Evaluating Kubernetes Performance Genai

Kubernetes-Native GenAI Inference Platform Design

This skill enables Claude to architect, configure, and optimize multi-stage GenAI inference pipelines on Kubernetes using three complementary components: Kueue for batch job scheduling with priority and preemption, Dynamic Accelerator Slicer (DAS) for just-in-time GPU partitioning via NVIDIA MIG, and the Gateway API Inference Extension (GAIE) with llm-d for prefix-cache-aware LLM request routing. The technique is drawn from an industry paper demonstrating that these components form a cohesive platform delivering up to 15% makespan reduction (Kueue), 36% faster mean job completion (DAS), and 90% improvement in tail TTFT latency (GAIE) on real ASR-to-summarization workloads.

When to Use

• When the user asks to deploy batch AI inference jobs (e.g., Whisper transcription, image generation) on Kubernetes and needs efficient GPU scheduling
• When the user wants to run more parallel GPU jobs on limited hardware by slicing GPUs with MIG profiles
• When the user is deploying an LLM serving layer (vLLM, llm-d) and needs to reduce Time to First Token under load
• When the user needs to build a multi-stage pipeline where batch output (e.g., transcripts) feeds into online inference (e.g., summarization)
• When the user asks how to configure Kueue ClusterQueues, ResourceFlavors, or priority-based preemption for GPU workloads
• When the user wants to understand or implement prefix-cache-aware routing for LLM inference on Kubernetes

Key Technique

The paper's core insight is that three Kubernetes-native projects address three distinct bottlenecks in GenAI inference and, when combined, eliminate compounding inefficiencies:

Kueue replaces Kubernetes' default scheduler for batch workloads by introducing a hierarchical queue system (ResourceFlavors -> ClusterQueues -> LocalQueues). It admits jobs based on available cluster capacity rather than letting pods pile up in Pending state. With BestEffortFIFO ordering, priority classes, and preemption policies, Kueue ensures high-value jobs (e.g., large Whisper models) preempt lower-priority work, reducing total makespan by up to 15%. Critically, Kueue's admission latency stays under 25ms at P99 even with complex multi-queue configurations.

DAS (Dynamic Accelerator Slicer) solves GPU underutilization by creating NVIDIA MIG partitions just-in-time based on pod resource requests. Instead of allocating a full A100 (40GB) to a job that only needs 5GB, DAS provisions a 1g.5gb slice, enabling 25 concurrent jobs where only 8 could run before. Individual jobs run ~20% slower on slices, but the 3x parallelism gain produces a net 36% reduction in mean completion time. DAS coordinates with the NVIDIA GPU Operator and binds scheduling to physical slice allocation.

GAIE + llm-d tackles online inference latency through inference-aware request routing. The Endpoint Picker Plugin (EPP) in GAIE inspects each request's prompt tokens and routes it to the vLLM replica whose KV-cache already contains the longest matching prefix. This "prefix-cache-aware" scheduling achieves a 6.25x improvement in TTFT (500ms -> 80ms) compared to random routing, with P99 tail latency improving by up to 90%. The overhead of the routing layer itself is only 3-11ms.

Step-by-Step Workflow

1. Assess the workload type: Classify each stage of the pipeline as batch inference (finite jobs processing a dataset) or online inference (request-response serving). Batch stages use Kueue + DAS; online stages use GAIE + llm-d.

2. Define ResourceFlavors for your GPU inventory: Create ResourceFlavor CRDs that describe each distinct GPU type or MIG profile available in the cluster (e.g., nvidia-a100-40gb, nvidia-a100-1g5gb). Map these to node labels and taints.

3. Configure Kueue's queue hierarchy: Create a ClusterQueue with resource quotas matching your GPU capacity. For multi-tenant or multi-priority workloads, create multiple ClusterQueues with borrowing limits. Attach LocalQueues in each namespace where jobs will be submitted.

4. Assign priority classes to job types: Create PriorityClass resources for each workload tier (e.g., large-model-high, medium-model-default). Configure preemption policies on the ClusterQueue so higher-priority jobs can reclaim resources from lower-priority ones.

5. Configure DAS profiles for GPU slicing: Define DASProfile CRDs specifying which MIG profiles to use for each workload size. For example: medium Whisper (769M params) -> 1g.5gb; large Whisper (1.55B params) -> 3g.20gb. Ensure the NVIDIA GPU Operator is installed with MIG support enabled.

6. Create batch job templates: Write Kubernetes Job manifests that request specific GPU slice resources (e.g., nvidia.com/mig-1g.5gb: 1). Set the Kueue queue label (kueue.x-k8s.io/queue-name) so jobs are admitted through the queue system rather than directly scheduled.

