feature-matrix.md

title
Feature Matrix

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

Feature	SGLang	TensorRT-LLM	vLLM	Source
Disaggregated Serving	✅	✅	✅	[Design Doc][disagg]
KV-Aware Routing	✅	✅	✅	[Router Doc][kv-routing]
SLA-Based Planner	✅	✅	✅	[Planner Doc][planner]
KV Block Manager	🚧	✅	✅	[KVBM Doc][kvbm]
Multimodal (Image)	✅	✅	✅	[Multimodal Doc][mm]
Multimodal (Video)			✅	[Multimodal Doc][mm]
Multimodal (Audio)			🚧	[Multimodal Doc][mm]
Request Migration	✅	🚧	✅	[Migration Doc][migration]
Request Cancellation	🚧	✅	✅	Backend READMEs
LoRA			✅	[K8s Guide][lora]
Tool Calling	✅	✅	✅	[Tool Calling Doc][tools]
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: [docs/backends/vllm/README.md][vllm-readme]

Feature	Disaggregated Serving	KV-Aware Routing	SLA-Based Planner	KV Block Manager	Multimodal	Request Migration	Request Cancellation	LoRA	Tool Calling	Speculative 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][kv-routing])
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][mm-vllm])
4. Video Support: vLLM supports video input with frame sampling. ([Source][mm-vllm])
5. Speculative Decoding: Eagle3 support documented. ([Source][vllm-spec])

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][sglang-readme]

Feature	Disaggregated Serving	KV-Aware Routing	SLA-Based Planner	KV Block Manager	Multimodal	Request Migration	Request Cancellation	LoRA	Tool Calling	Speculative Decoding
Disaggregated Serving	—
KV-Aware Routing	✅	—
SLA-Based Planner	✅	✅	—
KV Block Manager	🚧	🚧	🚧	—
Multimodal	✅2	1	—	🚧	—
Request Migration	✅	✅	✅	🚧	✅	—
Request Cancellation	🚧3	✅	✅	🚧	🚧	✅	—
LoRA				🚧				—
Tool Calling	✅	✅	✅	🚧	✅	✅	✅		—
Speculative Decoding	🚧	🚧	—	🚧	—	🚧	—		🚧	—

Notes:

1. Multimodal + KV-Aware Routing: Not supported. ([Source][kv-routing])
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][mm-sglang])
3. Request Cancellation: Cancellation during the remote prefill phase is not supported in disaggregated mode. ([Source][sglang-readme])
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][trtllm-readme]

Feature	Disaggregated Serving	KV-Aware Routing	SLA-Based Planner	KV Block Manager	Multimodal	Request Migration	Request Cancellation	LoRA	Tool Calling	Speculative Decoding
Disaggregated Serving	—
KV-Aware Routing	✅	—
SLA-Based Planner	✅	✅	—
KV Block Manager	✅	✅	✅	—
Multimodal	✅1	2	—	✅	—
Request Migration	✅	✅	✅	✅	🚧	—
Request Cancellation	✅3	✅3	✅3	✅3	✅3	✅3	—
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][mm-trtllm])
2. Multimodal + KV-Aware Routing: Not supported. The KV router currently tracks token-based blocks only. ([Source][kv-routing])
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.

---

{/* Backend READMEs — paths relative to rendered URL /getting-started/feature-matrix */} [vllm-readme]: ../backends/v-llm [sglang-readme]: ../backends/sg-lang [trtllm-readme]: ../backends/tensor-rt-llm

{/* Design Docs */} [disagg]: ../design-docs/disaggregated-serving [kv-routing]: ../components/router/router-guide [planner]: ../components/planner [kvbm]: ../components/kvbm [migration]: ../user-guides/fault-tolerance/request-migration [tools]: ../user-guides/tool-calling

{/* Multimodal */} [mm]: ../user-guides/multimodality-support [mm-vllm]: ../user-guides/multimodality-support/v-llm-multimodal [mm-trtllm]: ../user-guides/multimodality-support/tensor-rt-llm-multimodal [mm-sglang]: ../user-guides/multimodality-support/sg-lang-multimodal

{/* Feature-specific */} [lora]: ../kubernetes-deployment/deployment-guide/managing-models-with-dynamo-model [vllm-spec]: ../additional-resources/speculative-decoding/speculative-decoding-with-v-llm [trtllm-eagle]: ../additional-resources/tensor-rt-llm-details/llama-4-eagle