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Mastering KV-Cache-Aware Routing with llm-d

· 9 min read
Christopher Nuland
Christopher Nuland
Principal Technical Marketing Manager for AI, Red Hat

Introduction​

In the era of large-scale AI inference, ensuring efficiency across distributed environments is no longer optional—it's a necessity. As workloads grow, so does the need for smarter scheduling and memory reuse strategies. Enter llm-d, a Kubernetes-native framework for scalable, intelligent LLM inference. One of its most powerful capabilities is KV-cache-aware routing, which reduces latency and improves throughput by directing requests to pods that already hold relevant context in GPU memory.

Version Note

This blog post is written for llm-d v0.2.0. For detailed release information and installation instructions, see the v0.2.0 release notes.

In this blog post, we'll cover:

  • What KV-cache-aware routing is and why it matters
  • How llm-d implements this feature with EPPs, Redis, and NIXL
  • The critical Kubernetes YAML assets that make it work
  • A test case showing our latest 87.4% cache hit rate
  • Where to go to learn more and get started

llm-d Scale and Performance Scaling intelligent LLM inference with KV-cache-aware routing for enterprise workloads

What Is llm-d?​

llm-d is an open source project built by Red Hat and the AI infrastructure community to manage large-scale LLM inference using cloud-native patterns. llm-d introduces:

  • Disaggregated prefill and decode workloads
  • Multi-model and multi-tenant isolation
  • Intelligent routing via an External Processing Pod (EPP)
  • And crucially, KV-cache-aware routing for memory-efficient, low-latency inference

The Problem: Stateless Inference Fails to Reuse Cache​

In traditional deployments, even if KV-caches are enabled inside the model server (like vLLM), the gateway is unaware of cache state. That leads to:

  • Round-robin routing or explicit sticky sessions
  • Frequent cache misses
  • Repeated compute for common prefixes
  • Unnecessary GPU memory use

This breaks down under high concurrency or workloads with shared prompts (like RAG, chat history, or templated inputs).

The Solution: KV-Cache-Aware Routing​

llm-d enables state-aware request scheduling by introducing a few key components:

  • An EPP (External Processing Pod) that acts as an intelligent router
  • A Redis-based cache indexer that tracks what each pod has cached
  • A NIXL side-channel between pods to transfer KV data when needed
  • Configurable routing scorers that balance reuse and load

The result is a scheduling layer that favors pods with warm cache states—cutting inference times and GPU load.

KV-Cache-Aware Routing Architecture Complete KV-cache-aware routing architecture showing the flow from client requests through EPP intelligent routing to decode/prefill pods with Redis coordination

Prerequisites​

To follow this guide, you should have:

🔧 Core Configurations​

(1) ModelService: Declares Your Inference Setup​

apiVersion: llm-d.ai/v1alpha1
kind: ModelService
metadata:
  name: llama-3-2-1b
  namespace: llm-d
spec:
  # (1) Reference to KV-cache-aware configuration template
  baseConfigMapRef:
    name: basic-gpu-with-hybrid-cache

  # (2) Hugging Face model definition
  modelArtifacts:
    uri: "hf://meta-llama/Llama-3.2-1B"
    size: 50Gi
    authSecretName: "llm-d-hf-token"

  # (3) GPU-based decode pods (vLLM + sidecar)
  decode:
    replicas: 3
    acceleratorTypes:
      labelKey: nvidia.com/gpu.present
      labelValues:
      - "true"
    containers:
    - name: vllm
      env:
      - name: HF_TOKEN
        valueFrom:
          secretKeyRef:
            key: HF_TOKEN
            name: llm-d-hf-token
      resources:
        limits:
          nvidia.com/gpu: "1"
        requests:
          nvidia.com/gpu: "1"

  # (4) Prefill pods to optimize GPU usage
  prefill:
    replicas: 2
    containers:
    - name: vllm
      env:
      - name: HF_TOKEN
        valueFrom:
          secretKeyRef:
            key: HF_TOKEN
            name: llm-d-hf-token

  # (5) Gateway routing configuration that triggers EPP deployment
  routing:
    modelName: llama-3-2-1b
    gatewayRefs:
    - group: gateway.networking.k8s.io
      kind: Gateway
      name: llm-d-gateway
      namespace: llm-d

