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vLLM Office Hours #34

Huamin Chen

Distinguished Engineer, Red Hat

Saša Zelenović

Principal PMM, Red Hat

Special Topic: AI-Powered vLLM Semantic Router

Michael Goin

Principal Software Engineer, Red Hat

vLLM Committer

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What’s new in the past two weeks?

Upcoming vLLM Office Hours Sessions

  • [TODAY] AI-Powered vLLM Semantic
  • [Oct. 23] How to build and contribute to vLLM
  • [Nov. 6] Break. Working on a surprise!
  • [Nov. 20] vLLM-triton-backend: How to get SOTA performance on NVIDIA and AMD with just Triton

Register for all sessions here.

View previous recordings here.

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vLLM Project Update

  • DeepSeek-V3.2
  • PipelineRL: In-flight weight updates
  • vLLM Encoder Support via Transformers
  • bota
  • vLLM v0.11.0 is out!

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Join our [Virtual] Red Hat AI Events

Your Path to Enterprise-Ready AI

Red Hat AI: Day of Learning�October 16, 2025

  • Deep-dive’s from Red Hat and IBM experts on:
    • ⚡ Fast & efficient inference
    • 🎯 Model customization
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Accelerate You AI with AMD Resources

Free GPU hours for Office Hours attendees

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  • AMD Developer Cloud with MI300X 192G HBM
  • Free 100 hours of credit for all vLLM office hours participants
  • Additional credits on request
    • Email sasa@redhat.com

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What is vLLM?

The fastest growing de-facto standard in open source model serving

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Broad Model Support (>100 arches)

DeepSeek

Wide Hardware Support

CUDA

ROCm

Gaudi/XPU

TPU

Neuron

CPU

$ uv pip install vllm --torch-backend=auto

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$ vllm serve deepseek-ai/DeepSeek-V3.1 -tp 8

Llama

GPT-OSS

Qwen

Gemma

Mistral

Granite

Ascend

Phi

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DeepSeek-V3.2: How does DeepSeek Sparse Attention (DSA) work?

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Day 0 support for CUDA, Ascend, MLU!

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PipelineRL: In-flight weight updates during RL

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vLLM Encoder Support via Transformers

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bota: Dota 2 agent powered by GPT-OSS-20B and vLLM

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🏆 OpenAI Open Model Hackathon Winner - Wildcard category

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What’s new in vLLM v0.11.0

🎉 538 commits from 207 contributors, including 65 new contributors! 🎉

Model Support

  • New architectures: DeepSeek-V3.2-Exp (#25896), Qwen3-VL series (#24727), Qwen3-Next (#24526), OLMo3 (#24534), LongCat-Flash (#23991), Dots OCR (#24645), Ling2.0 (#24627), CWM (#25611).
  • Encoders: RADIO encoder support (#24595), Transformers backend support for encoder-only models (#25174), BERT token classification/NER (#24872), pooling multimodal models (#24451).
  • Data parallel for vision encoders: InternVL (#23909), Qwen2-VL (#25445), Qwen3-VL (#24955).
  • Speculative decoding: EAGLE3 for MiniCPM3 (#24243) and GPT-OSS (#25246).
  • Performance: GLM-4.1V 916ms TTFT reduction via fused RMSNorm (#24733), GLM-4 MoE SharedFusedMoE optimization (#24849), Qwen2.5-VL CUDA sync removal (#24741), Qwen3-VL Triton MRoPE kernel (#25055), FP8 checkpoints for Qwen3-Next (#25079).
  • Reasoning: SeedOSS reason parser (#24263).

Engine Core

  • CPU KV cache offloading with LRU management (#19848, #20075, #21448, #22595, #24251).
  • V1 features: Prompt embeddings (#24278), sharded state loading (#25308), FlexAttention sliding window (#24089), LLM.apply_model (#18465).
  • Hybrid allocator: Pipeline parallel (#23974), vary hidden sizes (#25101).
  • Async scheduling uniproc executor support (#24219).
  • Architecture: Tokenizer group removal (#24078), shared memory multimodal caching (#20452).
  • Attention: Hybrid SSM/Attention in Triton (#21197), FlashAttention 3 for ViT (#24347).
  • Performance: FlashInfer RoPE (#21126), fused Q/K RoPE (#24511, #25005), Batch Parallel Ngram (#24986), FlashInfer Spec Decoding support (#25196), avoid inputs_embeds copy (#25739).
  • CUDA graph mode FULL_AND_PIECEWISE default (#25444).
  • torch.compile CUDA graph Inductor partition integration (#24281)

