GPU Sharing Performance Reality: 50-100% Latency Impact Analysis Across 6 Strategies on Amazon EKS

eks-gpu-sharing-performance-analysis/
├── infra/                          # Infrastructure as Code
│   ├── cluster-config.yaml         # eksctl cluster configuration
│   ├── gpu-nodegroup.yaml          # GPU node group specifications
│   ├── nvidia-device-plugin-config.yaml # Time-slicing configuration
│   └── gpu-test-pod.yaml          # GPU validation pod
├── models/                         # Model deployment manifests
│   ├── mistral-memory-optimized.yaml    # Phi-3.5-Mini configuration
│   ├── deepseek-memory-optimized.yaml   # DeepSeek-R1 configuration  
│   ├── mistral-exclusive.yaml     # Exclusive GPU baseline
│   ├── deepseek-exclusive.yaml    # Exclusive GPU baseline
│   └── README.md                  # Model configuration guide
├── tests/                          # Performance testing framework
│   ├── load_test.sh               # Comprehensive performance testing
│   ├── load_test_exclusive.sh     # Exclusive GPU testing
│   ├── run_tests.sh               # Test orchestration guide
│   ├── final_test_report.md       # Results summary
│   └── test_results/              # Historical test data
│       ├── GPU_SLICING_FULL_performance_report_*.txt
│       └── performance_report_*.txt
└── README.md                       # Complete deployment guide

# infra/cluster-config.yaml
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: gpusharing-demo
  region: us-west-2
  version: "1.32"

nodeGroups:
  - name: main
    instanceType: t3.large
    desiredCapacity: 2
    minSize: 2
    maxSize: 4
    volumeSize: 20
    ssh:
      allow: false
    iam:
      withAddonPolicies:
        imageBuilder: true
        autoScaler: true
        externalDNS: true
        certManager: true
        appMesh: true
        appMeshPreview: true
        ebs: true
        fsx: true
        cloudWatch: true

# Deploy base cluster
eksctl create cluster -f infra/cluster-config.yaml

# Verify cluster creation
kubectl get nodes
# Expected: 2 t3.large nodes in Ready state

# infra/gpu-nodegroup.yaml
apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: gpusharing-demo
  region: us-west-2

nodeGroups:
  - name: gpu
    instanceType: g6e.2xlarge
    desiredCapacity: 1
    minSize: 1
    maxSize: 1
    volumeSize: 100
    ssh:
      allow: false
    labels:
      eks-node: gpu
    iam:
      withAddonPolicies:
        imageBuilder: true
        autoScaler: true
        externalDNS: true
        certManager: true
        appMesh: true
        appMeshPreview: true
        ebs: true
        fsx: true
        cloudWatch: true

# Add GPU node group
eksctl create nodegroup -f infra/gpu-nodegroup.yaml

# Verify GPU node
kubectl get nodes --show-labels | grep gpu
# Expected: g6e.2xlarge node with eks-node=gpu label

# Install NVIDIA GPU Operator
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

helm install gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --create-namespace \
  --set nodeSelector.eks-node=gpu \
  --wait

# Verify GPU detection
kubectl describe node $(kubectl get nodes -l eks-node=gpu -o jsonpath='{.items[0].metadata.name}') | grep nvidia.com/gpu
# Expected: nvidia.com/gpu: 1

# infra/nvidia-device-plugin-config.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: nvidia-device-plugin-config
  namespace: gpu-operator
data:
  config.yaml: |
    version: v1
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 10

# Apply time-slicing configuration
kubectl apply -f infra/nvidia-device-plugin-config.yaml

# Update GPU Operator to use time-slicing
helm upgrade gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --set nodeSelector.eks-node=gpu \
  --set devicePlugin.config.name=nvidia-device-plugin-config \
  --wait

# Verify time-slicing is active
kubectl describe node $(kubectl get nodes -l eks-node=gpu -o jsonpath='{.items[0].metadata.name}') | grep "nvidia.com/gpu:"
# Expected: nvidia.com/gpu: 10 (instead of 1)

