Router#

The Dynamo KV Router intelligently routes requests by evaluating their computational costs across different workers. It considers both decoding costs (from active blocks) and prefill costs (from newly computed blocks), using KV cache overlap to minimize redundant computation. Optimizing the KV Router is critical for achieving maximum throughput and minimum latency in distributed inference setups.

Quick Start#

Python / CLI Deployment#

To launch the Dynamo frontend with the KV Router:

python -m dynamo.frontend --router-mode kv --http-port 8000

This command:

  • Launches the Dynamo frontend service with KV routing enabled

  • Exposes the service on port 8000 (configurable)

  • Automatically handles all backend workers registered to the Dynamo endpoint

Backend workers register themselves using the register_llm API, after which the KV Router automatically tracks worker state and makes routing decisions based on KV cache overlap.

CLI Arguments#

Argument

Default

Description

--router-mode kv

round_robin

Enable KV cache-aware routing

--router-temperature <float>

0.0

Controls routing randomness (0.0 = deterministic, higher = more random)

--kv-cache-block-size <size>

Backend-specific

KV cache block size (should match backend config)

--kv-events / --no-kv-events

--kv-events

Enable/disable real-time KV event tracking

--kv-overlap-score-weight <float>

1.0

Balance prefill vs decode optimization (higher = better TTFT)

For all available options: python -m dynamo.frontend --help

Kubernetes Deployment#

To enable the KV Router in Kubernetes, add the DYN_ROUTER_MODE environment variable to your frontend service:

apiVersion: nvidia.com/v1alpha1
kind: DynamoGraphDeployment
metadata:
  name: my-deployment
spec:
  services:
    Frontend:
      dynamoNamespace: my-namespace
      componentType: frontend
      replicas: 1
      envs:
        - name: DYN_ROUTER_MODE
          value: kv  # Enable KV Smart Router

Key Points:

  • Set DYN_ROUTER_MODE=kv on the Frontend service only

  • Workers automatically report KV cache events to the router

  • No worker-side configuration changes needed

Environment Variables#

All CLI arguments can be configured via environment variables using the DYN_ prefix:

CLI Argument

Environment Variable

Default

--router-mode kv

DYN_ROUTER_MODE=kv

round_robin

--router-temperature

DYN_ROUTER_TEMPERATURE

0.0

--kv-cache-block-size

DYN_KV_CACHE_BLOCK_SIZE

Backend-specific

--no-kv-events

DYN_KV_EVENTS=false

true

--kv-overlap-score-weight

DYN_KV_OVERLAP_SCORE_WEIGHT

1.0

For complete K8s examples and advanced configuration, see K8s Examples.

For A/B testing and advanced K8s setup, see the KV Router A/B Benchmarking Guide.

For more configuration options and tuning guidelines, see the Router Guide.

Prerequisites and Limitations#

Requirements:

  • Dynamic endpoints only: KV router requires register_llm() with model_input=ModelInput.Tokens. Your backend handler receives pre-tokenized requests with token_ids instead of raw text.

  • Backend workers must call register_llm() with model_input=ModelInput.Tokens (see Backend Guide)

  • You cannot use --static-endpoint mode with KV routing (use dynamic discovery instead)

Multimodal Support:

  • vLLM and TRT-LLM: Multimodal routing supported for images via multimodal hashes

  • SGLang: Image routing not yet supported

  • Other modalities (audio, video, etc.): Not yet supported

Limitations:

  • Static endpoints not supported—KV router requires dynamic model discovery via etcd to track worker instances and their KV cache states

For basic model registration without KV routing, use --router-mode round-robin or --router-mode random with both static and dynamic endpoints.

Next Steps#

  • Router Guide: Deep dive into KV cache routing, configuration, disaggregated serving, and tuning

  • Router Examples: Python API usage, K8s examples, and custom routing patterns

  • Router Design: Architecture details, algorithms, and event transport modes

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