Feature Matrix

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

Updated for Dynamo v1.2.0

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: Image-aware KV routing is supported in the documented vLLM paths. The default Rust frontend path supports model families handled by llm-multimodal; the Python chat-processor path delegates to vLLM’s multimodal processor. (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 simple Aggregated EPD, E/PD, and E/P/D patterns. Traditional Disagg EP/D is not supported. (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: Supports EP/D (Traditional) and E/P/D (Full Disaggregation) image flows, including image URLs and pre-computed embeddings. (Source)
2. Multimodal + KV-Aware Routing: Image-aware KV routing is supported through the dedicated TRT-LLM MM Router Worker. It requires KV event publishing on the TRT-LLM workers. (Source)
3. 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.

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