Performance Optimization: Speed at Scale

Performance Optimization: Speed at Scale

As your codebase grows and your team scales, maintaining Cursor’s performance becomes critical. This guide covers advanced optimization techniques that keep Cursor responsive even under heavy load.

Understanding Performance Bottlenecks

Indexing Overhead

Initial and incremental indexing can consume significant CPU/memory

Context Window Size

Large context windows slow down AI responses exponentially

Extension Conflicts

Extensions can interfere with Cursor’s AI features

Network Latency

API calls to AI models can be bottlenecked by network speed

System-Level Optimizations

Hardware Recommendations

Component	Minimum	Recommended	Optimal
RAM	8GB	16GB	32GB+
CPU	4 cores	8 cores	12+ cores
Storage	SSD 256GB	NVMe 512GB	NVMe 1TB+
Network	10 Mbps	50 Mbps	100+ Mbps

Operating System Tuning

macOS

# Increase file descriptor limits
sudo launchctl limit maxfiles 65536 200000

# Disable Spotlight indexing for code directories
sudo mdutil -i off /path/to/code

# Increase shared memory
sudo sysctl -w kern.sysv.shmmax=2147483648
sudo sysctl -w kern.sysv.shmall=524288

# Add to /etc/sysctl.conf for persistence
echo "kern.sysv.shmmax=2147483648" | sudo tee -a /etc/sysctl.conf
echo "kern.sysv.shmall=524288" | sudo tee -a /etc/sysctl.conf

Linux

# Increase file descriptor limits
echo "* soft nofile 65536" | sudo tee -a /etc/security/limits.conf
echo "* hard nofile 65536" | sudo tee -a /etc/security/limits.conf

# Increase inotify watchers
echo "fs.inotify.max_user_watches=524288" | sudo tee -a /etc/sysctl.conf
sudo sysctl -p

# Optimize swappiness for development
echo "vm.swappiness=10" | sudo tee -a /etc/sysctl.conf

# Enable transparent huge pages
echo "always" | sudo tee /sys/kernel/mm/transparent_hugepage/enabled

Windows

# Run as Administrator

# Increase virtual memory
wmic computersystem set AutomaticManagedPagefile=False
wmic pagefileset set InitialSize=16384,MaximumSize=32768

# Disable Windows Search for code directories
Set-Service WSearch -StartupType Disabled

# Optimize for performance
powercfg -setactive 8c5e7fda-e8bf-4a96-9a85-a6e23a8c635c

# Disable unnecessary services
Set-Service "SysMain" -StartupType Disabled
Set-Service "Windows Search" -StartupType Disabled

Cursor Configuration Optimization

Memory and Cache Settings

// Cursor performance configuration
{
  "cursor.performance.memoryLimit": "8GB",
  "cursor.performance.maxWorkers": 6,
  "cursor.performance.cacheSize": "2GB",
  "cursor.performance.enableLazyLoading": true,
  "cursor.performance.garbageCollection": "aggressive",
  "cursor.performance.indexingThreads": 4,
  "cursor.performance.searchCacheEnabled": true,
  "cursor.performance.searchCacheSize": "1GB",
  "cursor.performance.incrementalIndexing": true,
  "cursor.performance.indexingBatchSize": 100
}

Indexing Optimization

Create an optimized .cursorignore file:

# .cursorignore - Maximize indexing performance

# Dependencies and packages
node_modules/
vendor/
packages/*/node_modules/
**/bower_components/
.pnpm-store/
.yarn/

# Build outputs
dist/
build/
out/
target/
*.min.js
*.min.css
*.map

# Large generated files
coverage/
*.generated.*
*.pb.go
*.pb.js
schema.graphql
package-lock.json
yarn.lock
pnpm-lock.yaml

# Media and binaries
*.jpg
*.jpeg
*.png
*.gif
*.mp4
*.pdf
*.zip
*.tar.gz

# Logs and databases
*.log
*.sqlite
*.db

# IDE and system files
.idea/
.vscode/
.DS_Store
Thumbs.db

# Test fixtures and data
fixtures/
__fixtures__/
testdata/
*.snapshot
__snapshots__/

Wskazówka

Pro Tip: Run find . -size +10M -type f to identify large files that should be added to .cursorignore.