# models/mistral-memory-optimized.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: mistral-7b-baseline
  namespace: llm-testing
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mistral-7b-baseline
  template:
    metadata:
      labels:
        app: mistral-7b-baseline
    spec:
      containers:
      - name: phi
        image: ghcr.io/huggingface/text-generation-inference:3.3.4
        args:
        - "--model-id"
        - "microsoft/Phi-3.5-mini-instruct"
        - "--port"
        - "80"
        - "--max-input-length"
        - "256"
        - "--max-total-tokens" 
        - "512"
        - "--max-batch-prefill-tokens"
        - "4096"  # Critical: Reduced from 8192 to prevent OOM
        - "--cuda-memory-fraction"
        - "0.4"   # Critical: 40% allocation for coexistence
        - "--max-concurrent-requests"
        - "16"
        ports:
        - containerPort: 80
        env:
        - name: PYTORCH_CUDA_ALLOC_CONF
          value: "expandable_segments:True"
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 8Gi
          requests:
            memory: 4Gi
            nvidia.com/gpu: 1
        volumeMounts:
        - name: cache-volume
          mountPath: /data
        - name: shm-volume
          mountPath: /dev/shm
      nodeSelector:
        eks-node: gpu
      volumes:
      - name: cache-volume
        emptyDir: {}
      - name: shm-volume
        emptyDir:
          medium: Memory
          sizeLimit: 512Mi

# Essential settings discovered through testing
apiVersion: apps/v1
kind: Deployment
metadata:
  name: memory-optimized-model
spec:
  template:
    spec:
      containers:
      - name: model-inference
        args:
        - "--cuda-memory-fraction"
        - "0.4"  # 40% allocation per model for coexistence
        - "--max-batch-prefill-tokens"
        - "4096"  # Reduced from 8192 to prevent OOM
        - "--max-input-length"
        - "256"  # Conservative for memory stability
        - "--max-total-tokens"
        - "512"  # Balanced for concurrent operation
        env:
        - name: PYTORCH_CUDA_ALLOC_CONF
          value: "expandable_segments:True"
        resources:
          limits:
            nvidia.com/gpu: 1
            memory: 8Gi
          requests:
            memory: 4Gi

# Dynamic GPU Sharing ROI Calculator
import requests
import json

class DynamicGPUSharingROI:
    def __init__(self):
        # Get current AWS pricing (pseudo-code - actual implementation would use AWS APIs)
        self.pricing_source = "https://aws.amazon.com/ec2/pricing/on-demand/"
        
    def calculate_monthly_costs(self, g6e_hourly_rate, t3_large_hourly_rate):
        """
        Calculate costs using current AWS pricing
        
        Args:
            g6e_hourly_rate: Current g6e.2xlarge hourly rate from AWS
            t3_large_hourly_rate: Current t3.large hourly rate from AWS
        """
        hours_per_month = 730
        
        # Dedicated setup: 2 GPU instances
        dedicated_monthly = 2 * g6e_hourly_rate * hours_per_month
        
        # Time-sliced setup: 1 GPU + 2 CPU instances
        timesliced_monthly = (g6e_hourly_rate + 2 * t3_large_hourly_rate) * hours_per_month
        
        savings = {
            'dedicated_monthly': dedicated_monthly,
            'timesliced_monthly': timesliced_monthly,
            'monthly_savings': dedicated_monthly - timesliced_monthly,
            'annual_savings': (dedicated_monthly - timesliced_monthly) * 12,
            'savings_percentage': ((dedicated_monthly - timesliced_monthly) / dedicated_monthly) * 100
        }
        
        return savings
    
    def generate_roi_report(self, current_pricing):
        """Generate ROI report with current AWS pricing"""
        costs = self.calculate_monthly_costs(
            current_pricing['g6e_2xlarge'], 
            current_pricing['t3_large']
        )
        
        return f"""
        GPU Sharing ROI Analysis (Current Pricing)
        ==========================================
        Dedicated GPU Setup: ${costs['dedicated_monthly']:,.2f}/month
        Time-Sliced Setup:   ${costs['timesliced_monthly']:,.2f}/month
        Monthly Savings:     ${costs['monthly_savings']:,.2f}
        Annual Savings:      ${costs['annual_savings']:,.2f}
        Savings Percentage:  {costs['savings_percentage']:.1f}%
        
