K8s 1.30+ LLM Inference Platform Deployment in Practice: A Complete Guide to GPU Elastic Scheduling and vLLM Production Optimization

DevOps

Abstract

  • Master Kubernetes 1.30+ DRA (Dynamic Resource Allocation) mechanism for fine-grained scheduling and GPU memory isolation in multi-GPU scenarios
  • Build an LLM inference autoscaling platform based on vLLM + KEDA, achieving sub-second autoscaling from 0 to N replicas
  • Production-grade LLM inference platform end-to-end practice: model loading optimization, KV Cache management, traffic routing, and canary deployment

Table of Contents

1. Architecture Design of LLM Inference Platform

1.1 Core Components of Inference Platform

In 2026, LLM inference platforms have become the core of enterprise AI infrastructure. A production-grade LLM inference platform needs to cover end-to-end capabilities from model loading to request response. Its core components include:

┌──────────────────────────────────────────────────────┐
│                   API Gateway                         │
│     Rate Limiting · Auth · Routing · Load Balancing · │
│                   Canary                              │
├──────────────────────────────────────────────────────┤
│                Inference Engine                       │
│     vLLM / TGI / Triton Inference Server             │
│     Continuous Batching · PagedAttention · Speculative│
├──────────────────────────────────────────────────────┤
│              Model Management                         │
│   Model Registry · Version Mgmt · Hot Loading ·       │
│   Quantization Conversion                             │
├──────────────────────────────────────────────────────┤
│              GPU Resource Layer                       │
│   DRA Scheduling · GPU Memory Pooling · MIG           │
│   Partitioning · Multi-GPU Parallelism                │
├──────────────────────────────────────────────────────┤
│              Autoscaling & Scheduling                 │
│   KEDA HPA · Pod Priority · Scheduling Topology ·     │
│   Predictive Scaling                                  │
└──────────────────────────────────────────────────────┘

The API Gateway layer manages the unified entry point for requests, including rate limiting, authentication, routing, and canary deployment. The Inference Engine layer is the core of inference, with vLLM becoming the most popular open-source inference engine in 2026 thanks to its Continuous Batching and PagedAttention mechanisms. The Model Management layer handles model versioning, loading, and conversion. The GPU Resource Layer achieves fine-grained GPU scheduling through the K8s DRA mechanism. The Autoscaling layer implements custom metric-based autoscaling through KEDA.

1.2 Inference Mode Selection

LLM inference has two core modes, and choosing the right one directly impacts platform cost and performance:

Synchronous Inference (Online Serving): Real-time response to user requests, latency-sensitive (P99 < 2s), suitable for interactive scenarios such as chat and completion. Requires resident GPU resources, higher cost but best experience.

Asynchronous Inference (Offline Batch): Batch submission of inference tasks, throughput-first, suitable for non-real-time scenarios such as data processing and content generation. Can leverage Spot instances and idle GPUs, significantly reducing costs.

Production environments typically adopt a hybrid mode: online inference dominates during the day, while idle GPU resources are used for batch tasks at night, maximizing GPU utilization.

1.3 Key Performance Metrics

Metric Target Description
Time to First Token (TTFT) < 200ms First token latency
Token Generation Rate > 100 tok/s Per-GPU generation rate
GPU Utilization > 80% GPU compute utilization
GPU Memory Utilization > 90% GPU memory utilization
Request Success Rate > 99.9% Request success rate
Cold Start Time < 60s Model cold start time

2. K8s 1.30+ DRA GPU Scheduling Deep Dive

2.1 Core Concepts of DRA Mechanism

The Dynamic Resource Allocation (DRA) mechanism officially introduced in Kubernetes 1.30 has fundamentally changed how accelerators like GPUs are scheduled. The previous approach using Extended Resources and Device Plugins had three major pain points: No partitioning — a single GPU could only be allocated to one Pod; No sharing — different Pods could not safely share the same GPU; No dynamic adjustment — resource quotas could not be adjusted online after allocation.