Context Management Strategies

Smart Context Selection

// Context optimization patterns

// 1. Layered Context Approach
class ContextOptimizer {
  // Start with minimal context
  async getMinimalContext(task: string) {
    return {
      currentFile: this.getCurrentFile(),
      directImports: await this.getDirectImports(),
      recentChanges: this.getRecentChanges(5)
    };
  }

  // Expand as needed
  async expandContext(feedback: string) {
    const additionalContext = await this.analyzeNeeds(feedback);
    return this.addContext(additionalContext);
  }

  // Never exceed limits
  async pruneContext(context: Context) {
    const tokenCount = await this.countTokens(context);
    if (tokenCount > this.maxTokens) {
      return this.intelligentPrune(context);
    }
    return context;
  }
}

Context Window Optimization

// Monitor and optimize context usage
class ContextMonitor {
  private contextHistory: ContextUsage[] = [];

  async analyzeUsage() {
    const stats = {
      averageTokens: this.calculateAverage(),
      peakUsage: this.findPeak(),
      wastedTokens: this.identifyWaste(),
      optimalSize: this.calculateOptimal()
    };

    return {
      stats,
      recommendations: this.generateRecommendations(stats)
    };
  }

  private identifyWaste() {
    // Find included files that were never referenced
    return this.contextHistory
      .flatMap(usage => usage.includedFiles)
      .filter(file => !this.wasReferenced(file));
  }
}

Model Selection for Performance

Task-Based Model Selection

// Intelligent model selection
class ModelSelector {
  selectModel(task: TaskType): Model {
    switch (task.complexity) {
      case 'simple':
        // Fast, lightweight model
        return {
          model: 'claude-4-sonnet',
          temperature: 0.3,
          maxTokens: 2000
        };

      case 'medium':
        // Balanced model
        return {
          model: 'claude-4-sonnet',
          temperature: 0.5,
          maxTokens: 4000
        };

      case 'complex':
        // Powerful but slower
        return {
          model: 'claude-4-opus',
          temperature: 0.7,
          maxTokens: 8000
        };

      case 'analysis':
        // Long context model
        return {
          model: 'gemini-2.5-pro',
          temperature: 0.4,
          maxTokens: 100000
        };
    }
  }
}

Performance vs Quality Trade-offs

Task Type	Model Choice	Response Time	Quality	Token Cost
Quick fixes	Sonnet 4	under 2s	Good	Low
Feature development	Sonnet 4	2-5s	Very Good	Medium
Complex refactoring	Opus 4	5-10s	Excellent	High
Codebase analysis	Gemini 2.5	3-8s	Very Good	Medium
Deep debugging	o3	10-20s	Excellent	Very High

Extension Management

Identifying Performance-Impacting Extensions

# Debug extension performance issues
cursor --inspect-brk-extensions 9229

# Run in safe mode (no extensions)
cursor --disable-extensions

# To find problematic extensions:
# 1. Disable all extensions via UI
# 2. Enable them one by one to isolate issues

Extension Optimization Checklist

• Disable unused extensions
• Check for extension conflicts with AI features
• Update all extensions to latest versions
• Remove duplicate functionality extensions
• Configure extensions to lazy-load

Recommended Extension Configuration

{
  // Disable extensions that conflict with Cursor AI
  "extensions.disabled": [
    "github.copilot",
    "tabnine.tabnine-vscode",
    "visualstudioexptteam.vscodeintellicode"
  ],

  // Lazy load heavy extensions
  "extensions.experimental.affinity": {
    "vscodevim.vim": 1,
    "dbaeumer.vscode-eslint": 2,
    "esbenp.prettier-vscode": 2
  }
}

Network Optimization

API Response Caching

// Implement intelligent caching
class ResponseCache {
  private cache = new Map<string, CachedResponse>();
  private readonly TTL = 5 * 60 * 1000; // 5 minutes

  async getCachedOrFetch(
    prompt: string,
    fetcher: () => Promise<Response>
  ): Promise<Response> {
    const key = this.hashPrompt(prompt);
    const cached = this.cache.get(key);

    if (cached && !this.isExpired(cached)) {
      return cached.response;
    }

    const response = await fetcher();
    this.cache.set(key, {
      response,
      timestamp: Date.now()
    });

    return response;
  }
}

Connection Pooling

{
  "cursor.network.connectionPool": {
    "maxSockets": 10,
    "maxFreeSockets": 5,
    "timeout": 60000,
    "keepAlive": true,
    "keepAliveMsecs": 30000
  },

  "cursor.network.http2": {
    "enabled": true,
    "maxConcurrentStreams": 100
  }
}

