Scaling Strategies & Load Management

Scaling AI systems is fundamentally different from scaling traditional web applications. Let's explore why:

Resource Requirements Comparison

Traditional web applications typically require:

• Memory: 256MB - 2GB per instance
• CPU: 0.5 - 2 cores
• Startup time: 1-5 seconds
• State: Usually stateless
• Resource sharing: Efficient multi-tenancy

AI applications require:

• Memory: 8GB - 80GB+ per instance
• GPU: Often dedicated GPU resources
• Startup time: 30-120 seconds for model loading
• State: Large model weights in memory
• Resource sharing: Limited due to GPU exclusivity

GPU Allocation Challenges

GPUs present unique scaling challenges:

1. Exclusive allocation: Unlike CPUs, GPUs typically can't be efficiently shared between processes
2. Memory constraints: Model size must fit within GPU memory limits
3. Cost considerations: GPU instances are 10-100x more expensive than CPU instances
4. Limited availability: Cloud providers often have GPU capacity constraints

Model Loading Overhead

The model loading process creates significant scaling challenges:

This overhead means:

• Cold starts are expensive: Can't quickly spin up new instances
• Memory persistence is critical: Can't afford to reload models frequently
• Scaling decisions must be predictive: React too late and users experience timeouts

Memory Management Complexity

AI models have complex memory requirements:

1. Model weights: The static model parameters (can be shared)
2. Activation memory: Dynamic memory for processing (per request)
3. KV cache: For transformer models, grows with sequence length
4. Batch dimension: Memory scales with batch size

Horizontal scaling (adding more instances) works well when:

1. Request volume is high but individual requests are independent
2. Model size is moderate (fits comfortably in available GPU memory)
3. Latency requirements allow for load balancer overhead
4. Budget permits multiple GPU instances

Horizontal Scaling Architecture

                    Load Balancer
                         |
        +----------------+----------------+
        |                |                |
   GPU Instance 1   GPU Instance 2   GPU Instance 3
   [Model Copy]     [Model Copy]     [Model Copy]

Benefits:

• Fault tolerance: Instance failure doesn't bring down service
• Predictable performance: Each instance handles limited load
• Geographic distribution: Can place instances in different regions

Challenges:

• Model duplication: Each instance needs full model copy
• Synchronization: Ensuring consistent model versions
• Cost: Multiple expensive GPU instances

When to Scale Vertically

Vertical scaling (using more powerful hardware) is optimal when:

1. Model is very large (approaching GPU memory limits)
2. Batch processing is efficient (better GPU utilization)
3. Request patterns are bursty (need headroom)
4. Cost optimization is critical (fewer instances to manage)

GPU Tier Comparison

GPU Tier	Memory	Performance	Cost/Hour	Best For
T4	16GB	Baseline	$0.35	Small models, development
V100	32GB	2.5x T4	$2.48	Medium models, production
A100 40GB	40GB	5x T4	$3.67	Large models, high throughput
A100 80GB	80GB	5x T4	$5.12	Very large models
H100	80GB	9x T4	$8.00	Cutting-edge models

Hybrid Scaling Strategies

The most effective approach often combines both:

1. Vertical scaling first: Upgrade to handle base load efficiently
2. Horizontal scaling for peaks: Add instances during high demand
3. Model sharding: Split very large models across GPUs
4. Pipeline parallelism: Different model stages on different GPUs

AI load balancers must consider more factors than traditional HTTP load balancers:

1. Model capabilities: Route to instances with appropriate models
2. GPU memory state: Avoid overloading GPU memory
3. Batch compatibility: Group similar requests for efficiency
4. Priority handling: Ensure critical requests get resources

Load Balancing Strategies

Least Loaded

Route to the instance with lowest current utilization:

• ✅ Good for uniform requests
• ❌ Doesn't consider request complexity

Latency Aware

Route based on recent response times:

• ✅ Adapts to actual performance
• ❌ Can create feedback loops

Capacity Based

Consider both current load and capacity:

• ✅ Prevents overload
• ❌ Requires accurate capacity estimates

Affinity Routing

Route similar requests to same instance:

• ✅ Better cache utilization
• ❌ Can create hot spots

Request Batching

Batching is crucial for GPU efficiency:

Traditional caching relies on exact key matches. AI systems can benefit from semantic caching:

1. Similar queries often have identical results
2. Computation cost is high (worth aggressive caching)
3. Response size is often small relative to compute cost
4. Semantic matching can dramatically improve hit rates

