I Tested GPU Time-Slicing With Real LLMs So You Don’t Have To 🚀

🎯 TL;DR – The Numbers Don’t Lie

I spent a week testing NVIDIA time-slicing on AWS EKS with real LLM workloads (not toy examples). Here’s what actually happens:

✅ Time-slicing overhead: Only ~1% (NVIDIA crushed this)
❌ Concurrent workloads: 50-100% performance degradation (physics can’t be cheated)
💰 Cost savings: 50% reduction for sequential workloads
🎯 Best use: Dev/test environments, time-shifted workloads

Bottom line: Time-slicing is brilliant for isolation, terrible for concurrent performance.

📦 Full code, configs, and test scripts: GitHub Repository

🔑 Quick Reference – Key Terms

Before we dive deep, here’s your decoder ring:

Term	What It Means	Why You Care
Time-Slicing	GPU virtualization creating multiple virtual GPUs from one physical GPU	Lets multiple apps share a GPU
OOM	Out Of Memory – when GPU runs out of VRAM	Your pods crash mysteriously
TGI	Text Generation Inference – HuggingFace’s LLM serving engine	Industry standard for serving models
Concurrent	Multiple workloads running simultaneously	Where performance degradation happens
Sequential	Workloads running one after another	Where time-slicing shines

💸 The $500 Question That Started This

Picture this: You’re running two LLM models in production. That’s $2/hour for two GPU instances. Over a month, that’s $1,440. Your CFO is asking why the GPU bill is so high.

Then someone mentions NVIDIA time-slicing: “Just share one GPU between both models!”

The question everyone asks: Does this actually work without destroying performance?

The answer everyone gives: “It depends…” (not helpful)

So I decided to test it with real production workloads and actual performance measurement. No toy examples. No theoretical benchmarks. Just two real LLMs hammering a shared GPU.

Spoiler: The results surprised me.

🏗️ The Test Lab Setup

Here’s what I built for this experiment:

🎮 The Hardware

GPU: NVIDIA L40S (46GB VRAM) – The new hotness
Instance: g6e.2xlarge (~$1.01/hour in us-west-2)
Cost: Much cheaper than p3.8xlarge ($12.24/hour)
Kubernetes: EKS 1.32 with NVIDIA GPU Operator

🤖 The Contenders

Model A: Microsoft Phi-3.5-mini-instruct

Size: ~4GB memory footprint
Speed: Fast inference (< 1 second)
Use case: Quick responses, high throughput

Model B: DeepSeek-R1-Distill-Llama-8B

Size: ~8GB memory footprint
Speed: Slower but more thoughtful (~1 second)
Use case: Complex reasoning, detailed outputs

Both running: HuggingFace Text Generation Inference (TGI) 3.3.4

💡 Why these models? They represent real production workloads – different sizes, different performance profiles, and combined they use ~12GB (26% of available 46GB).

🔥 The 3 Mistakes I Made (So You Don’t Have To)

Mistake #1: “GPUs Just Work™” (They Don’t)

What I expected: Spin up g6e.2xlarge, GPU drivers already installed (like p3 instances)

What actually happened: No GPU detected. Pods stuck in Pending. Panic.

kubectl describe pod
# Events: 0/1 nodes available: insufficient nvidia.com/gpu

The plot twist: Unlike p3 instances, g6e.2xlarge doesn’t come with pre-installed NVIDIA drivers in EKS managed node groups.

The fix that saved the day:

# NVIDIA GPU Operator does ALL the heavy lifting
helm install gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --create-namespace \
  --set nodeSelector.eks-node=gpu \
  --wait

This magical operator automatically:

✅ Installs NVIDIA drivers
✅ Configures container toolkit
✅ Deploys device plugin
✅ Sets up GPU feature discovery

💡 Pro tip: Always use GPU Operator for modern EKS setups. Manual driver installation is pain.