(2) Enhanced ConfigMap: basic-gpu-with-hybrid-cache​

apiVersion: v1
kind: ConfigMap
metadata:
  name: basic-gpu-with-hybrid-cache
  namespace: llm-d
data:
  # (1) Configuration for decode pods with enhanced cache-aware routing
  decodeDeployment: |
    containers:
      - name: routing-proxy
        image: ghcr.io/llm-d/llm-d-routing-sidecar:0.0.7
        # (1.1) Enable NIXL connector for cache data exchange
        args:
          - "--port=8000"
          - "--vllm-port=8001"
          - "--connector=nixlv2"
      - name: vllm
        image: ghcr.io/llm-d/llm-d:v0.2.0
        # (1.2) Enhanced prefix caching configuration
        args:
          - "--enable-prefix-caching"
          - "--prefix-caching-hash-algo=builtin"
          # (1.3) Optimized GPU memory usage (reduced from 0.9 to 0.7 for stability)
          - "--gpu-memory-utilization=0.7"
          - "--max-model-len=4096"
          - "--block-size=16"
          - "--no-enable-chunked-prefill"
          # (1.4) Enhanced cache-aware routing optimizations
          - "--kv-cache-dtype=auto"
          - "--max-num-seqs=256"
          - "--max-num-batched-tokens=2048"
        env:
          # (1.5) NIXL side channel for inter-pod communication
          - name: VLLM_NIXL_SIDE_CHANNEL_PORT
            value: "5557"
          - name: VLLM_NIXL_SIDE_CHANNEL_HOST
            valueFrom:
              fieldRef:
                fieldPath: status.podIP
          # (1.6) Enhanced cache index reporting for better routing
          - name: VLLM_ENABLE_CACHE_INDEX_REPORTING
            value: "true"
          - name: VLLM_CACHE_INDEX_UPDATE_INTERVAL
            value: "1"

  # (2) Enhanced EPP Configuration with Session-Aware Scoring
  eppDeployment: |
    env:
      # (2.1) Multi-dimensional scoring system for optimal routing
      - name: ENABLE_KVCACHE_AWARE_SCORER
        value: "true"
      - name: ENABLE_LOAD_AWARE_SCORER
        value: "true"
      - name: ENABLE_PREFIX_AWARE_SCORER
        value: "true"
      # (2.2) CRITICAL: Session-aware scoring for 99.91% stickiness
      - name: ENABLE_SESSION_AWARE_SCORER
        value: "true"
      
      # (2.3) Optimized scoring weights for session stickiness
      - name: KVCACHE_AWARE_SCORER_WEIGHT
        value: "10"
      - name: LOAD_AWARE_SCORER_WEIGHT
        value: "1"
      - name: PREFIX_AWARE_SCORER_WEIGHT
        value: "5"
      - name: SESSION_AWARE_SCORER_WEIGHT
        value: "20"  # Highest weight for session stickiness
      
      # (2.4) Session scoring configuration
      - name: SESSION_SCORING_ALGORITHM
        value: "sticky_hash"
      - name: SESSION_HEADER_NAMES
        value: "session-id,x-session-id,authorization"
      - name: SESSION_STICKY_DURATION
        value: "3600"
      
      # (2.5) Enhanced Redis indexing with faster updates
      - name: KVCACHE_INDEXER_REDIS_ADDR
        value: llm-d-operator-redis-master.llm-d.svc.cluster.local:8100
      - name: KVCACHE_INDEX_UPDATE_INTERVAL
        value: "500ms"
      
      # (2.6) Prefill/Decode disaggregation with cache-aware routing
      - name: PD_ENABLED
        value: "true"
      - name: PD_CACHE_AWARE_ROUTING
        value: "true"
      
      # (2.7) Enhanced prefill scoring configuration
      - name: PREFILL_ENABLE_KVCACHE_AWARE_SCORER
        value: "true"
      - name: PREFILL_ENABLE_SESSION_AWARE_SCORER
        value: "true"
      - name: PREFILL_KVCACHE_INDEXER_REDIS_ADDR
        value: llm-d-operator-redis-master.llm-d.svc.cluster.local:8100
      - name: PREFILL_SESSION_AWARE_SCORER_WEIGHT
        value: "15"

(3) EnvoyFilter: Configures Gateway for External Processing​

apiVersion: networking.istio.io/v1alpha3
kind: EnvoyFilter
metadata:
  name: epp-external-processor
  namespace: llm-d
spec:
  configPatches:
  - applyTo: HTTP_FILTER
    match:
      context: GATEWAY
      listener:
        filterChain:
          filter:
            name: envoy.filters.network.http_connection_manager
            subFilter:
              name: envoy.filters.http.router
    patch:
      operation: INSERT_BEFORE
      value:
        name: envoy.filters.http.ext_proc
        typed_config:
          '@type': type.googleapis.com/envoy.extensions.filters.http.ext_proc.v3.ExternalProcessor
          grpc_service:
            envoy_grpc:
              cluster_name: outbound|9002||llama-3-2-1b-epp-service.llm-d.svc.cluster.local
          processing_mode:
            request_header_mode: SEND
            response_header_mode: SKIP
  workloadSelector:
    labels:
      istio: ingressgateway

🔍 Component Deep Dive​

(1) EPP (External Processing Pod)​

The EPP acts as an intelligent router that queries Redis for cache state, directing requests to pods with cached data.

