Router Guide

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), 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. This guide helps you get started with using the Dynamo router, with further details on configuration, disaggregated serving setup, and parameter tuning.

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_model API, after which the KV Router automatically tracks worker state and makes routing decisions based on KV cache overlap.

CLI Arguments

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

For detailed configuration options and tuning parameters, see Using the KV Cache Router.

Kubernetes Deployment

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

1
apiVersion: nvidia.com/v1alpha1
2
kind: DynamoGraphDeployment
3
metadata:
4
  name: my-deployment
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spec:
6
  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

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:

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.

KV Cache Routing

KV cache routing optimizes large language model inference by intelligently directing requests to workers with the most relevant cached data. By maximizing cache reuse, it reduces redundant computation and improves both throughput and latency.

KV Cache reuse introduces complexity to LLM serving load balancing. While it can significantly reduce computation costs, routing strategies that ignore worker-specific KV states can lead to:

• Missed cache reuse opportunities due to suboptimal worker selection
• System throughput degradation from uneven request distribution across workers

The router uses a cost function that considers both the prefill cost (influenced by cached blocks) and the decode load to make optimal routing decisions:

Cost Calculation

1. Prefill blocks: Calculated by dividing the number of tokens requiring prefill processing by the block size. The system predicts this based on input tokens and available cached blocks per worker, updating the count when the first output token signals prefill completion.

2. Decode blocks: Estimated from the request’s input tokens and each worker’s active sequences. The count updates when requests complete and their blocks are freed.

3. Cost formula: cost = overlap_score_weight * prefill_blocks + decode_blocks

   • Lower costs indicate better routing choices
   • overlap_score_weight balances cache hit optimization against load distribution
   • Higher weights favor cache reuse (improving TTFT), while lower weights prioritize even load distribution (improving ITL)

Worker Selection

The router selects the worker with the lowest cost. When router_temperature is set to a non-zero value, the router uses softmax sampling on the normalized cost logits to introduce randomness in the selection, which can help with load distribution.

Example calculation with overlap_score_weight = 1.0:

• Worker 1: cost = 1.0 * 8 + 10 = 18
• Worker 2: cost = 1.0 * 5 + 5 = 10 (selected - lowest cost)
• Worker 3: cost = 1.0 * 2 + 9 = 11

Using the KV Cache Router

To enable KV cache-aware routing, start the frontend node like this:

$
python -m dynamo.frontend --router-mode kv

When KV blocks are created or removed, the engine notifies the Dynamo router, which then identifies the worker with the best matching blocks and routes traffic accordingly.

To evaluate the benefits of KV-aware routing, compare your workload’s performance using --router-mode random|round-robin against KV-aware routing.

The main KV-aware routing arguments (frontend uses the same --router-* flag names as the standalone router; legacy names without the prefix are obsolete):

• --router-kv-overlap-score-weight: Controls the importance of prefix cache overlaps in prefill cost calculations. Higher values improve Time To First Token (TTFT) at the cost of Inter-Token Latency (ITL). When set to 0, the router ignores prefix caches and uses pure load balancing. Defaults to 1. .

• --router-temperature: Controls worker selection randomness through softmax sampling of router cost logits. A value of 0 (default) ensures deterministic selection of the lowest-cost worker, while higher values introduce more randomness.

• --no-router-kv-events: Disables KV event tracking. By default (when this flag is not provided), the router uses KV events to monitor block creation and deletion from workers. When disabled with this flag, the router predicts cache state based on routing decisions with TTL-based expiration (default 120s) and pruning. Use this flag if your backend doesn’t support KV events (or you are not confident in the accuracy or responsiveness of the events).

• --router-durable-kv-events: (Deprecated — will be removed in a future release.) Enables JetStream mode for KV event transport. The event-plane subscriber (local_indexer mode) is now the recommended path. When enabled, workers publish to JetStream instead of the local indexer, and the frontend consumes from JetStream as a durable consumer. Without this flag (default), workers use the local indexer with NATS Core or ZMQ event plane.

• --router-replica-sync: Disabled by default. Enables NATS-based synchronization of local routing decisions between router replicas. When enabled, routers share their active sequence information and local predictions of block usage, improving routing consistency across instances. Note that this does not sync the radix tree or cached KV block states themselves - in JetStream mode those are synchronized through JetStream events; in local indexer mode (default) each router queries workers directly.