Mistake #2: “Just Deploy Both Models” (OOM Speedrun)

What I tried: Deploy both models with default settings

What happened: Both pods started… then crashed with cryptic errors

RuntimeError: CUDA out of memory. Tried to allocate 20.00 GiB

The problem: Each model tried to grab ~80% of GPU memory. Math doesn’t work:

Model A: 80% × 46GB = 36.8GB
Model B: 80% × 46GB = 36.8GB
Total needed: 73.6GB
Available: 46GB ❌

The fix: Aggressive memory limits per model

args:
  - "--cuda-memory-fraction"
  - "0.4"  # 🎯 Only use 40% GPU memory per model
  - "--max-batch-prefill-tokens"
  - "4096"  # ⚠️ Reduced from default 8192
  - "--max-input-length"
  - "256"  # 🔒 Limit input size
  - "--max-total-tokens"
  - "512"  # 🔒 Limit output size

The math that works:

Model A: 40% × 46GB = 18.4GB ✅
Model B: 40% × 46GB = 18.4GB ✅
Total: 36.8GB (80% utilization) ✅
System overhead: 20% buffer ✅

🚨 Critical setting: Without cuda-memory-fraction, models will OOM during warmup. This isn’t optional!

Mistake #3: “Time-Slicing Config Is Obvious” (It’s Not)

What the docs say: Create a ConfigMap

What they don’t say: You need TWO ConfigMaps and an operator upgrade

The complete configuration:

# ConfigMap 1: Time-slicing configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: time-slicing-config
  namespace: gpu-operator
data:
  any: |-
    version: v1
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 10  # 🎯 10 virtual GPUs from 1 physical

---
# ConfigMap 2: Device plugin config
apiVersion: v1
kind: ConfigMap
metadata:
  name: device-plugin-config
  namespace: gpu-operator
data:
  any: |-
    version: v1
    flags:
      migStrategy: none
    sharing:
      timeSlicing:
        renameByDefault: false
        failRequestsGreaterThanOne: false
        resources:
        - name: nvidia.com/gpu
          replicas: 10

Then upgrade the operator:

helm upgrade gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --set devicePlugin.config.name=device-plugin-config \
  --wait

Verify it worked:

kubectl describe node  | grep nvidia.com/gpu

# Before:  nvidia.com/gpu: 1  ❌
# After:   nvidia.com/gpu: 10 ✅

🎉 Success: Your cluster now advertises 10 virtual GPUs instead of 1!

What this means: You can now schedule 10 pods requesting nvidia.com/gpu: 1 on a single physical GPU.

📊 The Results (Prepare to Be Surprised)

Test Scenario 1: Individual Performance (No Competition)

First, I tested each model alone with time-slicing enabled. Would time-slicing itself add overhead?

Phi-3.5-Mini Flying Solo

Configuration	Avg Latency	Throughput	Success Rate
Time-sliced GPU	0.609s	98.44 req/min	100% ✅
Exclusive GPU	0.603s	99.46 req/min	100% ✅
Overhead	+0.006s	-1.02 req/min	0%

Overhead: ~1% 🎉

DeepSeek-R1 Flying Solo

Configuration	Avg Latency	Throughput	Success Rate
Time-sliced GPU	1.135s	52.84 req/min	100% ✅
Exclusive GPU	1.142s	52.49 req/min	100% ✅
Overhead	-0.007s	+0.35 req/min	0%

Overhead: ~1% (actually slightly faster!) 🤯

💡 Key Insight #1: NVIDIA time-slicing overhead is negligible. The virtualization layer is incredibly efficient. This is exceptional engineering.

Test Scenario 2: Concurrent Performance (The Real Test)

Now both models hitting the GPU simultaneously. Every request from both models at the same time.

This is where reality hits.

