KV Router

Overview

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). 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 the state of all registered workers
• Makes routing decisions based on KV cache overlap
• Balances load across available workers

Kubernetes Deployment

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

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apiVersion: nvidia.com/v1alpha1
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kind: DynamoGraphDeployment
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metadata:
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  name: my-deployment
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spec:
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  services:
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    Frontend:
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      dynamoNamespace: my-namespace
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      componentType: frontend
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      replicas: 1
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      envs:
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        - name: DYN_ROUTER_MODE
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          value: kv  # Enable KV Smart Router
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      extraPodSpec:
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        mainContainer:
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          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0
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    Worker:
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      # ... worker configuration ...

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

Complete K8s Examples:

• TRT-LLM aggregated router example
• vLLM aggregated router example
• SGLang aggregated router example
• Distributed inference tutorial

For A/B Testing and Advanced K8s Setup: See the comprehensive KV Router A/B Benchmarking Guide for step-by-step instructions on deploying, configuring, and benchmarking the KV router in Kubernetes.

Configuration Options

CLI Arguments (Python Deployment)

The KV Router supports several key configuration options:

• --router-mode kv: Enable KV cache-aware routing (required)

• --kv-cache-block-size <size>: Sets the KV cache block size (default: backend-specific). Larger blocks reduce overlap detection granularity but improve memory efficiency. This should match your backend configuration.

• --router-temperature <float>: Controls routing randomness (default: 0.0)

  • 0.0: Deterministic selection of the best worker
  • > 0.0: Probabilistic selection using softmax sampling
  • Higher values increase randomness, helping prevent worker saturation

• --kv-events / --no-kv-events: Controls how the router tracks cached blocks (default: --kv-events)

  • --kv-events: Uses real-time events from workers for accurate cache tracking
  • --no-kv-events: Uses approximation based on routing decisions (lower overhead, less accurate)

• --kv-overlap-score-weight <float>: Balance between prefill and decode optimization (default: 1.0)

  • Higher values (> 1.0): Prioritize reducing prefill cost (better TTFT)
  • Lower values (< 1.0): Prioritize decode performance (better ITL)

For a complete list of available options:

$
python -m dynamo.frontend --help

Kubernetes Environment Variables

All CLI arguments can be configured via environment variables in Kubernetes deployments. Use the DYN_ prefix with uppercase parameter names:

Example with Advanced Configuration

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apiVersion: nvidia.com/v1alpha1
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kind: DynamoGraphDeployment
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metadata:
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  name: my-deployment
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spec:
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  services:
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    Frontend:
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      dynamoNamespace: my-namespace
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      componentType: frontend
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      replicas: 1
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      envs:
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        - name: DYN_ROUTER_MODE
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          value: kv
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        - name: DYN_ROUTER_TEMPERATURE
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          value: "0.5"  # Add some randomness to prevent worker saturation
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        - name: DYN_KV_OVERLAP_SCORE_WEIGHT
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          value: "1.5"  # Prioritize TTFT over ITL
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        - name: DYN_KV_CACHE_BLOCK_SIZE
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          value: "16"
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      extraPodSpec:
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        mainContainer:
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          image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0

Alternative: Using Command Args in K8s

You can also pass CLI arguments directly in the container command:

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extraPodSpec:
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  mainContainer:
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    image: nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.6.0
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    command:
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      - /bin/sh
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      - -c
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    args:
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      - "python3 -m dynamo.frontend --router-mode kv --router-temperature 0.5 --http-port 8000"

Recommendation: Use environment variables for easier configuration management and consistency with Dynamo’s K8s patterns.

KV Router Architecture

The KV Router tracks two key metrics for each worker:

1. Potential Active Blocks: The number of blocks that would be used for decoding if a request is routed to a worker. This includes both existing active blocks and new blocks from the incoming request.

2. Potential New Prefill Blocks: The number of tokens that need to be computed from scratch on a worker, calculated as:

   • New prefill tokens = Total input tokens - (Overlap blocks × Block size)
   • Potential prefill blocks = New prefill tokens / Block size

Block Tracking Mechanisms

The router maintains block information through two complementary systems:

• Active Decoding Blocks: Tracked locally by the router throughout the request lifecycle:

  • Incremented when adding a new request
  • Updated during token generation
  • Decremented upon request completion

• Cached Blocks: Maintained globally by the KvIndexer using a prefix tree built from worker-reported KV events. This provides accurate overlap information for routing decisions.

Cost Function

The KV Router’s routing decision is based on a simple cost function:

logit = kv_overlap_score_weight × potential_prefill_blocks + potential_active_blocks

Where:

• Lower logit values are better (less computational cost)
• The router uses softmax sampling with optional temperature to select workers

Key Parameter: kv-overlap-score-weight

The kv-overlap-score-weight parameter (default: 1.0) controls the balance between prefill and decode optimization:

• Higher values (> 1.0): Emphasize reducing prefill cost

  • Prioritizes routing to workers with better cache hits
  • Optimizes for Time To First Token (TTFT)
  • Best for workloads where initial response latency is critical

• Lower values (< 1.0): Emphasize decode performance

  • Distributes active decoding blocks more evenly
  • Optimizes for Inter-Token Latency (ITL)
  • Best for workloads with long generation sequences

KV Events vs. Approximation Mode

The router uses KV events from workers by default to maintain an accurate global view of cached blocks. You can disable this with the --no-kv-events flag:

• With KV Events (default):

  • Calculates overlap accurately using actual cached blocks
  • Provides higher accuracy with event processing overhead
  • Recommended for production deployments

• Without KV Events (—no-kv-events):

  • Router predicts cache state based on routing decisions with TTL-based expiration and pruning
  • Tracks blocks from recent requests with configurable time-to-live
  • Reduces overhead at the cost of routing accuracy
  • NATS is not needed - suitable for simpler deployments without NATS infrastructure
  • Suitable for testing or when event processing becomes a bottleneck

Tuning Guidelines

1. Understand Your Workload Characteristics

• Prefill-heavy workloads (long prompts, short generations): Increase kv-overlap-score-weight
• Decode-heavy workloads (short prompts, long generations): Decrease kv-overlap-score-weight

2. Monitor Key Metrics

The router logs the cost calculation for each worker:

Formula for worker_1: 125.3 = 1.0 * 100.5 + 25.0 (cached_blocks: 15)

This shows:

• Total cost (125.3)
• Overlap weight × prefill blocks (1.0 × 100.5)
• Active blocks (25.0)
• Cached blocks that contribute to overlap (15)

3. Temperature-Based Routing

The router_temperature parameter controls routing randomness:

• 0.0 (default): Deterministic selection of the best worker
• > 0.0: Probabilistic selection, higher values increase randomness
• Useful for preventing worker saturation and improving load distribution

4. Iterative Optimization

1. Begin with default settings
2. Monitor TTFT and ITL metrics
3. Adjust kv-overlap-score-weight to meet your performance goals:
   • To reduce TTFT: Increase the weight
   • To reduce ITL: Decrease the weight
4. If you observe severe load imbalance, increase the temperature setting