Engineering Large Model Platforms: From Trillion-Parameter Models to Open Services

As large language models scale to trillions of parameters and support million-token multimodal contexts, delivering these capabilities stably and economically to developers becomes a major engineering challenge. This article explores the architectural optimizations required for long contexts, such as hybrid linear attention, and details platform-level strategies including context caching, tiered service provisioning, and high-concurrency governance to balance model capability, cost, and stability.