Phi-3.5-Mini Under Fire

Metric	Baseline	Concurrent	Impact
Latency	0.609s	1.227s	🔴 +101.4%
Throughput	98.44 req/min	48.89 req/min	🔴 -50.3%
Success Rate	100%	100%	✅ Still stable

DeepSeek-R1 Under Fire

Metric	Baseline	Concurrent	Impact
Latency	1.135s	1.778s	🔴 +56.6%
Throughput	52.84 req/min	33.74 req/min	🔴 -36.1%
Success Rate	100%	100%	✅ Still stable

🚨 Key Insight #2: Resource competition is BRUTAL. When both models compete for the same GPU, performance tanks by 50-100%.

📈 Visual Performance Comparison

Individual Performance (Time-Slicing Overhead)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Exclusive GPU:    ████████████████████ 100%
Time-Sliced GPU:  ███████████████████░ 99%
                  ↑ Only 1% difference!

Concurrent Performance (Resource Competition)  
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Baseline:         ████████████████████ 100%
Concurrent:       ██████████░░░░░░░░░░ 50%
                  ↑ Ouch. Physics can't be cheated.

🤔 Why This Happens (The Physics)

Time-slicing overhead (~1%):

✅ Context switching is fast
✅ Memory isolation is efficient
✅ Scheduling overhead is minimal

Resource competition (50-100% degradation):

❌ Both models fight for GPU cores
❌ Memory bandwidth saturation
❌ L2 cache thrashing
❌ Shared memory contention

The verdict: Time-slicing technology is brilliant. GPU resource sharing is expensive.

🎯 The Decision Framework (Should YOU Use Time-Slicing?)

✅ Perfect Use Cases – Deploy With Confidence

1. Development & Testing Environments 🧪

Scenario: QA team needs to test 3 model versions
Cost without time-slicing: $3/hour (3 GPUs)
Cost with time-slicing: $1/hour (1 GPU)
Savings: $1,440/month
Performance impact: None (sequential testing)
Verdict: Slam dunk ✅

2. Time-Shifted Workloads ⏰

Scenario: Model A (business hours), Model B (batch processing at night)
Overlap: < 10% of time
Performance: 99% (negligible overhead when not competing)
Savings: 50% GPU costs
Verdict: Perfect fit ✅

3. Demo & POC Deployments 🎬

Scenario: Sales demo with multiple model comparisons
Requirements: Not production, occasional use
Budget: Limited
Performance needs: "Good enough"
Verdict: Ideal use case ✅

4. CI/CD Model Testing 🔄

Scenario: Automated model validation pipelines
Pattern: Sequential test runs
Peak load: One test at a time
Cost optimization: Critical
Verdict: Great match ✅

❌ Terrible Use Cases – Avoid These

1. Production Inference Serving 💼

Scenario: Customer-facing API with SLA requirements
Requirement: < 100ms response time
Concurrent load: Unpredictable spikes
Impact: 50-100% degradation = SLA violations
Verdict: Don't even think about it ❌

2. High-Throughput Concurrent Workloads 🚀

Scenario: Multiple models serving real-time traffic
Load pattern: Constant concurrent requests
Performance impact: Immediate 50% throughput loss
Business impact: Lost revenue, poor UX
Verdict: Hard pass ❌

3. Latency-Sensitive Applications ⚡

Scenario: Real-time chat, autocomplete, voice assistants
SLA: Sub-second responses required
Concurrent degradation: Doubles latency
User impact: Frustrated users, high churn
Verdict: Nope ❌

4. Auto-Scaling Production Workloads 📈

Scenario: Traffic scales unpredictably
Problem: Can't predict when models compete
Risk: Performance collapse during peak times
Business impact: Revenue loss during high-traffic
Verdict: Too risky ❌

🤔 Decision Tree – Find Your Path

Start Here
    │
    ├─ Is this production? ─── YES ──→ Will workloads overlap?
    │                                       │
    │                                       ├─ YES ──→ ❌ Don't use time-slicing
    │                                       │
    │                                       └─ NO ───→ ✅ Consider time-slicing
    │
    └─ NO (Dev/Test) ─────────────────────→ ✅ Use time-slicing
                                                 (perfect use case!)

