223 lines
5.2 KiB
Markdown
223 lines
5.2 KiB
Markdown
# Performance Comparison: CPU vs GPU
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## Overview
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This document compares the performance of Ollama running on CPU vs GPU for the Munich News Daily system.
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## Test Configuration
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**Hardware:**
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- CPU: Intel Core i7-10700K (8 cores, 16 threads)
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- GPU: NVIDIA RTX 3060 (12GB VRAM)
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- RAM: 32GB DDR4
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**Model:** phi3:latest (2.3GB)
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**Test:** Processing 10 news articles with translation and summarization
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## Results
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### Processing Time
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```
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CPU Processing:
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├─ Model Load: 20s
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├─ 10 Translations: 15s (1.5s each)
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├─ 10 Summaries: 80s (8s each)
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└─ Total: 115s
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GPU Processing:
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├─ Model Load: 8s
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├─ 10 Translations: 3s (0.3s each)
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├─ 10 Summaries: 20s (2s each)
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└─ Total: 31s
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Speedup: 3.7x faster with GPU
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```
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### Detailed Breakdown
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| Operation | CPU Time | GPU Time | Speedup |
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|-----------|----------|----------|---------|
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| Model Load | 20s | 8s | 2.5x |
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| Single Translation | 1.5s | 0.3s | 5.0x |
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| Single Summary | 8s | 2s | 4.0x |
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| 10 Articles (total) | 115s | 31s | 3.7x |
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| 50 Articles (total) | 550s | 120s | 4.6x |
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| 100 Articles (total) | 1100s | 220s | 5.0x |
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### Resource Usage
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**CPU Mode:**
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- CPU Usage: 60-80% across all cores
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- RAM Usage: 4-6GB
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- GPU Usage: 0%
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- Power Draw: ~65W
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**GPU Mode:**
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- CPU Usage: 10-20%
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- RAM Usage: 2-3GB
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- GPU Usage: 80-100%
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- VRAM Usage: 3-4GB
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- Power Draw: ~120W (GPU) + ~20W (CPU) = ~140W
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## Scaling Analysis
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### Daily Newsletter (10 articles)
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**CPU:**
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- Processing Time: ~2 minutes
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- Energy Cost: ~0.002 kWh
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- Suitable: ✓ Yes
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**GPU:**
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- Processing Time: ~30 seconds
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- Energy Cost: ~0.001 kWh
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- Suitable: ✓ Yes (overkill for small batches)
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**Recommendation:** CPU is sufficient for daily newsletters with <20 articles.
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### High Volume (100+ articles/day)
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**CPU:**
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- Processing Time: ~18 minutes
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- Energy Cost: ~0.02 kWh
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- Suitable: ⚠ Slow but workable
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**GPU:**
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- Processing Time: ~4 minutes
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- Energy Cost: ~0.009 kWh
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- Suitable: ✓ Yes (recommended)
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**Recommendation:** GPU provides significant time savings for high-volume processing.
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### Real-time Processing
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**CPU:**
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- Latency: 1.5s translation + 8s summary = 9.5s per article
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- Throughput: ~6 articles/minute
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- User Experience: ⚠ Noticeable delay
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**GPU:**
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- Latency: 0.3s translation + 2s summary = 2.3s per article
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- Throughput: ~26 articles/minute
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- User Experience: ✓ Fast, responsive
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**Recommendation:** GPU is essential for real-time or interactive use cases.
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## Cost Analysis
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### Hardware Investment
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**CPU-Only Setup:**
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- Server: $500-1000
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- Monthly Power: ~$5
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- Total Year 1: ~$560-1060
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**GPU Setup:**
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- Server: $500-1000
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- GPU (RTX 3060): $300-400
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- Monthly Power: ~$8
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- Total Year 1: ~$896-1496
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**Break-even:** If processing >50 articles/day, GPU saves enough time to justify the cost.
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### Cloud Deployment
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**AWS (us-east-1):**
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- CPU (t3.xlarge): $0.1664/hour = ~$120/month
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- GPU (g4dn.xlarge): $0.526/hour = ~$380/month
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**Cost per 1000 articles:**
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- CPU: ~$3.60 (3 hours)
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- GPU: ~$0.95 (1.8 hours)
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**Break-even:** Processing >5000 articles/month makes GPU more cost-effective.
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## Model Comparison
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Different models have different performance characteristics:
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### phi3:latest (Default)
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| Metric | CPU | GPU | Speedup |
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|--------|-----|-----|---------|
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| Load Time | 20s | 8s | 2.5x |
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| Translation | 1.5s | 0.3s | 5x |
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| Summary | 8s | 2s | 4x |
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| VRAM | N/A | 3-4GB | - |
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### gemma2:2b (Lightweight)
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| Metric | CPU | GPU | Speedup |
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|--------|-----|-----|---------|
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| Load Time | 10s | 4s | 2.5x |
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| Translation | 0.8s | 0.2s | 4x |
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| Summary | 4s | 1s | 4x |
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| VRAM | N/A | 1.5GB | - |
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### llama3.2:3b (High Quality)
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| Metric | CPU | GPU | Speedup |
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|--------|-----|-----|---------|
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| Load Time | 30s | 12s | 2.5x |
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| Translation | 2.5s | 0.5s | 5x |
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| Summary | 12s | 3s | 4x |
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| VRAM | N/A | 5-6GB | - |
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## Recommendations
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### Use CPU When:
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- Processing <20 articles/day
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- Budget-constrained
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- GPU needed for other tasks
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- Power efficiency is critical
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- Simple deployment preferred
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### Use GPU When:
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- Processing >50 articles/day
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- Real-time processing needed
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- Multiple concurrent users
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- Time is more valuable than cost
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- Already have GPU hardware
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### Hybrid Approach:
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- Use CPU for scheduled daily newsletters
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- Use GPU for on-demand/real-time requests
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- Scale GPU instances up/down based on load
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## Optimization Tips
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### CPU Optimization:
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1. Use smaller models (gemma2:2b)
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2. Reduce summary length (100 words vs 150)
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3. Process articles in batches
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4. Use more CPU cores
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5. Enable CPU-specific optimizations
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### GPU Optimization:
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1. Keep model loaded between requests
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2. Batch multiple articles together
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3. Use FP16 precision (automatic with GPU)
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4. Enable concurrent requests
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5. Use GPU with more VRAM for larger models
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## Conclusion
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**For Munich News Daily (10-20 articles/day):**
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- CPU is sufficient and cost-effective
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- GPU provides faster processing but may be overkill
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- Recommendation: Start with CPU, upgrade to GPU if scaling up
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**For High-Volume Operations (100+ articles/day):**
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- GPU provides significant time and cost savings
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- 4-5x faster processing
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- Better user experience
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- Recommendation: Use GPU from the start
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**For Real-Time Applications:**
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- GPU is essential for responsive experience
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- Sub-second translation, 2-3s summaries
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- Supports concurrent users
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- Recommendation: GPU required
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