Multi-Level Caching Architecture

Request → L1 Cache → L2 Cache → L3 Cache → Model
          (Local)    (Redis)    (Semantic)
           <1ms       <5ms        <10ms      100ms+

Implementing Semantic Caching

1. Generate embeddings for each request
2. Store embeddings with responses
3. Search similar requests using vector similarity
4. Return cached result if similarity > threshold

Benefits:

• 5-10x higher hit rate than exact matching
• Handles paraphrasing and similar intents
• Reduces model load significantly

Cache Invalidation Strategies

AI caches need special invalidation approaches:

1. Model version based: Invalidate when model updates
2. Confidence threshold: Don't cache low-confidence results
3. Time-based with decay: Reduce confidence over time
4. Semantic drift detection: Invalidate when input distribution changes

Queues provide critical benefits for AI systems:

1. Burst handling: Accept requests even when at capacity
2. Priority management: Process critical requests first
3. Retry logic: Handle transient failures gracefully
4. Batch formation: Collect requests for efficient processing
5. Backpressure: Prevent system overload

Queue Design Patterns

Priority Queues

Different queues for different SLAs:

• Critical: Real-time inference (<100ms)
• High: Interactive applications (<1s)
• Normal: Standard requests (<10s)
• Batch: Bulk processing (minutes)

Request Coalescing

Detect and merge duplicate requests:

• Identify requests in queue
• Check for semantic similarity
• Return same result to multiple callers
• Reduces redundant processing

Timeout Handling

Respect request deadlines:

• Track request age in queue
• Skip expired requests
• Notify callers of timeout
• Prevent processing stale requests

Queue Sizing and Monitoring

Key metrics to track:

• Queue depth: Current number of waiting requests
• Queue time: How long requests wait
• Processing rate: Requests processed per second
• Timeout rate: Percentage of requests timing out

Traditional metrics (CPU, memory) are insufficient. Monitor:

1. GPU Utilization: Primary indicator of capacity
2. GPU Memory Usage: Prevents OOM errors
3. Inference Latency: P50, P95, P99 percentiles
4. Queue Depth: Leading indicator of demand
5. Model Load Time: Affects scaling responsiveness
6. Cost per Inference: Budget optimization

Scaling Decision Logic

Predictive Scaling

AI workloads often have patterns:

• Time-based: Business hours, batch jobs
• Event-driven: Product launches, campaigns
• Correlated: With user activity metrics

Use these patterns for proactive scaling:

1. Historical analysis: Learn from past patterns
2. Trend detection: Identify growing demand
3. Scheduled scaling: Pre-scale for known events
4. Capacity reservation: Ensure GPU availability

Cost components:

1. GPU instance hours: Largest cost component
2. Memory and storage: For model artifacts
3. Network transfer: Especially for multi-region
4. API calls: For managed services

Optimization Strategies

Right-Sizing Instances

• Profile actual GPU memory usage
• Don't overprovision "just in case"
• Use smaller instances for development

Spot/Preemptible Instances

• 70-90% cost savings
• Good for batch workloads
• Requires graceful shutdown handling

Reserved Capacity

• 30-50% savings for predictable load
• Commit to baseline capacity
• Use on-demand for peaks

Model Optimization

• Quantization: Reduce precision (2-4x savings)
• Distillation: Smaller models (5-10x savings)
• Pruning: Remove unnecessary parameters

Cost Monitoring and Alerting

Track:

• Cost per request: Overall efficiency metric
• Instance utilization: Identify waste
• Reserved vs on-demand: Optimization opportunities
• Regional costs: Place load strategically

1. Data Residency: Legal requirements for data location
2. Model Distribution: Syncing large model files
3. Latency: Users expect fast responses globally
4. Cost: Regional pricing variations
5. Capacity: GPU availability differs by region

Architecture Patterns

Active-Active

• Models deployed in multiple regions
• Load balanced based on geography
• Requires model sync mechanism
• Higher cost but better performance

Active-Passive

• Primary region serves all traffic
• Standby regions for failover
• Lower cost but higher latency
• Simpler model management

Edge Deployment

• Lightweight models at edge locations
• Full models in central regions
• Hybrid approach for global reach
• Balances cost and performance

Model Synchronization

Challenges:

• Model files are large (GB to TB)
• Updates must be atomic
• Version consistency critical

Solutions:

1. CDN distribution: For model files
2. Incremental updates: Only sync changes
3. Blue-green deployment: Per region
4. Version pinning: Explicit version control