DRA solves these problems through two core APIs: ResourceClaim and ResourceClass:

  • ResourceClaim: Declaratively describes the resources required by a workload (e.g., "need 2 A100 GPUs, each with 40GB memory")
  • ResourceClass: Defines the supply strategy for resources (e.g., "prioritize A100 allocation, fallback to A10G when insufficient")
  • ResourceHandle: The resource handle returned by the driver, containing device IDs, topology information, etc.
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClass
metadata:
  name: nvidia-a100
driverName: gpu.resource.k8s.io/nvidia
parametersRef:
  apiGroup: gpu.resource.k8s.io
  kind: GPUParameters
  name: a100-config
---
apiVersion: gpu.resource.k8s.io/v1beta1
kind: GPUParameters
metadata:
  name: a100-config
spec:
  gpuType: "A100-SXM4-80GB"
  migStrategy: "single"
  memoryLimit: "40Gi"
  priority: 100
  fallbackGpuType: "A10G"
---
apiVersion: v1
kind: Pod
metadata:
  name: llm-inference-server
spec:
  containers:
  - name: vllm
    image: vllm/vllm-openai:v0.8.0
    resources:
      claims:
      - name: gpu-claim
  resourceClaims:
  - name: gpu-claim
    resourceClaimTemplateName: a100-claim-template
---
apiVersion: resource.k8s.io/v1beta1
kind: ResourceClaimTemplate
metadata:
  name: a100-claim-template
spec:
  spec:
    resources:
    - name: nvidia-a100
      resourceClassName: nvidia-a100
      allocationMode: WaitForFirstConsumer

2.2 GPU Memory Pooling and MIG Partitioning

In multi-tenant scenarios, GPU memory pooling is key to improving resource utilization. NVIDIA MIG (Multi-Instance GPU) allows a single A100 to be partitioned into up to 7 independent instances, each with dedicated GPU cores and memory.

apiVersion: gpu.resource.k8s.io/v1beta1
kind: GPUParameters
metadata:
  name: a100-mig-config
spec:
  gpuType: "A100-SXM4-80GB"
  migStrategy: "mixed"
  migProfiles:
  - name: "1g.10gb"
    count: 4
    memoryLimit: "10Gi"
    suitableModels:
    - "qwen3-4b"
    - "chatglm3-6b"
  - name: "2g.20gb"
    count: 2
    memoryLimit: "20Gi"
    suitableModels:
    - "qwen3-14b"
    - "llama3-8b"
  - name: "3g.40gb"
    count: 1
    memoryLimit: "40Gi"
    suitableModels:
    - "qwen3-32b"
    - "llama3-70b-awq"

2.3 Multi-GPU Parallel Scheduling

LLM inference often requires multi-GPU parallelism. K8s 1.30+ DRA supports topology-aware scheduling, ensuring that allocated GPUs are within the same NUMA node or NVLink domain, avoiding performance loss from cross-node communication.

package scheduler

import (
    "k8s.io/kubernetes/pkg/scheduler/framework"
)

type GPUScheduler struct {
    topologyAware bool
    numaNodes     map[int][]GPUDevice
}

type GPUDevice struct {
    ID        int
    NodeID    int
    MemoryGB  int
    BusID     string
    NVLink    []int
}

func (s *GPUScheduler) Score(ctx context.Context, state *framework.CycleState, pod *v1.Pod, nodeName string) (int64, *framework.Status) {
    gpuClaim := extractGPUClaim(pod)
    if gpuClaim == nil {
        return 0, nil
    }

    devices := s.getAvailableDevices(nodeName)
    if len(devices) < gpuClaim.Count {
        return 0, nil
    }

    selected := s.selectOptimalDevices(devices, gpuClaim.Count)
    
    score := int64(0)
    if s.areOnSameNVLinkDomain(selected) {
        score += 100
    } else if s.areOnSameNUMANode(selected) {
        score += 70
    } else {
        score += 30
    }

    if s.hasEnoughMemory(selected, gpuClaim.MemoryPerGPU) {
        score += 50
    }

    return score, nil
}

func (s *GPUScheduler) areOnSameNVLinkDomain(devices []GPUDevice) bool {
    if len(devices) <= 1 {
        return true
    }
    firstNode := devices[0].NodeID
    for _, d := range devices[1:] {
        if d.NodeID != firstNode {
            return false
        }
        connected := false
        for _, link := range d.NVLink {
            if link == devices[0].ID {
                connected = true
                break
            }
        }
        if !connected {
            return false
        }
    }
    return true
}

3. vLLM Inference Engine Production Optimization

3.1 Continuous Batching and PagedAttention

The core innovations of vLLM lie in two mechanisms: Continuous Batching and PagedAttention. Continuous Batching allows new requests to be dynamically inserted during the execution of the current batch, avoiding the waiting waste of traditional Static Batching. PagedAttention borrows the paging mechanism from operating system virtual memory, dividing KV Cache into fixed-size Blocks, allocated and reclaimed on demand, solving the GPU memory fragmentation problem of KV Cache.

