OpenAI recently revealed a storage architecture utilizing sharding and caching technology to reliably support over a billion users, achieving data processing efficiency and latency optimization.
Imagine this: a billion people around the world are asking ChatGPT questions at the same time. It’s like a global-scale library where everyone rushes to the librarian to ask them to find a book. In an ordinary library, it would instantly turn into chaos and grind to a halt, but ChatGPT processes these massive requests reliably and fluidly. What kind of technical foundation makes this “magic” response possible?
| Recently, OpenAI revealed its core engineering strategy for supporting over a billion users worldwide. OpenAI Habitat: 70 million requests/s and Rust instead of Python This is being evaluated as a monumental leap in the infrastructure field, just as important as developing a new AI model. [OpenAI: Storage architecture for 1 billion… | AIKraft](https://aikraft.ru/news/rapidly-scaling-online-storage-to-serve-over-1-billion-chatgpt-users) |
Why does this matter?
| From a user’s perspective, ‘latency’ (the time spent waiting between sending a question and receiving an answer) is the most critical factor determining service quality. Maintaining this latency at under 100ms (0.1 seconds) while delivering top performance in an environment accessed by a billion users simultaneously is an extremely complex engineering challenge. [OpenAI: Storage architecture for 1 billion… | AIKraft](https://aikraft.ru/news/rapidly-scaling-online-storage-to-serve-over-1-billion-chatgpt-users) Without this kind of infrastructure optimization technology, we would likely face service errors daily or spend forever waiting for answers. |
The Library Analogy: Sharding and Caching
Let’s break down this complex technology using the library analogy mentioned earlier.
The first core technology is Sharding (splitting data). Instead of one giant library, imagine dividing the bookshelves into thousands of small zones and assigning several librarians to each zone. When a user’s request comes in, the system immediately identifies which zone the data is in, and the assigned librarian finds it instantly. Since no single librarian needs to search the entire giant bookshelf, the workload is distributed and far more efficient.
| The second is Caching (temporary storage). This is a strategy where popular books that people frequently look for, or conversations someone just looked up, are placed right next to the librarian’s desk. Since they don’t need to search through complex archives, they can retrieve information immediately, dramatically increasing response speed. [OpenAI: Storage architecture for 1 billion… | AIKraft](https://aikraft.ru/news/rapidly-scaling-online-storage-to-serve-over-1-billion-chatgpt-users) |
Current Status: How far has it come?
OpenAI has shared the design methods for the large-scale storage architecture it has accumulated over time. OpenAI Habitat: 70 million requests/s and Rust instead of Python As a result of consistently improving database management technology, it has recently advanced to a phenomenal level where even a single database server system can handle millions of queries per second. OpenAI Scales Single Primary PostgreSQL Instance to Millions of Queries per Second for ChatGPT - InfoQ
What happens next?
Future AI service competition will move beyond comparing the intelligence of models into a head-on engineering battle over “how many people can reliably use the service.” The architecture revealed this time demonstrates the robust foundation necessary for AI to move beyond simple experimentation and establish itself as an essential service in our daily lives. The era where the entire human population, well beyond a billion people, can freely converse with AI anytime and anywhere is rapidly approaching.
MindTickleBytes AI Reporter’s Perspective
Behind the flashy performance of AI models lies the blood and sweat of engineering. Optimization that reduces technical latency to the limit is the vital heart that determines whether we perceive AI as ‘magic’ or as a ‘slow and frustrating toy.’
References
- OpenAI Habitat: 70 million requests/s and Rust instead of Python
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[OpenAI: Storage architecture for 1 billion… AIKraft](https://aikraft.ru/news/rapidly-scaling-online-storage-to-serve-over-1-billion-chatgpt-users) - OpenAI Scales Single Primary PostgreSQL Instance to Millions of Queries per Second for ChatGPT - InfoQ
- Quantum encryption
- Sharding and caching
- Data compression algorithms
- Data deletion
- Maintaining latency of less than 100ms
- Stopping GPU usage
- To reduce AI training time
- To allow a massive number of users to use the service reliably at the same time
- To make computer hardware cheaper