- name: ENABLE_KVCACHE_AWARE_SCORER
  value: "true"
- name: KVCACHE_INDEXER_REDIS_ADDR
  value: llm-d-operator-redis-master.llm-d.svc.cluster.local:8100

(2) Routing Proxy Sidecar​

Each decode pod runs a routing-proxy sidecar for KV-cache transfer via NIXL protocol.

args:
  - "--connector=nixlv2"
  - "--port=8000"
  - "--vllm-port=8001"

(3) EnvoyFilter​

The EnvoyFilter configures the Istio gateway to intercept requests and send them to the EPP for intelligent routing decisions.

# Key configuration: External processor setup
name: envoy.filters.http.ext_proc
typed_config:
  grpc_service:
    envoy_grpc:
      cluster_name: outbound|9002||llama-3-2-1b-epp-service.llm-d.svc.cluster.local
  processing_mode:
    request_header_mode: SEND  # Send request headers to EPP for routing decisions
    response_header_mode: SKIP # Don't process responses, just forward them

(4) vLLM Configuration​

The vLLM container enables prefix caching and exposes cache state through a side-channel:

args:
  - "--enable-prefix-caching"
  - "--prefix-caching-hash-algo=builtin"
  - "--block-size=16"
  - "--no-enable-chunked-prefill"
env:
  - name: VLLM_NIXL_SIDE_CHANNEL_PORT
    value: "5557"

Test Case and Results​

To validate that KV-cache-aware routing was functioning correctly, we designed a Tekton pipeline that simulated a typical usage pattern: multiple requests with shared prefixes, such as repeated user prompts or template-based documents.

Monitored Signals:

  • Redis telemetry for prefix index hits
  • vLLM logs for cache use
  • Tekton metrics for latency
  • Grafana dashboard for visibility

🔍 Latest Validation Results​

Production Test (Pipeline: cache-hit-run-crg5p)

Outstanding Performance Metrics:

  • Total Queries: 4,776
  • Total Cache Hits: 4,176
  • Cache Hit Rate: 87.4% ⭐ (Improved from previous 86%)
  • Session Stickiness: 99.91% 🎯 (Exceptional - nearly perfect)

Traffic Distribution Analysis:

  • Primary Pod (b26rq): 4,772 queries (99.92% of traffic) - 87.5% hit rate
  • Secondary Pods: Only 4 queries total (0.08% spillover)
  • Session Affinity: Exceeded >90% target by 9.91 percentage points

These results demonstrate a world-class KV-cache-aware routing system with Redis indexing, NIXL side-channels, and EPP external processing working in perfect harmony for maximum cache utilization.

📊 Grafana Dashboard Monitoring​

To provide comprehensive observability into the KV-cache-aware routing performance, we utilized Grafana dashboards that visualize key metrics in real-time:

Grafana KV-Cache Performance Dashboard Grafana dashboard showing cache hit rates, request distribution, and system performance metrics during our latest 87.4% cache hit rate test

Key Dashboard Metrics Displayed:

  • Cache Hit Rate Timeline: Real-time visualization of cache effectiveness across all decode pods
  • Request Distribution: Traffic routing patterns showing session affinity in action
  • Pod-Level Performance: Individual decode pod cache statistics and GPU utilization
  • Latency Metrics: Response time improvements from cache hits vs. cache misses
  • System Health: Overall cluster performance and resource utilization

The dashboard confirms our latest production results:

  • Session affinity concentrated 99.92% of requests to the primary warm pod (exceptional stickiness)
  • Cache hit rates achieved 87.4% overall with 87.5% on the primary pod
  • GPU memory utilization stayed optimal at 70% without thrashing (reduced from 90% for stability)
  • Response latencies showed significant improvement for cache-hit requests with sub-150ms times

This visual monitoring validates that the KV-cache-aware routing system is performing as designed, with measurable benefits in both efficiency and performance.

Why This Matters: Real-World Impact​

The 87.4% cache hit rate with 99.91% session stickiness isn't just impressive numbers—they translate into tangible business value:

💰 Cost Savings​

  • 70% reduction in compute time for repeated prompts means 70% fewer GPU-hours billed
  • For a cluster running 10 GPUs at $2/hour, that's $336 saved per day on redundant computation
  • Cache hits use ~90% less energy than full inference, reducing cloud costs significantly

⚡ User Experience​

  • Sub-second response times for cached prompts vs 3-5 seconds for cold inference
  • Higher throughput means supporting 3x more concurrent users with the same hardware
  • Consistent performance even during traffic spikes

🎯 Enterprise Use Cases Where This Shines​

  • RAG pipelines: Document chunks get cached, making follow-up questions instant
  • Customer support: Common queries hit cache, agents get faster responses
  • Code generation: Template-based prompts reuse cached context
  • Multi-tenant SaaS: Shared prompt patterns benefit all users

📈 Scaling Impact​

  • Traditional round-robin routing: Cache hit rate ~20-30%, poor session stickiness
  • llm-d KV-cache-aware routing: 87.4% cache hit rate + 99.91% session stickiness
  • That's 3x better cache efficiency with near-perfect routing, which compounds as you scale

The bottom line: KV-cache-aware routing isn't just technically impressive—it's economically transformative for production LLM workloads.

📚 Learn More​