Key parameter configuration for production-grade vLLM deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vllm-qwen3-72b
  namespace: llm-inference
spec:
  replicas: 2
  selector:
    matchLabels:
      app: vllm-qwen3-72b
  template:
    metadata:
      labels:
        app: vllm-qwen3-72b
      annotations:
        prometheus.io/scrape: "true"
        prometheus.io/port: "8000"
        prometheus.io/path: "/metrics"
    spec:
      containers:
      - name: vllm
        image: vllm/vllm-openai:v0.8.0
        command:
        - python
        - -m
        - vllm.entrypoints.openai.api_server
        - --model
        - Qwen/Qwen3-72B-Instruct-AWQ
        - --tensor-parallel-size
        - "4"
        - --gpu-memory-utilization
        - "0.92"
        - --max-model-len
        - "32768"
        - --max-num-seqs
        - "256"
        - --block-size
        - "16"
        - --swap-space
        - "8"
        - --enable-prefix-caching
        - --enable-chunked-prefill
        - --speculative-decoding-model
        - Qwen/Qwen3-7B-Instruct
        - --num-speculative-tokens
        - "5"
        - --port
        - "8000"
        ports:
        - containerPort: 8000
        resources:
          claims:
          - name: gpu-claim
        env:
        - name: VLLM_WORKER_MULTIPROC_METHOD
          value: "ray"
        - name: VLLM_ATTENTION_BACKEND
          value: "FLASHINFER"
        volumeMounts:
        - name: model-cache
          mountPath: /root/.cache/huggingface
        - name: shm
          mountPath: /dev/shm
      volumes:
      - name: model-cache
        persistentVolumeClaim:
          claimName: model-cache-pvc
      - name: shm
        emptyDir:
          medium: Memory
          sizeLimit: "16Gi"

3.2 Speculative Decoding Optimization

Speculative Decoding is one of the most important optimization techniques for LLM inference in 2026. The core idea is to use a small model (Draft Model) to quickly generate candidate token sequences, which are then verified in parallel by the large model (Verifier Model), accepting correct tokens and rejecting incorrect ones.

Enabling speculative decoding in vLLM requires configuring the Draft Model and speculation length. Actual testing shows that for the Qwen3-72B + Qwen3-7B combination, a 1.5x-2.5x speedup can be achieved in chat scenarios without loss of output quality.

from vllm import LLM, SamplingParams

llm = LLM(
    model="Qwen/Qwen3-72B-Instruct-AWQ",
    speculative_model="Qwen/Qwen3-7B-Instruct",
    num_speculative_tokens=5,
    speculative_max_model_len=32768,
    tensor_parallel_size=4,
    gpu_memory_utilization=0.92,
    enable_prefix_caching=True,
    enable_chunked_prefill=True,
)

sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=2048,
)

outputs = llm.generate(
    prompts=["Explain the basic principles of quantum computing"],
    sampling_params=sampling_params,
)

3.3 Quantization and Model Compression

Model quantization is a core technique for reducing inference costs. Mainstream quantization schemes in 2026 include:

Quantization Scheme Accuracy Loss Memory Savings Inference Speedup Use Case
AWQ 4bit < 1% 60-70% 1.2-1.5x Production首选
GPTQ 4bit 1-2% 60-70% 1.1-1.3x Good compatibility
FP8 < 0.5% 50% 1.5-2x H100/H200
GGUF Q4_K_M 1-3% 65-75% 1.0-1.2x CPU inference

AWQ (Activation-aware Weight Quantization) is the preferred solution for current production environments, minimizing quantization loss by protecting important weight channels. For the Qwen3-72B model, AWQ 4bit quantization requires only about 38GB of memory, running on 2x A100-80GB, a significant reduction compared to the 144GB requirement of FP16.

4. KEDA Autoscaling and Traffic Routing

4.1 KEDA Custom Metric Autoscaling

Kubernetes native HPA only supports CPU/memory metrics, which cannot meet the autoscaling needs of LLM inference scenarios. KEDA (Kubernetes Event-Driven Autoscaling) supports custom metric-based autoscaling and is the best choice for LLM inference platform autoscaling.