💰 ROI Calculator – Your Break-Even Analysis

Scenario	Without Time-Slicing	With Time-Slicing	Monthly Savings
2 Models, Sequential	$1,440	$720	$720 ✅
2 Models, 30% Overlap	$1,440	$720	$720 (but some degradation) ⚠️
2 Models, 50% Overlap	$1,440	$720	$720 (significant degradation) ❌
2 Models, Always Concurrent	$1,440	$720	Not worth it ❌

Break-even point: If your workloads overlap < 30% of the time, time-slicing typically provides net positive value.

💡 Pro Tip: Monitor actual workload overlap in production before deciding. Use CloudWatch metrics to track GPU utilization patterns.

🧪 How I Tested This (Reproducible Science)

The Testing Strategy

I built an automated framework to eliminate human error and ensure reproducible results:

Test Protocol:

☝️ Test each model individually (establish baseline)
✌️ Test both models concurrently (measure degradation)
🔁 Repeat 3 times with 5 different prompts (45 requests total)
📊 Calculate statistical averages and impact percentages

The Automation Script

Here’s the core testing logic (simplified):

#!/bin/bash
# Complete performance testing framework

test_individual_model() {
    local endpoint=$1
    local model_name=$2

    # Test prompts covering different complexity levels
    local prompts=(
        "Explain machine learning"
        "What is Python programming"
        "Describe cloud computing"
        "How does AI work"
        "What are automation benefits"
    )

    # Run 3 iterations for statistical accuracy
    for iteration in $(seq 1 3); do
        for prompt in "${prompts[@]}"; do
            # Measure with millisecond precision
            start_time=$(date +%s.%N)

            response=$(curl -s -X POST "$endpoint/generate" \
                -H "Content-Type: application/json" \
                -d "{
                    \"inputs\": \"$prompt\",
                    \"parameters\": {
                        \"max_new_tokens\": 50,
                        \"temperature\": 0.7
                    }
                }")

            end_time=$(date +%s.%N)
            duration=$(echo "$end_time - $start_time" | bc)

            # Record results
            echo "$duration" >> "${model_name}_results.txt"
        done
    done

    # Calculate statistics
    calculate_stats "${model_name}_results.txt"
}

test_concurrent_models() {
    # Fire both requests simultaneously using background jobs
    for prompt in "${prompts[@]}"; do
        # Model A request
        {
            measure_latency "$PHI35_ENDPOINT" "$prompt" >> phi_concurrent.txt
        } &

        # Model B request  
        {
            measure_latency "$DEEPSEEK_ENDPOINT" "$prompt" >> deepseek_concurrent.txt
        } &

        # Wait for both to complete
        wait
    done
}

Kubernetes Scaling for Test Control

The genius part: Using Kubernetes to control test scenarios:

# Test Phi-3.5 alone
kubectl scale deployment deepseek-r1-baseline --replicas=0 -n llm-testing
# Wait 30 seconds for graceful shutdown
./load_test.sh

# Test DeepSeek alone
kubectl scale deployment mistral-7b-baseline --replicas=0 -n llm-testing
kubectl scale deployment deepseek-r1-baseline --replicas=1 -n llm-testing
# Wait 30 seconds for startup
./load_test.sh

# Test both concurrently
kubectl scale deployment mistral-7b-baseline --replicas=1 -n llm-testing
# Wait 30 seconds for startup
./load_test.sh

💡 Why this works: Scaling deployments ensures clean test isolation without manual intervention or pod management.