• --router-reset-states: Only applies in JetStream mode (--router-durable-kv-events). When specified, resets the router state on startup by clearing both the JetStream event stream and NATS object store, starting with a fresh state. Warning: Using --router-reset-states can bring existing router replicas into an inconsistent state. Only use this flag when launching the first router replica in a component, or consider using a different namespace/component for a clean slate.

• --router-snapshot-threshold: Only applies in JetStream mode (--router-durable-kv-events). Sets the number of messages in the JetStream before triggering a snapshot. When the message count exceeds this threshold, a router will attempt to purge acknowledged messages from the stream and create a snapshot of the current radix tree state in NATS object store. Defaults to 1000000. This helps manage stream size and provides faster initialization for routers that restart.

• --no-router-track-active-blocks: Disables tracking of active blocks (blocks being used for ongoing generation/decode phases). By default, the router tracks active blocks for load balancing. Disable this when routing to workers that only perform prefill (no decode phase), as tracking decode load is not relevant. This reduces router overhead and simplifies state management. .

• --router-track-output-blocks: Enables tracking of output blocks during generation (default: disabled). When enabled, the router adds placeholder blocks as tokens are generated and applies fractional decay based on progress toward the expected output sequence length (agent_hints.osl in nvext). This improves load balancing accuracy for long-running generation requests by accounting for output-side KV cache growth.

• --no-router-assume-kv-reuse: When tracking active blocks, disables the assumption of KV cache reuse. By default (router_assume_kv_reuse=true), the router computes actual block hashes for sequence tracking to deduplicate blocks and optimize load balancing. When disabled via this flag, the router generates random hashes for sequence blocks, treating each request’s blocks as unique. This is useful in disaggregated setups where prefill transfers blocks to decode workers that may already have those blocks cached, but the engine cannot coordinate transfers to avoid duplication. Without this flag, the router’s load balancing heuristics would undercount decode blocks when duplicates exist.

• --router-queue-threshold: Queue threshold fraction for prefill token capacity. When set, the router holds incoming requests in a priority queue while all workers exceed this fraction of max_num_batched_tokens, releasing them when capacity frees up. This defers dispatch (not rejection) so that routing decisions use the most up-to-date load metrics at the moment the request is actually sent to a worker. It also enables priority scheduling via latency_sensitivity hints in nvext.agent_hints — higher values shift a request’s effective arrival time earlier in the queue, giving it priority over lower-valued requests. Must be > 0. If not set (default), queueing is disabled and requests are dispatched immediately.

• --active-decode-blocks-threshold: Initial threshold (0.0-1.0) for determining when a worker is considered busy based on KV cache block utilization. When a worker’s KV cache active blocks exceed this percentage of total blocks, it will be marked as busy and excluded from routing. If not set, blocks-based busy detection is disabled. This feature works with all routing modes (--router-mode kv|round-robin|random) as long as backend engines publish load metrics. The threshold can be dynamically updated at runtime via the /busy_threshold HTTP endpoint (see Dynamic Threshold Configuration).

• --active-prefill-tokens-threshold: Literal token count threshold for determining when a worker is considered busy based on prefill token utilization. When active prefill tokens exceed this threshold, the worker is marked as busy. If not set, tokens-based busy detection is disabled.

• --active-prefill-tokens-threshold-frac: Fraction of max_num_batched_tokens for busy detection. A worker is marked busy when active_prefill_tokens > frac * max_num_batched_tokens. Uses OR logic with --active-prefill-tokens-threshold (worker is busy if either threshold is exceeded). If not set, fractional busy detection is disabled.

• --router-ttl-secs: Time-to-live in seconds for blocks in the router’s local cache predictions. Blocks older than this duration will be automatically expired and removed from the router’s radix tree. Defaults to 120.0 seconds when --no-router-kv-events is used. This helps manage memory usage by removing stale cache predictions that are unlikely to be accurate.

• --router-max-tree-size: Maximum tree size (number of blocks) before pruning is triggered. When the total number of blocks in the radix tree exceeds this threshold, the router will prune the least recently used blocks. Defaults to 1048576 (2^20 blocks) when --no-router-kv-events is used. This prevents unbounded memory growth in long-running deployments.

• --router-prune-target-ratio: Target size ratio to prune down to when --router-max-tree-size is exceeded. For example, with a value of 0.8 (default) and max tree size of 1048576, the router will prune down to approximately 838860 blocks when the threshold is exceeded. Defaults to 0.8 when --no-router-kv-events is used. This creates headroom before the next pruning cycle.

