Cost-Aware Scaling of Large Language Models

Project Proposal: Cost-Aware Scaling in Multi-User LLM Systems Problem Statement Large Language Models (LLMs) are increasingly used in chatbots, educational tools, and enterprise applications. While they deliver powerful results, LLMs are computationally expensive. When multiple users send queries at the same time, systems may face not only performance issues (slow responses, failures) but also high operational costs due to API charges or GPU usage.

Objectives
This project focuses on designing and evaluating strategies for scaling LLM applications in a cost-efficient way under multi-user load. We will: Simulate multiple concurrent users sending prompts to an LLM-based system.

Measure performance metrics → response times, error rates, resource usage. Measure cost metrics → API billing per request, or GPU-hour cost for local deployment.
Analyze trade-offs between speed, reliability, and cost. Propose cost-aware scaling strategies, such as: Dynamic model routing (small model for easy queries, big model for complex ones). Batching requests to reduce API/GPU calls. Auto-scaling policies to add/remove GPU resources only when needed. Methodology Load Simulation: Use tools like Locust or JMeter to simulate 50–500 users. Deployment Options: Cloud API (e.g., OpenAI / HuggingFace). Local open-source model (e.g., Llama 2/3). Monitoring: Track latency, error rates, and GPU/CPU usage (Prometheus + Grafana). Cost Analysis: For API: calculate per-request token usage cost. For local: estimate GPU runtime cost. Scaling Strategies: Implement queueing, batching, and auto-scaling. Expected Outcomes A benchmark report showing how costs increase with user load. Clear trade-off curves: cost vs performance. Best practices for deploying LLM apps cost-effectively in real-world scenarios. Significance This project will provide practical insights for startups, ed-tech tools, and enterprises that want to scale AI applications without overspending. It connects AI, systems design, and DevOps principles, making it highly relevant to industry challenges.