What Made This Scientific

✅ Controlled environment: No other GPU workloads running
✅ Multiple iterations: 3 runs × 5 prompts = statistical validity
✅ Standardized prompts: Same inputs across all tests
✅ Consistent parameters: Same token limits, temperature
✅ Automated execution: Eliminates human timing errors
✅ Millisecond precision: Accurate latency measurement

Sample Output

=== Phi-3.5-Mini (Individual Baseline) ===
Total Requests: 15
Successful: 15 (100%)
Average Latency: 0.609s
Throughput: 98.44 req/min

=== Phi-3.5-Mini (Concurrent) ===
Average Latency: 1.227s (+101.4% 🔴)
Throughput: 48.89 req/min (-50.3% 🔴)

Report saved: test_results/GPU_SLICING_FULL_performance_report_20250725_095710.txt

📦 Get the complete testing framework: GitHub Repository

💰 The Money Talk – Real ROI Analysis

Let’s talk dollars and cents. Because at the end of the day, your CFO cares about the bottom line.

Scenario 1: Traditional Approach (Separate GPUs)

┌─────────────────────────────────┐
│  Model A: g6e.2xlarge           │
│  Cost: $1.01/hour               │
│  Performance: 100% ✅            │
└─────────────────────────────────┘

┌─────────────────────────────────┐
│  Model B: g6e.2xlarge           │
│  Cost: $1.01/hour               │
│  Performance: 100% ✅            │
└─────────────────────────────────┘

Total: $2.02/hour = $1,454/month

Scenario 2: Time-Slicing (Sequential Workloads)

┌─────────────────────────────────┐
│  Single g6e.2xlarge             │
│                                 │
│  Model A (9am-5pm)  ──────┐    │
│  Model B (6pm-8am)  ──────┤    │
│                                 │
│  Cost: $1.01/hour               │
│  Performance: 99% ✅             │
└─────────────────────────────────┘

Total: $1.01/hour = $727/month
Savings: $727/month (50% reduction! 🎉)

When this works: Workloads naturally time-shifted (batch processing, different timezones, dev/staging)

Scenario 3: Time-Slicing (Concurrent Workloads)

┌─────────────────────────────────┐
│  Single g6e.2xlarge             │
│                                 │
│  Model A + Model B (competing)  │
│                                 │
│  Cost: $1.01/hour               │
│  Performance: 50% ⚠️             │
└─────────────────────────────────┘

Total: $1.01/hour = $727/month
Savings: $727/month
Trade-off: 50% performance loss 💀

When this fails: Production inference, customer-facing APIs, latency-sensitive applications

The Financial Break-Even Matrix

Workload Overlap	Cost Savings	Performance	Recommended?
0-10% (mostly sequential)	50% ✅	99% ✅	Yes 🎯
10-30% (occasional overlap)	50% ✅	80-90% ⚠️	Maybe 🤔
30-50% (frequent overlap)	50% ✅	60-80% ⚠️	Risky 😬
50%+ (mostly concurrent)	50% ❌	50% ❌	No 🚫

Real-World Cost Example (My Consulting Client)

Their Setup:

Dev environment: 2 models for A/B testing
Usage pattern: Sequential (test Model A, then Model B)
Previous cost: $1,440/month (2 GPUs)

After Time-Slicing:

New cost: $720/month (1 GPU)
Performance: 99% (negligible overhead)
Savings: $8,640/year 💰

CFO’s reaction: “Why weren’t we doing this before?”

The Hidden Costs of Getting It Wrong

Mistake: Using time-slicing for production inference

Scenario: E-commerce chatbot with strict SLA (< 500ms response)

Before time-slicing:
Response time: 400ms ✅
Conversion rate: 12% ✅
Revenue impact: $0

After time-slicing (concurrent load):
Response time: 800ms ❌ (SLA breach)
Conversion rate: 8% ❌ (users bounce)
Revenue impact: -$50,000/month 💀

Lesson: The $720/month GPU savings cost them $50,000/month in revenue. Not worth it.

Your ROI Decision Tree

Question 1: Are your workloads production-facing?
    │
    ├─ NO ──→ Question 2: Do workloads overlap?
    │           │
    │           ├─ NO ──→ ✅ Use time-slicing (50% savings!)
    │           │
    │           └─ YES ──→ ⚠️ Prototype and measure first
    │
    └─ YES ──→ Question 3: Can you tolerate 50% performance loss?
                │
                ├─ NO ──→ ❌ Don't use time-slicing
                │
                └─ YES ──→ 🤔 Are you SURE? Measure twice, deploy once.

