Analysis of LLM Training and Serving Mechanisms

Through an in-depth analysis of Memory/Compute Scaling in LLMs, this piece explores the training and serving mechanisms of large-scale models.

The key points are as follows:

• Maximizing Inference Efficiency: Explains that inference using Large Batching is highly efficient in terms of memory and compute resource utilization.
• Infrastructure Optimization: Analyzes why operating LLMs in local environments or private clouds can be inefficient from a scaling perspective.
• Real-World Case Analysis: Provides technical insights into how major models such as GPT, Claude, and Gemini are actually trained and served in production environments.