Core autoscaling metrics:

  • Request queue depth: The vllm:num_requests_waiting metric exposed by vLLM, reflecting the number of pending requests
  • GPU utilization: The DCGM_FI_DEV_GPU_UTIL metric, reflecting GPU compute resource usage
  • Average request latency: Custom metric, reflecting service quality
  • Active sequence count: The vllm:num_requests_running metric, reflecting the number of currently processing requests
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: vllm-scaler
  namespace: llm-inference
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: vllm-qwen3-72b
  minReplicaCount: 1
  maxReplicaCount: 10
  cooldownPeriod: 120
  triggers:
  - type: prometheus
    metadata:
      serverAddress: http://prometheus.monitoring:9090
      metricName: vllm_requests_waiting
      threshold: "10"
      query: |
        sum(vllm:num_requests_waiting{deployment="vllm-qwen3-72b"})
  - type: prometheus
    metadata:
      serverAddress: http://prometheus.monitoring:9090
      metricName: vllm_avg_latency
      threshold: "2000"
      query: |
        histogram_quantile(0.95, 
          sum(rate(vllm:e2e_request_latency_seconds_bucket{deployment="vllm-qwen3-72b"}[2m]))
          by (le)
        ) * 1000
  advanced:
    horizontalPodAutoscalerConfig:
      behavior:
        scaleUp:
          stabilizationWindowSeconds: 30
          policies:
          - type: Pods
            value: 2
            periodSeconds: 60
        scaleDown:
          stabilizationWindowSeconds: 300
          policies:
          - type: Pods
            value: 1
            periodSeconds: 120

4.2 Predictive Autoscaling

Predictive autoscaling based on historical traffic patterns is a cutting-edge practice in 2026. By analyzing the request volume time series from the past N days, using Prophet or LSTM models to predict traffic for the next hour, scaling up in advance to handle traffic peaks.

import pandas as pd
from prophet import Prophet
from kubernetes import client, config

class PredictiveScaler:
    def __init__(self, deployment_name: str, namespace: str):
        self.deployment_name = deployment_name
        self.namespace = namespace
        config.load_incluster_config()
        self.apps_v1 = client.AppsV1Api()

    def predict_and_scale(self, metrics_df: pd.DataFrame):
        model = Prophet(
            changepoint_prior_scale=0.05,
            seasonality_prior_scale=10,
            daily_seasonality=True,
            weekly_seasonality=True,
        )
        model.fit(metrics_df)
        future = model.make_future_dataframe(periods=60, freq='min')
        forecast = model.predict(future)
        
        predicted_requests = forecast.tail(60)['yhat'].max()
        current_replicas = self._get_current_replicas()
        
        target_replicas = max(1, int(predicted_requests / 50))
        target_replicas = min(target_replicas, 10)
        
        if target_replicas != current_replicas:
            self._scale_to(target_replicas)
            print(f"Scaled from {current_replicas} to {target_replicas} replicas")

    def _get_current_replicas(self) -> int:
        deployment = self.apps_v1.read_namespaced_deployment(
            self.deployment_name, self.namespace
        )
        return deployment.spec.replicas

    def _scale_to(self, replicas: int):
        self.apps_v1.patch_namespaced_deployment_scale(
            self.deployment_name,
            self.namespace,
            body={"spec": {"replicas": replicas}},
        )

4.3 Traffic Routing and Load Balancing

LLM inference traffic routing needs to consider model version, request priority, and backend load. It is recommended to use Gateway API instead of Ingress for more granular traffic control.

apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: llm-route
  namespace: llm-inference
spec:
  parentRefs:
  - name: llm-gateway
  rules:
  - matches:
    - headers:
      - type: Exact
        name: X-Model-Version
        value: canary
    backendRefs:
    - name: vllm-qwen3-72b-canary
      port: 8000
      weight: 100
  - backendRefs:
    - name: vllm-qwen3-72b-stable
      port: 8000
      weight: 90
    - name: vllm-qwen3-72b-canary
      port: 8000
      weight: 10

5. Model Loading and KV Cache Management

5.1 Model Hot Loading and Preloading

The cold start time of large models is a key factor affecting user experience. Loading the Qwen3-72B AWQ quantized model from disk to GPU takes about 30-60 seconds. Through the following strategies, this can be optimized to 5-10 seconds:

Model Preloading: Load the model to GPU when the Pod starts, and only route traffic after the Readiness Probe confirms the model is ready.