• --router-event-threads: Number of event processing threads for the KV indexer. When set to 1 (default), the router uses a single-threaded radix tree with channel-based event processing, which supports TTL-based expiration and pruning. When set to a value greater than 1, the router uses a concurrent radix tree with a thread pool of the specified size for higher event throughput. Note: the concurrent indexer does not support TTL/pruning (--router-ttl-secs, --router-max-tree-size, --router-prune-target-ratio are ignored when --router-event-threads > 1). Can be set via DYN_ROUTER_EVENT_THREADS env var. For details on the underlying index data structures (RadixTree, ConcurrentRadixTree, PositionalIndexer) and their concurrency model (inline reads, sticky-routed writes via thread pool), see the KV Router Index documentation.

To implement KV event publishing for custom inference engines, enabling them to participate in Dynamo’s KV cache-aware routing, see KV Event Publishing for Custom Engines.

For details on per-request agent hints (latency_sensitivity, osl, speculative_prefill), see the Agent Hints Guide.

Basic Routing

Dynamo supports several routing strategies when sending requests from one component to another component’s endpoint.

First, we must create a client tied to a components endpoint, we can do this using the labels defined above. Here we are getting a client tied to the generate endpoint of the VllmWorker component.

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client = namespace('dynamo').component('VllmWorker').endpoint('generate').client()

We can then use the default routing methods exposed by the client class to send requests to the VllmWorker component.

• Random routing: Default strategy, available via client.generate() or client.random()
• Round-robin routing: Cycles through available workers via client.round_robin()
• Direct routing: Explicitly targets a specific worker via client.direct(input, component_id)

KV Cache routing uses direct routing with a special worker selection algorithm.

For benchmarking KV router performance, see the KV Router A/B Benchmarking Guide.

For custom routing logic and advanced patterns, see Routing Patterns in the examples documentation.

Tuning Guidelines

1. Understand Your Workload Characteristics

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

Prometheus Metrics

The router exposes Prometheus metrics on the frontend’s HTTP port (default 8000) at /metrics. All router metrics require --router-mode kv and will not appear when using round-robin or random routing.

For the full list of router metrics (dynamo_router_*, dynamo_router_overhead_*, per-worker gauges), see the Metrics reference.

Disaggregated Serving

Dynamo supports disaggregated serving where prefill (prompt processing) and decode (token generation) are handled by separate worker pools. When you register workers with ModelType.Prefill (see Backend Guide), the frontend automatically detects them and activates an internal prefill router.

Automatic Prefill Router Activation

The prefill router is automatically created when:

1. A decode model is registered (e.g., via register_model() with ModelType.Chat | ModelType.Completions)
2. A prefill worker is detected with the same model name and ModelType.Prefill

Key characteristics of the prefill router:

• Always disables active block tracking (track_active_blocks=false) since prefill workers don’t perform decode
• Seamlessly integrated into the request pipeline between preprocessing and decode routing
• Falls back gracefully to decode-only mode if prefill fails or no prefill workers are available

Setup Example

When both workers are registered, requests are automatically routed.

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# Decode worker registration (in your decode worker)
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decode_endpoint = runtime.namespace("dynamo").component("decode").endpoint("generate")
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await register_model(
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    model_input=ModelInput.Tokens,
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    model_type=ModelType.Chat | ModelType.Completions,
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    endpoint=decode_endpoint,
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    model_name="meta-llama/Llama-2-7b-hf",
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    # ... other parameters
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)
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await decode_endpoint.serve_endpoint(decode_handler.generate)
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# Prefill worker registration (in your prefill worker)
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prefill_endpoint = runtime.namespace("dynamo").component("prefill").endpoint("generate")
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await register_model(
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    model_input=ModelInput.Tokens,
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    model_type=ModelType.Prefill,  # <-- Mark as prefill worker
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    endpoint=prefill_endpoint,
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    model_name="meta-llama/Llama-2-7b-hf",  # Must match decode model name
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    # ... other parameters
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)
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await prefill_endpoint.serve_endpoint(prefill_handler.generate)

Request Flow

The following diagram shows an overview of the major components in disaggregated serving:

Serving Multiple Router Replicas

For improved fault tolerance, you can launch multiple frontend + router replicas. Since the frontend and router are currently tied together, you’ll need to use different HTTP ports for each instance. (The separation of the frontend and Router is WIP.)