Monitoring and Profiling

Performance Metrics Dashboard

// Real-time performance monitoring
class PerformanceMonitor {
  private metrics = {
    indexingTime: new MetricCollector('indexing'),
    searchLatency: new MetricCollector('search'),
    aiResponseTime: new MetricCollector('ai_response'),
    memoryUsage: new MetricCollector('memory'),
    cpuUsage: new MetricCollector('cpu')
  };

  startMonitoring() {
    // Collect metrics every 30 seconds
    setInterval(() => {
      this.collectMetrics();
      this.analyzeThresholds();
      this.generateAlerts();
    }, 30000);
  }

  private analyzeThresholds() {
    const alerts = [];

    if (this.metrics.memoryUsage.current > 0.9) {
      alerts.push('High memory usage detected');
    }

    if (this.metrics.aiResponseTime.p95 > 10000) {
      alerts.push('Slow AI responses detected');
    }

    return alerts;
  }
}

Performance Logging

// Enable performance logging
{
  "cursor.telemetry.performanceLogging": true,
  "cursor.telemetry.logLevel": "verbose",
  "cursor.telemetry.logPath": "~/.cursor/performance.log",
  "cursor.telemetry.metrics": [
    "indexing",
    "search",
    "completion",
    "memory",
    "network"
  ]
}

Real-World Performance Scenarios

Scenario 1: Monorepo with 5M+ LOC

A team optimized their massive monorepo:

1. Partitioned the Codebase

   • Split into 12 logical workspaces
   • Only 2-3 active at a time
   • Result: 75% reduction in memory usage

2. Optimized Indexing

   • Aggressive .cursorignore rules
   • Incremental indexing every 5 minutes
   • Result: Initial index from 45min to 8min

3. Context Strategy

   • Layered context approach
   • Smart file inclusion
   • Result: 60% faster AI responses

4. Model Selection

   • Sonnet for 90% of tasks
   • Opus only for architecture
   • Result: 40% cost reduction

Scenario 2: High-Frequency Trading System

Performance-critical environment optimizations:

// Ultra-low latency configuration
{
  "cursor.performance": {
    "mode": "performance",
    "disableAnimations": true,
    "disableTelemetry": true,
    "minimalUI": true,
    "aggressiveCaching": true,
    "preloadModels": ["claude-4-sonnet"],
    "dedicatedWorkers": 8
  }
}

// Results:
// - Tab completion: under 50ms
// - Inline edits: under 100ms
// - Agent responses: under 2s average

Troubleshooting Performance Issues

Diagnostic Checklist

High CPU Usage

1. Check indexing status
2. Review extension activity
3. Monitor background processes
4. Verify no infinite loops in rules

Slow Responses

1. Test network speed
2. Check context size
3. Verify model availability
4. Review API rate limits

Memory Leaks

1. Restart Cursor periodically
2. Clear cache directories
3. Update to latest version
4. Report persistent issues

Indexing Hangs

1. Check .cursorignore
2. Remove problematic files
3. Reset index database
4. Increase timeout settings

Performance Reset Procedure

#!/bin/bash
# Full performance reset

# 1. Close Cursor
killall Cursor

# 2. Clear caches
rm -rf ~/.cursor/Cache
rm -rf ~/.cursor/CachedData
rm -rf ~/.cursor/GPUCache

# 3. Reset indexes
rm -rf ~/.cursor/IndexedDB

# 4. Clean workspace storage
rm -rf ~/.cursor/workspaceStorage

# 5. Restart with fresh profile
cursor --user-data-dir ~/.cursor-fresh

Best Practices Summary

1. Profile Before Optimizing

   • Measure actual bottlenecks
   • Don’t optimize prematurely
   • Focus on biggest impact areas

2. Incremental Improvements

   • Make one change at a time
   • Measure impact of each change
   • Document what works

3. Team-Wide Standards

   • Share performance configurations
   • Establish context size limits
   • Monitor aggregate metrics

4. Regular Maintenance

   • Weekly cache clearing
   • Monthly extension review
   • Quarterly configuration audit

Uwaga

Warning: Over-optimization can reduce functionality. Always balance performance with productivity features you actually need.

Next Steps

1. Run Performance Audit - Use the diagnostic checklist
2. Implement Quick Wins - Start with .cursorignore optimization
3. Monitor Metrics - Set up performance tracking
4. Continue to Privacy & Security - Ensure optimizations don’t compromise security

Remember: The fastest Cursor is one configured for your specific workflow. There’s no one-size-fits-all solution—experiment and measure to find your optimal setup.