Reiner Pope 深度访谈

The massive scale of modern LLM training. How GPT, Claude, and Gemini are actually trained across thousands of GPUs with sophisticated orchestration systems.

现代LLM训练的巨大规模。GPT、Claude和Gemini如何在数千个GPU上通过复杂的编排系统进行训练。

The engineering challenges of serving billions of LLM inference requests. Batching, quantization, memory optimization, and the economics of cloud AI compute.

服务数十亿LLM推理请求的工程挑战。批处理、量化、内存优化，以及云AI计算的经济学。

Where the bottlenecks really are - networking, memory, power. The race to build more efficient AI hardware and what comes after GPU scale.

真正的瓶颈在哪里——网络、内存、电力。构建更高效AI硬件的竞赛，以及GPU规模之后的未来。