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What’s new in vLLM v0.11.0

🎉 538 commits from 207 contributors, including 65 new contributors! 🎉

Hardware & Performance

  • NVIDIA: FP8 FlashInfer MLA (#24705), FlashInfer BF16 MoE (#25503).
  • DeepGEMM: Enabled by default (#24462), contiguous layout 5% throughput improvement (#24783).
  • CPU: RISC-V support (#22112), ARM 4-bit fused MoE (#23809).
  • AMD: ROCm 7.0 (#25178), GLM-4.5 MI300X tuning (#25703).
  • Intel XPU: MoE DP accuracy fix (#25465).

Quantization

  • FP8: Per-token-group quant CustomOp (#24342), hardware-accelerated quant (#24757), tune paged attention fp8 kv cache (#22222).
  • FP4: FBGEMM NVFP4 for dense models (#25609), ModelOpt Gemma3 (#22771).
  • W4A8: Faster Machete preprocessing (#23972).
  • Compressed Tensors: Blocked FP8 for MoE (#25219).

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Large Scale Serving & Performance

  • Dual-Batch Overlap (DBO): Overlapping computation mechanism (#23693), DeepEP high throughput + prefill (#24845).
  • Data Parallelism: torchrun launcher (#24899), Ray placement groups (#25026), Triton DP/EP kernels (#24588).
  • EPLB: Hunyuan V1 (#23078), Mixtral (#22842), static placement (#23745), reduced overhead (#24573).
  • Disaggregated serving: KV transfer metrics (#22188), NIXL MLA latent dimension (#25902).
  • MoE: Shared expert overlap optimization (#24254), SiLU kernel for DeepSeek-R1 (#24054), Enable Allgather/ReduceScatter backend for NaiveAllToAll (#23964).
  • Distributed: NCCL symmetric memory with 3-4% throughput improvement (#24532), enabled by default for TP (#25070).

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What’s new in vLLM v0.11.0

🎉 538 commits from 207 contributors, including 65 new contributors! 🎉

API & Frontend

  • OpenAI: Prompt logprobs for all tokens (#24956), logprobs=-1 for full vocab (#25031), reasoning streaming events (#24938), Responses API MCP tools (#24628, #24985), health 503 on dead engine (#24897).
  • Multimodal: Media UUID caching (#23950), image path format (#25081).
  • Tool calling: XML parser for Qwen3-Coder (#25028), Hermes-style tokens (#25281).
  • Config: env var validation (#24761), NVTX profiling (#25501), guided decoding backward compatibility (#25615, #25422).
  • Metrics: V1 TPOT histogram (#24015), hidden deprecated gpu_ metrics (#24245), KV cache GiB units (#25204, #25479).

Dependencies

  • PyTorch 2.8 for CPU (#25652), FlashInfer 0.3.1 (#24470), CUDA 13 (#24599), ROCm 7.0 (#25178).
  • Build requirements: C++17 now enforced globally (#24823).

V0 Deprecation

  • Engines: AsyncLLMEngine (#25025), LLMEngine (#25033), MQLLMEngine (#25019), core (#25321), model runner (#25328), MP executor (#25329).
  • Components: Attention backends (#25351), encoder-decoder (#24907), output processor (#25320), sampling metadata (#25345), Sequence/Sampler (#25332).
  • Interfaces: LoRA (#25686), async output processor (#25334), MultiModalPlaceholderMap (#25366), seq group methods (#25330), placeholder attention (#25510), input embeddings (#25242), multimodal registry (#25362), max_seq_len_to_capture (#25543), attention classes (#25541), hybrid models (#25400), backend suffixes (#25489), compilation fallbacks (#25675), default args (#25409).

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AI-Powered vLLM Semantic Router

Huamin Chen

Distinguished Engineer, Red Hat

Today’s special topic:

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Problem Statement

Based on MMLU-Pro benchmark, Llama-3.1-8B is behind Qwen2-7B in Math, but excels in Chemistry.