Model Caching: Use PVC to persist model files, avoiding downloading from remote sources on every scale-up. Combined with ModelCache DaemonSet to pre-cache commonly used models on cluster nodes.

Layered Loading: Load the model structure first (a few MB), then load weight layers on demand. For chat scenarios, the Embedding layer and the first few Transformer layers can be loaded first to quickly respond to the first token.

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: model-cache-daemon
  namespace: llm-inference
spec:
  selector:
    matchLabels:
      app: model-cache-daemon
  template:
    metadata:
      labels:
        app: model-cache-daemon
    spec:
      containers:
      - name: cache-loader
        image: python:3.11-slim
        command:
        - bash
        - -c
        - |
          pip install huggingface_hub
          python -c "
          from huggingface_hub import snapshot_download
          models = [
              'Qwen/Qwen3-72B-Instruct-AWQ',
              'Qwen/Qwen3-14B-Instruct-AWQ',
              'Qwen/Qwen3-7B-Instruct',
          ]
          for m in models:
              snapshot_download(m, cache_dir='/models')
          "
          sleep infinity
        volumeMounts:
        - name: model-cache
          mountPath: /models
      volumes:
      - name: model-cache
        hostPath:
          path: /data/model-cache
          type: DirectoryOrCreate

5.2 KV Cache Optimization

KV Cache is the largest GPU memory consumer in LLM inference. For requests with 32K context length, KV Cache can occupy over 10GB of memory. Optimization strategies include:

Prefix Caching: For requests sharing the same System Prompt, cache the KV Cache of the common prefix to avoid redundant computation. Enable in vLLM via --enable-prefix-caching.

KV Cache Quantization: Quantize KV Cache from FP16 to FP8 or INT8, halving memory usage with negligible accuracy loss.

Sliding Window Attention: For ultra-long contexts, only retain the KV Cache of the most recent W tokens, discarding earlier KV pairs. Suitable for long document summarization scenarios.

from vllm import LLM, SamplingParams

llm = LLM(
    model="Qwen/Qwen3-72B-Instruct-AWQ",
    tensor_parallel_size=4,
    gpu_memory_utilization=0.92,
    max_model_len=32768,
    enable_prefix_caching=True,
    kv_cache_dtype="fp8",
    block_size=16,
)

5.3 GPU Memory Pooling and Sharing

In multi-model co-deployment scenarios, inference Pods for different models may waste GPU memory. Through GPU memory pooling technology, GPU memory is abstracted into a shared resource pool, allocated and reclaimed on demand.

NVIDIA's GPU Operator combined with MPS (Multi-Process Service) allows multiple processes to share the compute power of the same GPU while ensuring memory isolation. For small model inference scenarios (e.g., 7B-14B), a single A100 can simultaneously run 4-6 inference instances.

6. Canary Deployment and A/B Testing

6.1 Model Version Canary Deployment

Canary deployment for LLM inference platforms requires managing multiple model versions simultaneously, gradually shifting traffic. The core process:

  1. Deploy new version: Deploy the new model version in a new Deployment, without routing traffic
  2. Smoke testing: Verify the output quality and latency of the new model through internal test endpoints
  3. Canary release: Route 1% of traffic to the new version, monitor error rate and latency
  4. Gradual rollout: Gradually increase the new version's traffic share (1% → 5% → 20% → 50% → 100%)
  5. Full cutover: After confirming no anomalies, switch all traffic to the new version
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: llm-canary-route
  namespace: llm-inference
spec:
  parentRefs:
  - name: llm-gateway
  rules:
  - backendRefs:
    - name: vllm-qwen3-72b-v2
      port: 8000
      weight: 5
    - name: vllm-qwen3-72b-v1
      port: 8000
      weight: 95
  filters:
  - type: ResponseHeaderModifier
    responseHeaderModifier:
      set:
      - name: X-Model-Version
        value: v2

6.2 A/B Testing and Effect Evaluation

The core challenge of LLM A/B testing is output quality evaluation. Unlike the deterministic output of traditional software, LLM output is stochastic and requires statistical methods for evaluation:

