How does ChatGPT architecture scale to billions of parameters in production systems?

ChatGPT scales using distributed training, model parallelism, and optimized inference serving infrastructure.

Scaling ChatGPT-like models involves splitting computation and memory across multiple GPUs and nodes. Techniques such as tensor parallelism, pipeline parallelism, and data parallelism are combined to train models with billions of parameters. During inference, optimized serving stacks use KV caching, quantization, and batching to reduce latency. The transformer architecture remains unchanged, but the execution is distributed across hardware.

At scale, bottlenecks shift from compute to communication overhead (GPU-to-GPU synchronization), memory bandwidth, and inference latency.…