Multimodal Inference in Dynamo#

Dynamo supports multimodal inference across multiple LLM backends, enabling models to process images, video, and audio alongside text. This section provides comprehensive documentation for deploying multimodal models.

Important

Security Requirement: Multimodal processing must be explicitly enabled at startup. See the relevant documentation for each backend for the necessary flags.

This prevents unintended processing of multimodal data from untrusted sources.

Backend Documentation#

Support Matrix#

Backend Capabilities#

Stack

E/PD

E/P/D

EP/D

EPD

Image

Video

Audio

vLLM

✅

✅

✅

✅

✅

✅

🧪

TRT-LLM

❌

🚧*

✅

✅

✅

❌

❌

SGLang

✅

✅

❌

❌

✅

❌

❌

* E/P/D supported in TRT-LLM with pre-computed embeddings only; image URL support is WIP (PR #4668)

Pattern Key:

  • EPD - All-in-one worker (Simple Aggregated)

  • E/PD - Separate encode, combined prefill+decode

  • E/P/D - All stages separate

  • EP/D - Combined encode+prefill, separate decode

Status: ✅ Supported | 🚧 WIP | 🧪 Experimental | ❌ Not supported

Input Format Support#

Format

vLLM

TRT-LLM

SGLang

HTTP/HTTPS URL

✅

✅

✅

Data URL (Base64)

✅

❌

❌

Pre-computed Embeddings (.pt)

❌

✅

❌

Architecture Patterns#

Dynamo supports several deployment patterns for multimodal inference based on two dimensions:

  1. Encoding: Is media encoding handled inline (within prefill) or by a separate Encode Worker?

    • Inline: Simpler setup, encoding happens in the prefill worker

    • Separate (EPD): Dedicated encode worker transfers embeddings via NIXL (RDMA), enabling independent scaling

  2. Prefill/Decode: Are prefill and decode in the same worker or separate?

    • Aggregated: Single worker handles both prefill and decode

    • Disaggregated: Separate workers for prefill and decode, with KV cache transfer between them

These combine into four deployment patterns:

EPD - Simple Aggregated#

All processing happens within a single worker - the simplest setup.

HTTP Frontend (Rust)
    ↓
Worker (Python)
    ↓ image load + encode + prefill + decode
Response

Component

Purpose

Frontend (Rust)

HTTP entry point, tokenization, image URL preprocessing

Worker

Complete inference pipeline (encode + prefill + decode)

When to use: Quick setup, smaller models, development/testing.

E/PD - Encode Separate#

Encoding happens in a separate worker; prefill and decode share the same engine.

HTTP Frontend (Rust)
    ↓
Processor (Python)
    ↓ tokenizes, extracts media URL
Encode Worker (Python)
    ↓ downloads media, generates embeddings, NIXL transfer
PD Worker (Python)
    ↓ receives embeddings via NIXL, prefill + decode
Response

Component

Purpose

Frontend (Rust)

HTTP entry point

Processor (Python)

Tokenization, extracts media URLs

Encode Worker

Media encoding, embeddings generation

PD Worker

Prefill + Decode with embeddings

When to use: Offload vision encoding to separate GPU, scale encode workers independently.

E/P/D - Full Disaggregation#

Full disaggregation with separate workers for encoding, prefill, and decode. There are two variants of this workflow:

  • Prefill-first, used by vLLM

  • Decode-first, used by SGlang

Prefill-first:

HTTP Frontend (Rust)
    ↓
Processor (Python)
    ↓ tokenizes, extracts media URL
Encode Worker (Python)
    ↓ downloads media, generates embeddings, NIXL transfer
Prefill Worker (Python)
    ↓ receives embeddings via NIXL, prefill only, KV cache transfer
Decode Worker (Python)
    ↓ decode only, token generation
Response

OR

Decode-first:

HTTP Frontend (Rust)
    ↓
Processor (Python)
    ↓ tokenizes, extracts media URL
Encode Worker (Python)
    ↓ downloads media, generates embeddings, NIXL transfer
Decode Worker (Python)
    ↓ Bootstraps prefill worker
Prefill Worker (Python)
    ↓ receives embeddings via NIXL, prefill only, KV cache transfer
Decode Worker (Python)
    ↓ decode only, token generation
Response

Component

Purpose

Frontend (Rust)

HTTP entry point

Processor (Python)

Tokenization, extracts media URLs

Encode Worker

Media encoding, embeddings generation

Prefill Worker

Prefill only, transfers KV cache

Decode Worker

Decode only, token generation

When to use: Maximum optimization, multi-node deployment, independent scaling of each phase.

EP/D - Traditional Disaggregated#

Encoding is combined with prefill, with decode separate.

HTTP Frontend (Rust)
    ↓
Processor (Python)
    ↓ tokenizes, extracts media URL
Encode+Prefill Worker (Python)
    ↓ downloads media, encodes inline, prefill, KV cache transfer
Decode Worker (Python)
    ↓ decode only, token generation
Response

Component

Purpose

Frontend (Rust)

HTTP entry point

Processor (Python)

Tokenization, extracts media URLs (vLLM only)

Encode+Prefill Worker

Combined encoding and prefill

Decode Worker

Decode only, token generation

Note: TRT-LLM’s EP/D mode skips the Python Processor - the Rust frontend handles tokenization and routes directly to the Prefill worker. For multimodal requests, the Python prefill worker still re-tokenizes/builds inputs; Rust token_ids are ignored.

When to use: Models without pre-computed embedding support (Llama 4), or TRT-LLM disaggregated deployment.

Example Workflows#

You can find example workflows and reference implementations for deploying multimodal models in:

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