For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
Digest
  • Getting Started
    • Quickstart
    • Installation
    • Support Matrix
    • Feature Matrix
    • Examples
  • Kubernetes Deployment
  • User Guides
    • Tool Calling
    • Multimodality Support
    • Finding Best Initial Configs
    • Dynamo Benchmarking Guide
    • Tuning Disaggregated Performance
    • Writing Python Workers in Dynamo
    • Glossary
  • Components
    • Router
  • Design Docs
    • Overall Architecture
    • Architecture Flow
    • Disaggregated Serving
    • Distributed Runtime
NVIDIANVIDIA
Developer-friendly docs for your API
Privacy Policy | Your Privacy Choices | Terms of Service | Accessibility | Corporate Policies | Product Security | Contact

Copyright © 2026, NVIDIA Corporation.

LogoLogoDocumentation
Digest
On this page
  • Quick Comparison
  • 1. vLLM Backend
  • 2. SGLang Backend
  • 3. TensorRT-LLM Backend
  • Source References
  • Backends
  • Design Docs
  • Multimodal
  • Feature-specific
Getting Started

Dynamo Feature Compatibility Matrices

||View as Markdown|
Previous

Dynamo Support Matrix

Next

Dynamo Examples

This document provides a comprehensive compatibility matrix for key Dynamo features across the supported backends.

Updated for Dynamo v0.8.0

Legend:

  • ✅ : Supported
  • 🚧 : Work in Progress / Experimental / Limited

Quick Comparison

FeaturevLLMTensorRT-LLMSGLangSource
Disaggregated Serving✅✅✅Design Doc
KV-Aware Routing✅✅✅Router Doc
SLA-Based Planner✅✅✅Planner Doc
KV Block Manager✅✅🚧KVBM Doc
Multimodal (Image)✅✅✅Multimodal Doc
Multimodal (Video)✅Multimodal Doc
Multimodal (Audio)🚧Multimodal Doc
Request Migration✅🚧✅Migration Doc
Request Cancellation✅✅🚧Backend READMEs
LoRA✅K8s Guide
Tool Calling✅✅✅Tool Calling Doc
Speculative Decoding✅✅🚧Backend READMEs

1. vLLM Backend

vLLM offers the broadest feature coverage in Dynamo, with full support for disaggregated serving, KV-aware routing, KV block management, LoRA adapters, and multimodal inference including video and audio.

Source: vLLM Backend

FeatureDisaggregated ServingKV-Aware RoutingSLA-Based PlannerKV Block ManagerMultimodalRequest MigrationRequest CancellationLoRATool CallingSpeculative Decoding
Disaggregated Serving—
KV-Aware Routing✅—
SLA-Based Planner✅✅—
KV Block Manager✅✅✅—
Multimodal✅1—✅—
Request Migration✅✅✅✅✅—
Request Cancellation✅✅✅✅✅✅—
LoRA✅✅2—✅—✅✅—
Tool Calling✅✅✅✅✅✅✅✅—
Speculative Decoding✅✅—✅—✅✅—✅—

Notes:

  1. Multimodal + KV-Aware Routing: The KV router uses token-based hashing and does not yet support image/video hashes, so it falls back to random/round-robin routing. (Source)
  2. KV-Aware LoRA Routing: vLLM supports routing requests based on LoRA adapter affinity.
  3. Audio Support: vLLM supports audio models like Qwen2-Audio (experimental). (Source)
  4. Video Support: vLLM supports video input with frame sampling. (Source)
  5. Speculative Decoding: Eagle3 support documented. (Source)

2. SGLang Backend

SGLang is optimized for high-throughput serving with fast primitives, providing robust support for disaggregated serving, KV-aware routing, and request migration.

Source: SGLang Backend

FeatureDisaggregated ServingKV-Aware RoutingSLA-Based PlannerKV Block ManagerMultimodalRequest MigrationRequest CancellationLoRATool CallingSpeculative Decoding
Disaggregated Serving—
KV-Aware Routing✅—
SLA-Based Planner✅✅—
KV Block Manager🚧🚧🚧—
Multimodal✅21—🚧—
Request Migration✅✅✅🚧✅—
Request Cancellation🚧3✅✅🚧🚧✅—
LoRA🚧—
Tool Calling✅✅✅🚧✅✅✅—
Speculative Decoding🚧🚧—🚧—🚧—🚧—

Notes:

  1. Multimodal + KV-Aware Routing: Not supported. (Source)
  2. Multimodal Patterns: Supports E/PD and E/P/D only (requires separate vision encoder). Does not support simple Aggregated (EPD) or Traditional Disagg (EP/D). (Source)
  3. Request Cancellation: Cancellation during the remote prefill phase is not supported in disaggregated mode. (Source)
  4. Speculative Decoding: Code hooks exist (spec_decode_stats in publisher), but no examples or documentation yet.

3. TensorRT-LLM Backend

TensorRT-LLM delivers maximum inference performance and optimization, with full KVBM integration and robust disaggregated serving support.

Source: TensorRT-LLM Backend

FeatureDisaggregated ServingKV-Aware RoutingSLA-Based PlannerKV Block ManagerMultimodalRequest MigrationRequest CancellationLoRATool CallingSpeculative Decoding
Disaggregated Serving—
KV-Aware Routing✅—
SLA-Based Planner✅✅—
KV Block Manager✅✅✅—
Multimodal✅12—✅—
Request Migration🚧3✅✅✅🚧—
Request Cancellation✅✅✅✅✅✅—
LoRA—
Tool Calling✅✅✅✅✅✅✅—
Speculative Decoding✅✅—✅—✅✅✅—

Notes:

  1. Multimodal Disaggregation: Fully supports EP/D (Traditional) pattern. E/P/D (Full Disaggregation) is WIP and currently supports pre-computed embeddings only. (Source)
  2. Multimodal + KV-Aware Routing: Not supported. The KV router currently tracks token-based blocks only. (Source)
  3. Request Migration: Supported on Decode/Aggregated workers only. Prefill workers do not support migration. (Source)
  4. Speculative Decoding: Llama 4 + Eagle support documented. (Source)

Source References

Backends

Design Docs

Multimodal

Feature-specific