7. Deploy the LLM serving layer with llm-d: Deploy vLLM replicas serving your target model (e.g., Qwen3-8B) with prefix caching enabled. Create InferencePool and InferenceModel CRDs to register the model endpoint with GAIE.

8. Configure GAIE routing with the Endpoint Picker Plugin: Set up the InferenceGateway with an Envoy-based service mesh. Configure the EPP to use prefix-cache-aware scheduling ("precise" mode) rather than random or round-robin routing.

9. Wire the pipeline stages together: Implement a controller or workflow engine (e.g., Argo Workflows, Tekton) that submits batch transcription jobs, collects outputs, then sends them as requests to the LLM serving endpoint for summarization.

10. Benchmark and tune: Use kube-burner for batch workload orchestration and GuideLLM for online inference benchmarking. Monitor Kueue admission latency, DAS slice utilization, and GAIE TTFT/E2E latency. Adjust queue borrowing limits, MIG profiles, and vLLM replica count based on results.

Concrete Examples

Example 1: Configuring Kueue for GPU batch transcription

User: "I have 8 A100 GPUs and need to run 32 Whisper transcription jobs. Some are large model, some medium. How do I set up Kueue to prioritize the large jobs?"

Approach:

1. Create two PriorityClasses: whisper-large (value: 1000) and whisper-medium (value: 500)
2. Define a ResourceFlavor for nvidia.com/gpu on the GPU node
3. Create a ClusterQueue with quota of 8 GPUs, preemption policy ReclaimWithinCohort
4. Create a LocalQueue in the workload namespace
5. Label each Job with the appropriate priority class and queue name

Output:

apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: gpu-queue
spec:
  queueingStrategy: BestEffortFIFO
  preemption:
    reclaimWithinCohort: Any
    withinClusterQueue: LowerPriority
  resourceGroups:
  - coveredResources: ["nvidia.com/gpu"]
    flavors:
    - name: a100-40gb
      resources:
      - name: nvidia.com/gpu
        nominalQuota: 8
---
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  name: transcription-queue
  namespace: asr-pipeline
spec:
  clusterQueue: gpu-queue
---
apiVersion: batch/v1
kind: Job
metadata:
  name: whisper-large-001
  namespace: asr-pipeline
  labels:
    kueue.x-k8s.io/queue-name: transcription-queue
spec:
  template:
    spec:
      priorityClassName: whisper-large
      containers:
      - name: whisper
        image: openai/whisper:large-v3
        resources:
          limits:
            nvidia.com/gpu: 1
      restartPolicy: Never

Expected behavior: Kueue admits jobs up to the 8-GPU quota. When a whisper-large job arrives and all GPUs are occupied by whisper-medium jobs, Kueue preempts a medium job to free a GPU. Admission latency stays under 25ms.

Example 2: Enabling DAS GPU slicing for higher parallelism

User: "My Whisper medium model only needs 5GB of GPU memory but each job gets a full 40GB A100. Can I run more jobs in parallel?"

Approach:

1. Enable MIG mode on the A100 GPUs via the NVIDIA GPU Operator
2. Configure DAS with a profile mapping: medium Whisper -> 1g.5gb MIG slice
3. Update job manifests to request nvidia.com/mig-1g.5gb instead of nvidia.com/gpu
4. DAS dynamically creates/destroys MIG partitions as pods are scheduled

Output:

apiVersion: inference.networking.x-k8s.io/v1alpha2
kind: DASProfile
metadata:
  name: whisper-medium-profile
spec:
  migProfile: 1g.5gb
  accelerator: nvidia-a100-40gb
---
apiVersion: batch/v1
kind: Job
metadata:
  name: whisper-medium-017
  labels:
    kueue.x-k8s.io/queue-name: transcription-queue
spec:
  template:
    spec:
      containers:
      - name: whisper
        image: openai/whisper:medium
        resources:
          limits:
            nvidia.com/mig-1g.5gb: 1
      restartPolicy: Never

Expected behavior: Each A100 is split into up to 7 MIG slices. Cluster capacity rises from 8 concurrent jobs (full GPUs) to ~25 (sliced). Individual jobs take ~17 min instead of ~14 min, but mean completion across all 32 jobs drops from 28 min to 18 min (36% reduction).

Example 3: Prefix-cache-aware LLM routing with GAIE and llm-d

User: "I have 8 vLLM replicas serving Qwen3-8B for summarization. Under load, TTFT spikes to 500ms+. How do I reduce it?"

