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LLMOps Framework
Performance Optimization

Performance Optimization

Comprehensive strategies and tools for optimizing LLM performance, reducing latency, improving throughput, and maximizing cost efficiency.

Overview

Performance Optimization in LLMOps focuses on maximizing the efficiency and effectiveness of LLM operations while minimizing costs and resource usage. This includes optimizing model performance, reducing latency, improving throughput, and implementing intelligent caching strategies.

Key Performance Metrics

Core Metrics to Track

  • Latency - Response time (p50, p95, p99)
  • Throughput - Requests per second
  • Token Efficiency - Tokens per query
  • Cost per Query - Total cost including compute and API calls
  • Error Rate - Failed requests percentage
  • Cache Hit Rate - Percentage of cached responses

Latency Optimization

Response Time Analysis

// Analyze and optimize response times
const performanceAnalyzer = await ants.llmops.performanceAnalyzer
 
const latencyAnalysis = await performanceAnalyzer.analyzeLatency({
  modelId: 'customer-support-v2',
  timeRange: 'last_7_days',
  breakdown: [
    'model-inference',
    'prompt-processing',
    'response-formatting',
    'network-overhead'
  ]
})
 
console.log('Latency Analysis:')
console.log(`P50: ${latencyAnalysis.p50}ms`)
console.log(`P95: ${latencyAnalysis.p95}ms`)
console.log(`P99: ${latencyAnalysis.p99}ms`)
console.log('Breakdown:', latencyAnalysis.breakdown)

Latency Optimization Strategies

# Implement latency optimization strategies
latency_optimizer = ants.llmops.latency_optimizer
 
# Optimize prompt length
prompt_optimization = latency_optimizer.optimize_prompts({
    'model_id': 'customer-support-v2',
    'strategies': [
        'prompt_compression',
        'instruction_clarification',
        'example_reduction'
    ],
    'target_latency': 1500,  # ms
    'max_accuracy_loss': 0.02
})
 
print("Prompt Optimization Results:")
print(f"Original latency: {prompt_optimization.original_latency}ms")
print(f"Optimized latency: {prompt_optimization.optimized_latency}ms")
print(f"Improvement: {prompt_optimization.improvement:.1%}")
print(f"Accuracy impact: {prompt_optimization.accuracy_impact:.3f}")
 
# Optimize model parameters
model_optimization = latency_optimizer.optimize_model({
    'model_id': 'customer-support-v2',
    'parameters': {
        'temperature': 0.7,  # Reduce for faster responses
        'max_tokens': 150,    # Limit response length
        'top_p': 0.9         # Optimize sampling
    },
    'performance_targets': {
        'latency_p95': 2000,
        'accuracy_min': 0.90
    }
})

Throughput Optimization

Concurrent Request Management

// Optimize concurrent request handling
const throughputOptimizer = await ants.llmops.throughputOptimizer
 
const optimizationConfig = await throughputOptimizer.configure({
  modelId: 'customer-support-v2',
  concurrency: {
    maxConcurrentRequests: 100,
    requestQueueSize: 1000,
    timeoutMs: 30000
  },
  loadBalancing: {
    strategy: 'round-robin',
    healthCheckInterval: 5000,
    failoverEnabled: true
  },
  autoScaling: {
    enabled: true,
    minInstances: 2,
    maxInstances: 20,
    scaleUpThreshold: 0.8,
    scaleDownThreshold: 0.3
  }
})
 
console.log('Throughput optimization configured')
console.log(`Max concurrent requests: ${optimizationConfig.concurrency.maxConcurrentRequests}`)
console.log(`Auto-scaling enabled: ${optimizationConfig.autoScaling.enabled}`)

Request Batching

# Implement request batching for efficiency
batch_processor = ants.llmops.batch_processor
 
# Configure batch processing
batch_config = batch_processor.configure({
    'model_id': 'customer-support-v2',
    'batch_size': 10,
    'batch_timeout': 100,  # ms
    'max_wait_time': 1000,  # ms
    'batch_strategy': 'similarity'  # Group similar requests
})
 
# Process batched requests
batch_results = batch_processor.process_batch([
    {'query': 'How do I reset my password?', 'user_id': 'user1'},
    {'query': 'Password reset help', 'user_id': 'user2'},
    {'query': 'I forgot my password', 'user_id': 'user3'}
])
 
print(f"Processed {len(batch_results)} requests in batch")
print(f"Average latency: {batch_results.avg_latency}ms")
print(f"Throughput improvement: {batch_results.throughput_gain:.1%}")

