Dynamo Feature Compatibility Matrices

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

Updated for Dynamo v0.9.0

Legend:

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

Quick Comparison

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: docs/backends/vllm/README.md

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: docs/backends/sglang/README.md

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: docs/backends/trtllm/README.md

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)
5. Request Cancellation: Due to known issues, the TensorRT-LLM engine is temporarily not notified of request cancellations, meaning allocated resources for cancelled requests are not freed.