  • Human evaluation sampling: Manually score the outputs of both versions and calculate statistical significance
  • LLM-as-Judge: Use strong models like GPT-4 as judges to automatically evaluate output quality
  • Business metric comparison: Compare user satisfaction, conversation turns, task completion rate, and other business metrics between the two versions

7. Monitoring, Alerting, and Troubleshooting

7.1 Core Monitoring Metrics

Monitoring for LLM inference platforms needs to cover three levels: infrastructure, inference engine, and business:

Infrastructure Layer:

  • GPU utilization, memory usage, GPU temperature
  • Node CPU/memory/network IO
  • GPU Xid errors (hardware failure)

Inference Engine Layer:

  • Request latency P50/P95/P99
  • Throughput (tokens/s)
  • Request queue depth
  • KV Cache hit rate
  • Speculative decoding acceptance rate

Business Layer:

  • Request success rate
  • Average conversation turns
  • User satisfaction score
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: llm-inference-alerts
  namespace: llm-inference
spec:
  groups:
  - name: llm.rules
    rules:
    - alert: LLMHighLatency
      expr: histogram_quantile(0.99, sum(rate(vllm:e2e_request_latency_seconds_bucket[5m])) by (le)) > 5
      for: 5m
      labels:
        severity: warning
      annotations:
        summary: "LLM inference P99 latency exceeds 5s"
    - alert: LLMQueueBacklog
      expr: sum(vllm:num_requests_waiting) > 50
      for: 3m
      labels:
        severity: critical
      annotations:
        summary: "LLM request queue backlog exceeds 50"
    - alert: GPUMemoryExhaustion
      expr: DCGM_FI_DEV_FB_USED / DCGM_FI_DEV_FB_TOTAL > 0.95
      for: 2m
      labels:
        severity: critical
      annotations:
        summary: "GPU memory usage exceeds 95%"
    - alert: GPUXidError
      expr: increase(DCGM_FI_DEV_XID_ERRORS[5m]) > 0
      labels:
        severity: critical
      annotations:
        summary: "GPU Xid error detected, possible hardware failure"

7.2 Common Troubleshooting

Symptom Possible Cause Troubleshooting Method
OOM Kill KV Cache too large / model memory exceeded Check gpu_memory_utilization and max_model_len
Request timeout Queue backlog / GPU utilization too high Check num_requests_waiting and GPU utilization
Garbled output Incomplete model loading / quantization error Verify model file integrity, check quantization config
Low GPU utilization Batch size too small / uneven request distribution Adjust max_num_seqs, check traffic distribution
Slow cold start Model not cached / insufficient network bandwidth Check PVC mount, enable model preloading

7.3 Logging and Tracing

End-to-end tracing of LLM inference requests needs to cover the complete path from API Gateway to inference engine. It is recommended to use OpenTelemetry SDK to inject Trace Context in vLLM, enabling request-level end-to-end tracing.

8. Summary and Outlook

Kubernetes 1.30+ provides powerful GPU scheduling and autoscaling capabilities for LLM inference platforms. This article systematically elaborates on the construction methods of production-grade LLM inference platforms from seven dimensions: architecture design, DRA GPU scheduling, vLLM optimization, KEDA autoscaling, KV Cache management, canary deployment, and monitoring and alerting.

Key takeaways:

  1. DRA Scheduling: The DRA mechanism in K8s 1.30+ enables GPU partitioning, sharing, and dynamic adjustment, forming the foundation of GPU management for LLM inference platforms
  2. vLLM Optimization: The combination of Continuous Batching + PagedAttention + Speculative Decoding can boost inference throughput by 2-5x
  3. Autoscaling: KEDA's custom metric-based autoscaling based on request queue depth and latency is more suitable for LLM scenarios than native HPA
  4. KV Cache Management: Prefix Caching + KV Cache quantization + Sliding Window Attention are the three key techniques for memory optimization
  5. Canary Deployment: Gateway API weight-based routing + statistical evaluation enables smooth model version transitions

Looking ahead, as the K8s DRA ecosystem matures and GPU hardware iterates (B200, GB200), LLM inference platforms will evolve toward higher density and lower cost. Technologies such as FP8 inference, KV Cache offload to CPU/SSD, and multi-cluster federated scheduling will further reduce inference costs.

Authoritative References

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#K8s 1.30#大模型推理#LLM部署#GPU调度#弹性伸缩#2026