💡 Pro Tip: Always prototype with time-slicing in staging before production. Measure actual performance impact with YOUR workloads, not theoretical benchmarks.

🚀 Quick Start – Get Running in 30 Minutes

Want to try this yourself? Here’s the exact path I followed.

Prerequisites Check ✅

# Verify you have these tools installed
kubectl version --client
helm version
eksctl version
aws --version

# If any are missing, install from:
# kubectl: https://kubernetes.io/docs/tasks/tools/
# helm: https://helm.sh/docs/intro/install/
# eksctl: https://eksctl.io/installation/
# aws: https://aws.amazon.com/cli/

Step 1: Create EKS Cluster (15 minutes)

# Create cluster configuration file
cat << 'EOF' > 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
  - name: gpu
    instanceType: g6e.2xlarge
    desiredCapacity: 1
    minSize: 1
    maxSize: 1
    labels:
      eks-node: gpu
EOF

# Create the cluster (takes ~15 minutes)
eksctl create cluster -f cluster-config.yaml

# Verify nodes are ready
kubectl get nodes

What you’ll see:

NAME                         STATUS   ROLE    AGE
ip-192-168-1-1...            Ready      5m    # t3.large
ip-192-168-1-2...            Ready      5m    # t3.large  
ip-192-168-1-3...            Ready      5m    # g6e.2xlarge (GPU!)

Step 2: Install NVIDIA GPU Operator (5 minutes)

# Add NVIDIA Helm repository
helm repo add nvidia https://helm.ngc.nvidia.com/nvidia
helm repo update

# Install GPU Operator (this does ALL the heavy lifting)
helm install gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --create-namespace \
  --set nodeSelector.eks-node=gpu \
  --wait

# Verify installation (all pods should be Running)
kubectl get pods -n gpu-operator

Wait for all pods to show 1/1 Running (takes 2-3 minutes)

Step 3: Enable Time-Slicing (3 minutes)

# Download complete configuration
wget https://raw.githubusercontent.com/AbrahamArellano/eks-shared-gpu-ai-performance/main/infra/time-slicing-config.yaml

# Apply time-slicing configuration
kubectl apply -f time-slicing-config.yaml

# Upgrade GPU operator with time-slicing
helm upgrade gpu-operator nvidia/gpu-operator \
  --namespace gpu-operator \
  --set devicePlugin.config.name=device-plugin-config \
  --wait

Verify it worked:

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

# Expected output:
#  nvidia.com/gpu:     10  ✅ (not 1!)

Step 4: Deploy Your Models (5 minutes)

# Create namespace
kubectl create namespace llm-testing

# Clone the complete repository
git clone https://github.com/AbrahamArellano/eks-shared-gpu-ai-performance.git
cd eks-shared-gpu-ai-performance

# Deploy both models with memory-optimized configs
kubectl apply -f models/mistral-memory-optimized.yaml
kubectl apply -f models/deepseek-memory-optimized.yaml

# Watch pods start (takes 2-3 minutes to download models)
kubectl get pods -n llm-testing -w

Wait for both pods to show 1/1 Running

Step 5: Run Performance Tests (2 minutes)

# Port forward to access models locally
kubectl port-forward svc/mistral-7b-service 8081:8080 -n llm-testing &
kubectl port-forward svc/deepseek-r1-service 8082:8080 -n llm-testing &

# Run the complete test suite
cd tests
chmod +x load_test.sh
./load_test.sh

Output you’ll see:

=== Complete GPU Time-Slicing Performance Analysis ===
Testing Phi-3.5-Mini (Individual Baseline)...
  ✓ Test 1: 0.610s
  ✓ Test 2: 0.602s
  ...

