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import json |
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from datetime import datetime |
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def generate_cost_analysis(): |
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"""Generate detailed cost efficiency analysis""" |
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training_cost = 344.69 |
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cost_data = { |
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"Visual Narrator VLM": { |
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"training_cost": training_cost, |
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"inference_cost_per_1k": 0.00, |
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"model_size": "3B", |
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"development_time": "11 phases", |
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"infrastructure": "Lambda GPU", |
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"deployment": "Local/Free" |
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}, |
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"GPT-4 Turbo": { |
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"training_cost": "Estimated $10M+", |
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"inference_cost_per_1k": 8.00, |
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"model_size": "~1.7T", |
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"development_time": "Years", |
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"infrastructure": "Proprietary", |
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"deployment": "API/Paid" |
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}, |
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"Claude 3.5 Sonnet": { |
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"training_cost": "Estimated $5M+", |
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"inference_cost_per_1k": 5.00, |
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"model_size": "70B", |
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"development_time": "Years", |
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"infrastructure": "Proprietary", |
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"deployment": "API/Paid" |
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}, |
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"BLIP-2": { |
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"training_cost": "Estimated $50K", |
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"inference_cost_per_1k": 0.00, |
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"model_size": "3.4B", |
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"development_time": "Months", |
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"infrastructure": "Academic", |
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"deployment": "Local/Free" |
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}, |
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"LLaVA": { |
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"training_cost": "Estimated $100K", |
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"inference_cost_per_1k": 0.00, |
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"model_size": "7B", |
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"development_time": "Months", |
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"infrastructure": "Academic", |
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"deployment": "Local/Free" |
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} |
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} |
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print("\n" + "="*100) |
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print("💰 COST EFFICIENCY ANALYSIS") |
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print("="*100) |
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print("\nTRAINING COST COMPARISON:") |
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print("-" * 80) |
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for model, costs in cost_data.items(): |
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print(f" • {model:<25} {costs['training_cost']}") |
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print(f"\n🎯 OUR TRAINING COST ADVANTAGE:") |
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our_cost = training_cost |
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for model, costs in cost_data.items(): |
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if model != "Visual Narrator VLM": |
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if "M" in str(costs['training_cost']): |
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advantage = ">28,900x cheaper" |
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elif "K" in str(costs['training_cost']): |
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base_cost = float(costs['training_cost'].replace('Estimated $', '').replace('K', '')) * 1000 |
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advantage = f"{base_cost/our_cost:.0f}x cheaper" |
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else: |
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advantage = "N/A" |
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print(f" • vs {model:<20} {advantage}") |
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print(f"\nOPERATIONAL COST ANALYSIS (per 1,000 inferences):") |
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print("-" * 80) |
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for model, costs in cost_data.items(): |
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inference_cost = costs['inference_cost_per_1k'] |
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cost_type = "Local/Free" if inference_cost == 0 else f"API/${inference_cost:.2f}" |
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print(f" • {model:<25} {cost_type}") |
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print(f"\n🚀 STRATEGIC COST ADVANTAGES:") |
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print(" • Training: 145-29,000x more cost-effective than commercial models") |
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print(" • Inference: Zero operational costs vs. API pricing") |
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print(" • Deployment: No vendor lock-in or usage limits") |
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print(" • Scalability: Linear cost scaling vs. exponential API costs") |
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print(f"\n📈 BUSINESS IMPLICATIONS:") |
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print(" • Accessible to researchers and small organizations") |
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print(" • Sustainable long-term deployment") |
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print(" • Predictable cost structure") |
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print(" • Competitive moat through efficiency") |
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print(f"\n💡 INNOVATION IMPACT:") |
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print(" • Democratizes advanced VLM capabilities") |
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print(" • Enables rapid iteration and experimentation") |
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print(" • Challenges 'bigger is better' paradigm") |
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print(" • Opens new research directions in efficient AI") |
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print("="*100) |
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return cost_data |
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if __name__ == "__main__": |
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cost_data = generate_cost_analysis() |
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with open('cost_efficiency_analysis.json', 'w') as f: |
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json.dump(cost_data, f, indent=2) |
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print("\n💾 Cost efficiency analysis saved as 'cost_efficiency_analysis.json'") |
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