Token Efficiency Optimization

Token Usage Analysis

// Analyze token usage patterns
const tokenAnalyzer = await ants.llmops.tokenAnalyzer
 
const tokenAnalysis = await tokenAnalyzer.analyze({
  modelId: 'customer-support-v2',
  timeRange: 'last_30_days',
  breakdown: [
    'input-tokens',
    'output-tokens',
    'prompt-tokens',
    'completion-tokens'
  ],
  optimization: {
    identifyRedundancy: true,
    suggestCompression: true,
    costImpact: true
  }
})
 
console.log('Token Usage Analysis:')
console.log(`Total tokens: ${tokenAnalysis.totalTokens}`)
console.log(`Average per query: ${tokenAnalysis.avgTokensPerQuery}`)
console.log(`Cost per query: $${tokenAnalysis.costPerQuery}`)
console.log('Optimization suggestions:', tokenAnalysis.suggestions)

Token Optimization Strategies

# Implement token optimization strategies
token_optimizer = ants.llmops.token_optimizer
 
# Optimize prompts for token efficiency
prompt_optimization = token_optimizer.optimize_prompts({
    'model_id': 'customer-support-v2',
    'strategies': [
        'remove_redundant_words',
        'use_abbreviations',
        'compress_examples',
        'optimize_instructions'
    ],
    'target_reduction': 0.3,  # 30% token reduction
    'maintain_accuracy': True
})
 
print("Token Optimization Results:")
print(f"Original tokens: {prompt_optimization.original_tokens}")
print(f"Optimized tokens: {prompt_optimization.optimized_tokens}")
print(f"Token reduction: {prompt_optimization.reduction:.1%}")
print(f"Cost savings: ${prompt_optimization.cost_savings:.4f} per query")
 
# Optimize response length
response_optimization = token_optimizer.optimize_responses({
    'model_id': 'customer-support-v2',
    'max_tokens': 100,
    'response_format': 'concise',
    'include_examples': False
})

Intelligent Caching

Response Caching

// Implement intelligent response caching
const cacheManager = await ants.llmops.cacheManager
 
const cacheConfig = await cacheManager.configure({
  strategy: 'semantic-similarity',
  cacheLayers: [
    {
      name: 'exact-match',
      ttl: 3600, // 1 hour
      maxSize: 10000
    },
    {
      name: 'semantic-match',
      ttl: 1800, // 30 minutes
      maxSize: 5000,
      similarityThreshold: 0.95
    }
  ],
  invalidation: {
    strategy: 'time-based',
    conditions: ['model-update', 'prompt-change', 'data-drift']
  }
})
 
// Cache a response
const cacheKey = await cacheManager.cache({
  query: 'How do I reset my password?',
  response: 'To reset your password, go to...',
  metadata: {
    modelId: 'customer-support-v2',
    promptId: 'password-reset-v1',
    timestamp: Date.now()
  }
})
 
console.log(`Response cached with key: ${cacheKey}`)

Cache Performance Monitoring

# Monitor cache performance
cache_monitor = ants.llmops.cache_monitor
 
# Get cache statistics
cache_stats = cache_monitor.get_stats({
    'model_id': 'customer-support-v2',
    'time_range': 'last_24_hours'
})
 
print("Cache Performance:")
print(f"Hit rate: {cache_stats.hit_rate:.2%}")
print(f"Miss rate: {cache_stats.miss_rate:.2%}")
print(f"Total requests: {cache_stats.total_requests}")
print(f"Cache size: {cache_stats.cache_size} entries")
print(f"Memory usage: {cache_stats.memory_usage}MB")
 
# Optimize cache configuration
cache_optimization = cache_monitor.optimize({
    'target_hit_rate': 0.8,
    'max_memory_usage': 1000,  # MB
    'optimization_strategies': [
        'adjust_ttl',
        'increase_cache_size',
        'improve_similarity_threshold'
    ]
})
 
print("Cache Optimization Recommendations:")
for rec in cache_optimization.recommendations:
    print(f"- {rec.strategy}: {rec.description}")
    print(f"  Expected improvement: {rec.improvement:.1%}")

Model Selection Optimization

Dynamic Model Selection

// Implement dynamic model selection based on performance
const modelSelector = await ants.llmops.modelSelector
 
const selectionConfig = await modelSelector.configure({
  strategy: 'performance-based',
  criteria: [
    {
      name: 'latency',
      weight: 0.3,
      threshold: 2000
    },
    {
      name: 'accuracy',
      weight: 0.4,
      threshold: 0.90
    },
    {
      name: 'cost',
      weight: 0.3,
      threshold: 0.01
    }
  ],
  models: [
    {
      id: 'gpt-4',
      capabilities: ['high-accuracy', 'high-latency', 'high-cost']
    },
    {
      id: 'gpt-3.5-turbo',
      capabilities: ['medium-accuracy', 'low-latency', 'low-cost']
    },
    {
      id: 'claude-3-haiku',
      capabilities: ['medium-accuracy', 'very-low-latency', 'very-low-cost']
    }
  ]
})
 
// Select optimal model for query
const selectedModel = await modelSelector.selectModel({
  query: 'Simple customer support question',
  context: {
    urgency: 'low',
    complexity: 'simple',
    userTier: 'basic'
  }
})
 
console.log(`Selected model: ${selectedModel.id}`)
console.log(`Selection reason: ${selectedModel.reason}`)

