Optimizing LLM Design: Cost, Performance & Model Choice

Choosing the right LLM model is vital for balancing cost, performance, and capability. The LLM system design process now includes four key levers at inference time: model size scaling, series (thinking) scaling, parallel scaling, and input context scaling. These factors can multiply inference costs by thousands, making cost-efficiency essential. Key model selection criteria include matching benchmark performance with real-world tasks, multimodality, context window, latency, reasoning ability, security, and trustworthiness. Open-weight and closed-API models offer different trade-offs: open models provide flexibility and data security, while closed APIs benefit from optimized GPU utilization and managed compliance. A practical design guide emphasizes choosing between open and closed models, gauging reasoning needs, evaluating model attributes (accuracy, speed, context, multimodality), and applying prompting, RAG, and custom evaluation. Fine-tuning and distillation enable deep specialization in narrow domains. By following this LLM system design framework, developers can optimize model selection, manage inference costs, and deliver reliable AI solutions.