzhuo Liu, Bowei He, Xue Liu, Andy Luo, Haichen Zhang, Huamin Chen

arXiv Technical Report

We combine Flash Attention, prompt compression, and near-streaming body processing to cut routing latency from seconds to tens of milliseconds while keeping the router lightweight enough to share hardware with serving.

2026 / Paper

inference-fleet-sim: A Queueing-Theory-Grounded Fleet Capacity Planner for LLM Inference

Huamin Chen, Xunzhuo Liu, Yuhan Liu, Junchen Jiang, Bowei He, Xue Liu

arXiv Technical Report

We present a queueing-theory-grounded fleet planner and discrete-event simulator for sizing multi-pool LLM GPU fleets against P99 TTFT targets, without requiring hardware profiling runs up front.

2026 / Paper

FleetOpt: Analytical Fleet Provisioning for LLM Inference with Compress-and-Route as Implementation Mechanism

Huamin Chen, Xunzhuo Liu, Yuhan Liu, Junchen Jiang, Bowei He, Xue Liu

arXiv Technical Report

We derive the minimum-cost two-pool LLM fleet directly from the workload CDF and P99 TTFT target, then use Compress-and-Route to make the optimal boundary deployable in practice.

2026 / Paper

The 1/W Law: An Analytical Study of Context-Length Routing Topology and GPU Generation Gains for LLM Inference Energy Efficiency

Huamin Chen, Xunzhuo Liu, Yuhan Liu, Junchen Jiang, Bowei He, Xue Liu

arXiv Technical Report

We derive the 1/W law showing that tokens per watt roughly halve whenever the serving context window doubles, making context-length routing topology a larger energy-efficiency lever than a pure GPU generation upgrade.

2026 / Paper

Conflict-Free Policy Languages for Probabilistic ML Predicates: A Framework and Case Study with the Semantic Router DSL

Xunzhuo Liu, Hao Wu, Huamin Chen, Bowei He, Xue Liu

arXiv Technical Report

We show how probabilistic ML predicates in policy languages can silently co-fire on the same query, and implement conflict detection plus a softmax-based prevention mechanism in the Semantic Router DSL.

2026 / Paper

From Inference Routing to Agent Orchestration: Declarative Policy Compilation with Cross-Layer Verification

Huamin Chen, Xunzhuo Liu, Bowei He, Xue Liu

arXiv Technical Report

We extend the Semantic Router DSL from stateless, per-request routing to multi-step agent workflows, emitting verified decision nodes for orchestration frameworks, Kubernetes artifacts, YANG/NETCONF payloads, and protocol-boundary gates from a single declarative source file.

2026 / Paper

Knowledge Access Beats Model Size: Memory Augmented Routing for Persistent AI Agents

Xunzhuo Liu, Bowei He, Xue Liu, Andy Luo, Haichen Zhang, Huamin Chen

arXiv Technical Report

We show that conversational memory and retrieval-grounded routing let a lightweight 8B model recover most of a 235B model’s performance on persistent user-specific queries while cutting effective inference cost by 96%.

2026 / PaperRAG VERIFICATION

Fast and Faithful: Real-Time Verification for Long-Document Retrieval-Augmented Generation Systems

Xunzhuo Liu, Bowei He, Xue Liu, Haichen Zhang, Huamin Chen

SIGIR 2026 Industry Track

We present a real-time verification component for long-document RAG that processes contexts up to 32K tokens, balancing latency and grounding coverage so interactive systems can detect unsupported answers without falling back to truncated checks.

2026 / Paper

Token-Budget-Aware Pool Routing for Cost-Efficient LLM Inference

Huamin Chen, Xunzhuo Liu, Junchen Jiang, Bowei He, Xue Liu

arXiv Technical Report

We propose token-budget-aware pool routing, which estimates each request’s total token budget using a self-calibrating bytes-per-token ratio and dispatches it to short or long vLLM pools to cut fleet cost while avoiding KV-cache failures.

2025 / Paper

When to Reason: Semantic Router for vLLM

Chen Wang, Xunzhuo Liu, Yuhan Liu, Yue Zhu, Xiangxi Mo, Junchen Jiang, Huamin Chen

NeurIPS - MLForSys

We present a semantic router that classifies queries based on their reasoning requirements and selectively applies reasoning only when beneficial.

2025 / Paper

Category-Aware Semantic Caching for Heterogeneous LLM Workloads

Chen Wang, Xunzhuo Liu, Yue Zhu, Alaa Youssef, Priya Nagpurkar, Huamin Chen

We present a category-aware semantic caching where similarity thresholds, TTLs, and quotas vary by query category, with a hybrid architecture separating in-memory HNSW search from external document storage.

2025 / Paper

Semantic Inference Routing Protocol (SIRP)

Huamin Chen, Luay Jalil

Internet Engineering Task Force (IETF)

This document specifies the Semantic Inference Routing Protocol (SIRP), a framework for content-level classification and semantic routing in AI inference systems.

2025 / Paper

Multi-Provider Extensions for Agentic AI Inference APIs

H. Chen, L. Jalil, N. Cocker

Internet Engineering Task Force (IETF) - Network Management Research Group

This document specifies multi-provider extensions for agentic AI inference APIs. Published: 20 October 2025. Intended Status: Informational. Expires: 23 April 2026.

Community

Maintainers across research, infrastructure, and model systems shape the project together.

Maintainer

Xunzhuo Liu

@AMD

Maintainer

Huamin Chen

@Microsoft

Committer

FAUST

Cloud-native Open Source Contributor @Tongji University

Committer

David Shrader

GTM Tech Lead @Google

Committer

yangw

Cloud-native Engineer @DaoCloud

Committer

Ramakrishnan Sathyavageeswaran

Computer Science Engineer @Intuit

Committer

WUKUNTAI

Software Engineer @DELTA ELECTRONICS, INC.

Committer

Aayush Saini

SDE, Data and AI @Red Hat

Committer

siloteemu

Open Source Contributor

Committer

samzong

AI Infrastructure / Cloud-Native PM @DaoCloud

Committer

Asaad Balum

Senior Software Engineer @Red Hat

Committer

Noa Limoy

Software Engineer @Red Hat

Committer

Abdallah Samara

Senior Software Engineer @Red Hat

Committer

Hen Schwartz

Software Engineer @Red Hat

Committer

yuluo-yx

Individual Contributor

Committer

cryo-zd

Individual Contributor

Committer

Hao Wu

Individual Contributor

Meet the people turning Mixture-of-Models into shared infrastructure.

View All Team Members
Start building

Compose your Mixture-of-Models.

Shape every model path with signals, preferences, and policy.