Load Balancing Across Models

# Implement load balancing across multiple models
load_balancer = ants.llmops.load_balancer
 
# Configure load balancing
lb_config = load_balancer.configure({
    'strategy': 'weighted-round-robin',
    'models': [
        {'id': 'gpt-4', 'weight': 0.3, 'max_concurrent': 50},
        {'id': 'gpt-3.5-turbo', 'weight': 0.5, 'max_concurrent': 100},
        {'id': 'claude-3-haiku', 'weight': 0.2, 'max_concurrent': 200}
    ],
    'health_checks': {
        'interval': 30,  # seconds
        'timeout': 5,    # seconds
        'failure_threshold': 3
    },
    'failover': {
        'enabled': True,
        'strategy': 'automatic'
    }
})
 
# Route request through load balancer
routing_result = load_balancer.route({
    'query': 'Customer support request',
    'preferences': {
        'max_latency': 2000,
        'min_accuracy': 0.85
    }
})
 
print(f"Routed to: {routing_result.model_id}")
print(f"Estimated latency: {routing_result.estimated_latency}ms")
print(f"Estimated cost: ${routing_result.estimated_cost:.4f}")

Performance Monitoring & Alerting

Real-time Performance Monitoring

// Set up comprehensive performance monitoring
const performanceMonitor = await ants.llmops.performanceMonitor
 
const monitoringConfig = await performanceMonitor.configure({
  modelId: 'customer-support-v2',
  metrics: [
    'latency',
    'throughput',
    'error-rate',
    'token-usage',
    'cost-per-query',
    'cache-hit-rate'
  ],
  thresholds: {
    latency: { p95: 2000, p99: 3000 },
    throughput: { min: 50 },
    errorRate: { max: 0.05 },
    costPerQuery: { max: 0.01 }
  },
  alerts: [
    {
      metric: 'latency',
      condition: 'p95 > 2000',
      severity: 'warning',
      channels: ['email', 'slack']
    },
    {
      metric: 'error-rate',
      condition: 'rate > 0.05',
      severity: 'critical',
      channels: ['email', 'slack', 'pagerduty']
    }
  ]
})
 
console.log('Performance monitoring configured')
console.log(`Monitoring ${monitoringConfig.metrics.length} metrics`)
console.log(`Alert channels: ${monitoringConfig.alerts.length}`)

Performance Analytics & Insights

# Get performance insights and recommendations
performance_insights = ants.llmops.performance_insights
 
# Analyze performance trends
trend_analysis = performance_insights.analyze_trends({
    'model_id': 'customer-support-v2',
    'time_range': 'last_30_days',
    'metrics': ['latency', 'throughput', 'cost', 'accuracy']
})
 
print("Performance Trend Analysis:")
for metric, trend in trend_analysis.trends.items():
    print(f"{metric}: {trend.direction} ({trend.change:.1%})")
    if trend.significant:
        print(f"  Significant change detected")
 
# Get optimization recommendations
recommendations = performance_insights.get_recommendations({
    'model_id': 'customer-support-v2',
    'focus_areas': ['latency', 'cost', 'throughput']
})
 
print("\nOptimization Recommendations:")
for rec in recommendations:
    print(f"- {rec.category}: {rec.description}")
    print(f"  Impact: {rec.impact}")
    print(f"  Effort: {rec.effort}")
    print(f"  Priority: {rec.priority}")

Best Practices

1. Performance Monitoring

  • Monitor key metrics continuously
  • Set appropriate thresholds for alerts
  • Track trends over time
  • Correlate performance with business metrics

2. Latency Optimization

  • Optimize prompts for efficiency
  • Use appropriate model parameters
  • Implement caching for common queries
  • Consider model selection based on requirements

3. Throughput Optimization

  • Implement request batching where possible
  • Use load balancing across multiple models
  • Configure auto-scaling for traffic spikes
  • Optimize concurrent request handling

4. Token Efficiency

  • Analyze token usage patterns regularly
  • Optimize prompts for token efficiency
  • Use appropriate response lengths
  • Implement token-aware caching

5. Caching Strategy

  • Implement multi-layer caching
  • Use semantic similarity for cache hits
  • Monitor cache performance regularly
  • Invalidate cache appropriately

6. Model Selection

  • Choose models based on use case requirements
  • Implement dynamic model selection
  • Use load balancing across models
  • Monitor model performance continuously

Integration with Other Components

FinOps Integration

  • Cost optimization through performance improvements
  • Budget monitoring for performance-related costs
  • ROI analysis of optimization investments

SRE Integration

  • SLA monitoring and alerting
  • Incident response for performance issues
  • Capacity planning based on performance trends

Security Posture Integration

  • Performance impact of security measures
  • Security monitoring without performance degradation
  • Compliance reporting on performance metrics

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