Approach:

1. Deploy llm-d as the inference serving layer with prefix caching enabled on each vLLM instance
2. Create InferencePool and InferenceModel CRDs for the model
3. Configure the GAIE Endpoint Picker Plugin with prefix-cache-aware ("precise") scheduling
4. Route all summarization requests through the GAIE gateway

Output:

apiVersion: inference.networking.x-k8s.io/v1alpha2
kind: InferencePool
metadata:
  name: summarization-pool
spec:
  targetPortNumber: 8000
  selector:
    matchLabels:
      app: vllm-qwen3-8b
  extensionRef:
    name: endpoint-picker
---
apiVersion: inference.networking.x-k8s.io/v1alpha2
kind: InferenceModel
metadata:
  name: qwen3-8b-summarizer
spec:
  modelName: Qwen/Qwen3-8B
  poolRef:
    name: summarization-pool
  criticality: Critical

Expected behavior: The EPP inspects each incoming request's prompt tokens and routes to the replica with the longest matching prefix in its KV-cache. TTFT drops from ~500ms (random routing) to ~80ms (prefix-aware). P99 tail latency improves by up to 90%. The routing layer adds only 3-11ms overhead, which is far outweighed by cache hit gains.

Best Practices

• Do start with Kueue even before adding DAS -- the admission control alone prevents GPU thrashing from too many pending pods and gives you deterministic scheduling with <25ms overhead.
• Do right-size MIG profiles to your model's actual VRAM needs. A Whisper medium model (769M params) fits in 1g.5gb; don't allocate 3g.20gb unless the model requires it.
• Do enable prefix caching on vLLM replicas (--enable-prefix-caching) before configuring GAIE's prefix-aware routing -- the routing is useless without cached prefixes.
• Do benchmark with realistic prompt distributions. Prefix-cache-aware routing benefits degrade if prompts share no common prefixes.
• Avoid using DAS MIG slicing for models that saturate GPU compute -- slicing helps memory-bound workloads but hurts compute-bound ones.
• Avoid setting Kueue borrowing limits too aggressively in multi-queue setups. Overly generous borrowing defeats the purpose of resource isolation between teams.

Error Handling

• DAS slice creation failure: If MIG partitioning fails (e.g., incompatible GPU driver version), pods remain Pending. Ensure the NVIDIA GPU Operator version supports MIG mode for your GPU generation. A100 and H100 support MIG; older GPUs (V100, T4) do not.
• Kueue admission deadlock: If all ClusterQueues are at quota and preemption is disabled, new jobs stall indefinitely. Always configure at least withinClusterQueue: LowerPriority preemption for production queues.
• GAIE routing overhead under very low load: At low request rates, prefix-cache-aware routing may show slightly higher latency than random due to the EPP lookup. This overhead (3-11ms) only matters if your latency budget is extremely tight and request volume is low.
• KV-cache cold start: After replica restarts or scale-up events, new vLLM instances have empty caches. The EPP will naturally route fewer requests to cold replicas, but initial requests will see higher TTFT until caches warm.
• MIG profile mismatch: Requesting a MIG profile that doesn't match the GPU's supported configurations causes scheduling failures. Verify supported profiles with nvidia-smi mig -lgip on the target node.

Limitations

• GPU hardware requirement: DAS/MIG slicing requires NVIDIA Ampere (A100) or newer GPUs. Older GPUs like V100 or T4 do not support MIG and cannot use DAS.
• Kueue-DAS integration is nascent: As of the paper, Kueue does not natively understand MIG slice capacity. Jobs must request specific MIG device types, and Kueue treats them as opaque resources. GPU-memory-aware scheduling integration is ongoing.
• Single-node scope in experiments: The paper's testbed used a single p4d.24xlarge (8x A100). Multi-node GPU scheduling with DAS and Kueue introduces additional complexity around topology-aware placement.
• Prefix-cache routing assumes shared prompt structure: The GAIE prefix-aware scheduler provides the greatest benefit when requests share long common prefixes (e.g., system prompts, repeated document context). For fully unique prompts, it degrades to near-random performance.
• GAIE maturity: The Gateway API Inference Extension and llm-d are early-stage projects. CRD APIs may change between releases. Pin versions in production.

Reference

Evaluating Kubernetes Performance for GenAI Inference: From Automatic Speech Recognition to LLM Summarization -- Malleni et al., 2026. Focus on Sections 3-5 for Kueue queue hierarchy design, DAS MIG profile configuration, and GAIE prefix-cache-aware routing benchmarks. The paper's GitHub repository contains reproducible manifests for all experiments.