        Performance Impact: 50-100% latency increase
        Recommended Use: Development, testing, batch processing
        
        Pricing Source: {self.pricing_source}
        """

# Usage Example:
# roi_calculator = DynamicGPUSharingROI()
# current_aws_rates = {
#     'g6e_2xlarge': 2.24,  # Check current AWS pricing
#     't3_large': 0.08      # Check current AWS pricing  
# }
# print(roi_calculator.generate_roi_report(current_aws_rates))

# Dynamic GPU Sharing ROI Calculator
class GPUSharingROI:
    def __init__(self, current_gpu_hourly_cost, performance_impact):
        """
        Initialize with current AWS pricing from:
        https://aws.amazon.com/ec2/pricing/on-demand/
        
        Args:
            current_gpu_hourly_cost: Current g6e.2xlarge hourly rate
            performance_impact: Measured latency increase (e.g., 1.0 for 100%)
        """
        self.gpu_hourly_cost = current_gpu_hourly_cost
        self.performance_impact = performance_impact
        self.hours_per_month = 730
    
    def calculate_infrastructure_savings(self):
        # Dedicated: 2 GPU instances
        dedicated_monthly = 2 * self.gpu_hourly_cost * self.hours_per_month
        
        # Time-sliced: 1 GPU instance (CPU costs minimal in comparison)
        timesliced_monthly = self.gpu_hourly_cost * self.hours_per_month
        
        monthly_savings = dedicated_monthly - timesliced_monthly
        annual_savings = monthly_savings * 12
        savings_percentage = (monthly_savings / dedicated_monthly) * 100
        
        return {
            'monthly_savings': monthly_savings,
            'annual_savings': annual_savings,
            'savings_percentage': savings_percentage
        }
    
    def assess_business_impact(self, user_abandonment_per_100ms=0.01):
        """Calculate customer impact from latency increase"""
        latency_increase_ms = self.performance_impact * 100
        user_impact = latency_increase_ms * user_abandonment_per_100ms
        return user_impact
    
    def net_business_value(self, current_revenue_monthly):
        savings = self.calculate_infrastructure_savings()
        user_impact = self.assess_business_impact()
        revenue_loss = current_revenue_monthly * user_impact
        
        return {
            'infrastructure_savings': savings['monthly_savings'],
            'potential_revenue_impact': revenue_loss,
            'net_monthly_value': savings['monthly_savings'] - revenue_loss,
            'pricing_source': 'https://aws.amazon.com/ec2/pricing/on-demand/'
        }

# Usage with current AWS pricing:
# Check https://aws.amazon.com/ec2/pricing/on-demand/ for g6e.2xlarge rates
# roi_calculator = GPUSharingROI(
#     current_gpu_hourly_cost=2.24,  # Update with current rate
#     performance_impact=1.0         # 100% latency increase from testing
# )

#!/bin/bash
# GPU Sharing Performance Validation Script
# Based on the testing methodology from Section V

echo "=== GPU Sharing Performance Validation ==="

# Phase 1: Baseline Individual Performance
echo "Phase 1: Measuring individual model performance..."
kubectl scale deployment model-b --replicas=0 -n gpu-testing
sleep 30
./performance_test.sh --model model-a --iterations 10 --output baseline_a.json

kubectl scale deployment model-a --replicas=0 -n gpu-testing
kubectl scale deployment model-b --replicas=1 -n gpu-testing
sleep 30
./performance_test.sh --model model-b --iterations 10 --output baseline_b.json

# Phase 2: Concurrent Performance Testing
echo "Phase 2: Measuring concurrent performance impact..."
kubectl scale deployment model-a --replicas=1 -n gpu-testing
sleep 30
./performance_test.sh --model both --iterations 10 --output concurrent.json

# Phase 3: Analysis and Reporting
echo "Phase 3: Analyzing results and generating report..."
python3 analyze_results.py baseline_a.json baseline_b.json concurrent.json

echo "Validation complete. Check performance_report.html for detailed results."