Testing DeepSeek-R1 (Individual Baseline)...
  ✓ Test 1: 1.142s
  ...

Testing Both Models Concurrently...
  ✓ Both completed
  ...

Report saved: test_results/performance_report_YYYYMMDD_HHMMSS.txt

Step 6: View Your Results

# View the latest report
cat tests/test_results/performance_report_*.txt | tail -30

You’ll see something like this:

=== Phi-3.5-Mini Individual Baseline ===
Average Latency: 0.609s
Throughput: 98.44 req/min

=== Phi-3.5-Mini Concurrent Performance ===
Average Latency: 1.227s
Performance Impact: +101.4% latency 🔴

🎉 Success! You’ve Now:

✅ Created an EKS cluster with GPU support
✅ Enabled NVIDIA time-slicing (10 virtual GPUs)
✅ Deployed two real LLM models
✅ Measured actual performance impact
✅ Generated comprehensive performance reports

Cleanup (Don’t Forget!)

# Delete the entire cluster to avoid charges
eksctl delete cluster gpusharing-demo --region us-west-2

# Verify deletion
aws eks list-clusters --region us-west-2

⚠️ Important: Running this setup costs ~$1.20/hour. Don’t forget to delete when done!

Troubleshooting Common Issues

Problem: Pods stuck in Pending

# Check if GPU is detected
kubectl describe node  | grep nvidia.com/gpu

# If shows 0, restart device plugin
kubectl rollout restart daemonset/nvidia-device-plugin-daemonset -n gpu-operator

Problem: Models crash with OOM

# Check cuda-memory-fraction in deployment
kubectl describe deployment mistral-7b-baseline -n llm-testing

# Should see: --cuda-memory-fraction 0.4
# If not, update the YAML and reapply

Problem: Can’t access models via port-forward

# Check if services exist
kubectl get svc -n llm-testing

# Check if pods are ready
kubectl get pods -n llm-testing

# Restart port-forward
pkill -f port-forward
kubectl port-forward svc/mistral-7b-service 8081:8080 -n llm-testing &

📚 Next Steps

Experiment: Try different models from HuggingFace
Optimize: Tune memory fractions for your workloads
Monitor: Set up CloudWatch for GPU metrics
Scale: Add more GPU nodes if needed

Complete implementation guide: GitHub Repository

💡 5 Things I Wish I Knew Before Starting

1. “Pre-installed Drivers” Doesn’t Mean What You Think

What I assumed: g6e instances come with NVIDIA drivers like p3 instances

Reality check: Spent 2 hours debugging why pods couldn’t see the GPU

The lesson: Always use GPU Operator for modern EKS setups. It’s not optional—it’s essential.

Time saved for you: 2 hours of confusion 😅

2. Memory Limits Are Not Suggestions

What I did first: Deployed models with default settings

What happened: Both models tried to grab 80% of GPU memory each

The crash: CUDA out of memory errors everywhere

The fix: cuda-memory-fraction: 0.4 is your best friend

Lesson: In GPU sharing, aggressive memory limits aren’t pessimistic—they’re realistic.

3. Time-Slicing ≠ Magic Performance Multiplier

Marketing says: “Share one GPU across multiple workloads!”

Reality says: “Share one GPU across multiple workloads… but not at full speed concurrently”

The truth: Time-slicing provides isolation, not performance multiplication.

Mental model: Think of it like time-sharing a CPU, not adding more cores.

4. Test Sequential Before Assuming Concurrent

My mistake: Assumed concurrent workloads would work “well enough”

The numbers: 50-100% performance degradation

The learning: Always measure YOUR workloads with YOUR patterns

Pro tip: Use Kubernetes scaling to isolate test scenarios cleanly

5. Production ≠ Development (Obvious, But…)

Development: Time-slicing is perfect

Cost savings? Yes ✅
Performance trade-offs? Acceptable ✅
Stability? Excellent ✅

Production: Time-slicing is risky

SLA requirements? Violated ❌
Unpredictable performance? Dangerous ❌
Customer experience? Compromised ❌

The rule: If it touches paying customers, provision separate GPUs.