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curl -X POST http://vllm-semantic-router/v1/chat/completions \

-H "Content-Type: application/json" \

-d '{

"model": "Llama-3.1-8B-Instruct",

"messages": [

{

"role": "user",

"content": "what is the derivative of x^2"

}

]}'

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  • Generic system prompt doesn’t improve inference quality
  • Setting reasoning effort for simple questions won’t improve accuracy, but resulting in more token usage
  • Setting wrong max token in reasoning mode could truncate chat completion output, resulting in wrong conclusion

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curl -X POST http://vllm-semantic-router/v1/chat/completions \

-H "Content-Type: application/json" \

-d '{

"model": "gpt-oss-120b",

"messages": [

{

"role": "system",

"content": "You are a helpful assistant",

"role": "user",

"content": "what is the 100th fibonacci number"

}

],

max_tokens: 100,

reasoning_effort: "high",

}'

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GenAI Development Like Assembly Language Programming

GenAI developers must master each model's specifics:

  • Parameter formats (chat_template_kwargs vs reasoning_effort)
  • Model-specific reasoning families (qwen3, deepseek, gpt-oss)
  • Token limits and pricing structures

Semantic Router as High-Level Language

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Semantic Router: Intelligent Model Routing System

Core Value

  • Open Source and democratize advanced AI gateway features.
  • Abstracts away model specific complexity.

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Core Functionalities

  • ModernBERT Classification: Uses trained ModernBERT models for category classification, PII detection, and jailbreak protection to automatically route requests to appropriate models
  • Auto Model Selection and Configuration: Replaces hardcoded model names with "model": "auto". Router analyzes prompt content and selects optimal models from configured categories (math, creative, code, etc.) and automatically sets max_tokens, reasoning_effort, and model-specific parameters based on query complexity and category classification results
  • Semantic Caching: Implements similarity-based caching using embeddings to return cached responses for semantically similar queries, reducing redundant model calls
  • Envoy ExtProc Integration: Built as an Envoy External Processor service in Go, providing production-ready HTTP traffic management with header-based routing to backend model endpoints
  • Prompt Protection: Detect and handle policy-driven PII and jailbreak in prompts.

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Router Reasoning Bench

Goal:

  • Prove that semantic router's intelligent automation delivers equivalent or better performance than manual model configuration while dramatically reducing complexity for developers.

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Feature:

  • Academic Performance Testing: Evaluates semantic router against direct vLLM inference across multiple academic datasets (MMLU, GPQA, ARC, TruthfulQA, CommonsenseQA, HellaSwag) to measure real-world reasoning capabilities
  • Reasoning Mode Comparison: Tests three distinct approaches - neutral baseline (NR), explicit chain-of-thought prompting (XC), and model-native reasoning (NR_REASONING) - to understand how different reasoning strategies affect accuracy
  • Auto vs Manual Routing: Compares semantic router's automatic model selection (model: "auto") against manually configured direct model calls to demonstrate the benefits of intelligent routing
  • Token Optimization Analysis: Measures and compares token usage, response times, and cost efficiency between router-managed requests and direct vLLM calls with dataset-optimized token limits
  • Production Readiness Validation: Provides comprehensive metrics (accuracy, latency, token consumption) to validate that semantic router maintains or improves performance while simplifying model management in production environments

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Disclaimer: Work in progress, final numbers may vary

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Disclaimer: Work in progress, final numbers may vary

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Ecosystem Support

  • Semantic Cache
    • In-memory (Development)
    • Milvus (persistent)
  • Gateway
    • Support Kubernetes Gateway API Inference Extension
    • Support Envoy AI Gateway

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Demo

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Get involved with the vLLM Community

Contribute to key vLLM features�Comment and review PRs that are interesting to you. Join the discussion on RFCs. Check out “good first issue” tags.

Give Us Feedback�We’ll email you today’s recording as soon as it’s ready. Respond and tell us what we are doing right and what we can do better with vLLM office hours. Or comment on this slide!

Join vLLM Developer Slack�Ask questions and engage with us via Slack. Join here.

Join Red Hat’s vLLM Mission�Red Hat wants to bring open-source LLMs and vLLM to every enterprise on the planet. We are looking for vLLM Engineers to help us accomplish our mission. Apply here.

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Thank you, and see you in two weeks!

Huamin Chen�Distinguished Engineer, Red Hat

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Saša Zelenović�Principal Product Marketing Manager, Red Hat

Michael Goin�Principal Software Engineer, Red Hat�vLLM Committer

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