Router State Management

The KV Router tracks two types of state (see Router Design for details):

1. Prefix blocks (cached KV blocks): Maintained in a radix tree, tracking which blocks are cached on each worker. This state is persistent - in local indexer mode (default) state is rebuilt from workers on startup; in JetStream mode (--router-durable-kv-events) it is backed by JetStream events and object store snapshots.

2. Active blocks (decoding blocks): Tracks blocks currently being used for active generation requests. This state is ephemeral - when a new router replica starts, it begins with zero active block knowledge but becomes eventually consistent as it handles requests.

Enabling Router Replica Synchronization

$
# Router replica 1
$
python -m dynamo.frontend --router-mode kv --http-port 8000 --router-replica-sync
$
$
# Router replica 2 (can be started later)
$
python -m dynamo.frontend --router-mode kv --http-port 8001 --router-replica-sync

The --router-replica-sync flag enables active block synchronization between replicas:

• Active blocks are shared via NATS core messaging (fire-and-forget)
• Replicas exchange routing decisions to maintain consistent load estimates
• A new replica starts with zero active blocks but quickly converges through request handling, by itself and active syncing with other replicas

Without this flag, each replica maintains its own isolated view of active blocks, potentially leading to suboptimal routing.

Persistence and Recovery

Persistence behavior depends on which event transport mode is active:

NATS Core / Event Plane with Local Indexer Mode (default):

• State persists on workers—events are fire-and-forget but workers retain their local indexer state
• On startup, the router queries each worker’s local indexer to rebuild state
• Recovery depends on workers being available; if a worker is down, its blocks cannot be recovered
• Simpler infrastructure (no JetStream required)

JetStream Mode (--router-durable-kv-events on both frontend and workers):**

• Prefix blocks are stored in NATS JetStream with 1-hour retention
• Snapshots saved to NATS object store at configurable thresholds
• New replicas automatically restore this state on startup
• You can launch a third Router replica even if the first two are down, and it will recover the full prefix state

$
python -m dynamo.frontend --router-mode kv --http-port 8002 --router-replica-sync

Dynamic Threshold Configuration

Dynamic threshold configuration allows you to adjust worker busy thresholds at runtime without restarting the frontend, enabling real-time tuning of load balancing behavior based on observed system performance.

The busy thresholds can be updated at runtime without restarting the frontend. The frontend exposes HTTP endpoints at /busy_threshold:

Get or set a model’s thresholds (POST):

$
# Set both thresholds for a model
$
curl -X POST http://localhost:8000/busy_threshold \
>
  -H "Content-Type: application/json" \
>
  -d '{"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85, "active_prefill_tokens_threshold": 1000}'
$
# Response: {"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85, "active_prefill_tokens_threshold": 1000}
$
$
# Set only active decode blocks threshold
$
curl -X POST http://localhost:8000/busy_threshold \
>
  -H "Content-Type: application/json" \
>
  -d '{"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85}'
$
# Response: {"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85, "active_prefill_tokens_threshold": <current_value>}
$
$
# Get current thresholds (omit threshold fields)
$
curl -X POST http://localhost:8000/busy_threshold \
>
  -H "Content-Type: application/json" \
>
  -d '{"model": "meta-llama/Llama-2-7b-hf"}'
$
# Response: {"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85, "active_prefill_tokens_threshold": 1000}
$
# Or if not configured: {"model": "...", "active_decode_blocks_threshold": null, "active_prefill_tokens_threshold": null}

List all configured thresholds (GET):

$
curl http://localhost:8000/busy_threshold
$
# Response: {"thresholds": [{"model": "meta-llama/Llama-2-7b-hf", "active_decode_blocks_threshold": 0.85, "active_prefill_tokens_threshold": 1000}]}

See Also

• Router README: Quick start guide for the KV Router
• Router Examples: Python API usage, K8s examples, and custom routing patterns
• KV Router Index Data Structures: RadixTree, ConcurrentRadixTree, and PositionalIndexer internals and concurrency model
• Router Design: Architecture details and event transport modes
• KV Event Publishing for Custom Engines: Integrate custom inference engines with KV-aware routing
• Prometheus and Grafana Setup: General Prometheus/Grafana configuration
• Metrics Developer Guide: How the Dynamo metrics API works