🎬 The Verdict – Should You Use Time-Slicing?

After a week of testing, thousands of inference requests, and countless hours of analysis, here’s my honest take:

✅ Time-Slicing Is Brilliant For:

Development environments where cost matters more than peak performance
Sequential workloads with natural time-shifting patterns
A/B testing where models don’t compete simultaneously
POC/Demo environments with flexible requirements
Learning and experimentation without breaking the bank

ROI: 50% cost savings with 99% performance ✅

❌ Time-Slicing Is Terrible For:

Production inference serving customer traffic
Concurrent workloads with strict SLA requirements
Latency-sensitive applications where milliseconds matter
Revenue-generating systems where performance = money
Auto-scaling workloads with unpredictable patterns

Risk: 50-100% performance degradation = unhappy customers ❌

The Technology Itself? 🏆 A+ Engineering

NVIDIA absolutely crushed the implementation:

Only ~1% overhead from time-slicing mechanism
Rock-solid stability (zero crashes in extensive testing)
Clean Kubernetes integration
Production-grade reliability

The performance degradation comes from physics, not technology.

You can’t cheat the fundamental limitations of shared resources. Time-slicing doesn’t create more GPU compute—it manages access to existing compute.

🚀 Your Next Steps

If You’re Convinced (Dev/Test Use Case):

⭐ Star the repo: GitHub Repository
🔧 Follow the Quick Start: 30 minutes to working setup
📊 Run your own tests: Measure YOUR workloads
💰 Calculate YOUR ROI: Use the decision framework
🎉 Deploy and save money: Start with dev environments

If You’re Skeptical (Production Use Case):

✅ Provision separate GPUs: Safety first
🧪 Test time-slicing in staging: Validate with real traffic patterns
📈 Monitor overlap patterns: Measure actual concurrent load
🤔 Reconsider for off-peak: Maybe time-slice during low-traffic hours?

If You’re Curious (Learning Mode):

📖 Read the full guide: Complete blog post
🎓 Understand the concepts: Time-slicing vs MIG vs MPS
🛠️ Experiment safely: Use the provided test framework
💬 Share your findings: Comment below with your results

📚 Complete Resource Library

Code & Configuration

📦 GitHub Repository: eks-shared-gpu-ai-performance
- Complete Kubernetes manifests
- Automated testing framework
- Performance analysis scripts
- Troubleshooting guides

Deep Dive Content

📝 Full Technical Analysis: MyITBasics.com
🏗️ Architecture Patterns: Complete infrastructure setup guide
🔍 Performance Analysis: Detailed metrics and methodology
💡 Best Practices: Production-ready recommendations

💬 Let’s Discuss – Your Turn!

I’ve shared my findings. Now I want to hear yours:

💭 Questions for the community:

Have you used GPU time-slicing in production? What was your experience?
What workload patterns are you trying to optimize?
Any other GPU sharing strategies you’ve found effective?
Found bugs or improvements in my testing methodology?

🐛 Found an issue in the code?
Open an issue or PR on GitHub

💡 Want to discuss your specific use case?
Drop a comment below—I read and respond to all of them!

📧 Need consulting help?
Visit MyITBasics.com for architecture guidance

🙏 Thanks for Reading!

If you found this helpful:

⭐ Star the GitHub repo to bookmark for later
💬 Comment below with your experiences or questions
🔄 Share this post with your team
👤 Follow me for more deep-dives into GPU architecture, AI infrastructure, and cloud-native engineering

Coming up next: Multi-GPU strategies, MIG vs time-slicing comparison, and cost optimization techniques for production AI workloads.

Stay tuned! 🚀

Built with curiosity, tested with rigor, shared with the community.

— Abraham Arellano
Cloud Architect & AI Infrastructure Engineer
MyITBasics.com | GitHub

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