gRPC - How AI Systems “Talk” to Each Other
gRPC - How AI Systems “Talk” to Each Other
When we run a model in inference, there are many components working together - the application sending requests, the model computing answers, and the server mediating between them. But how does this communication even happen?
The answer: gRPC.
What is gRPC?
gRPC (short for Google Remote Procedure Call) is a fast and efficient communication protocol that allows one service to run a function located on another server - as if both were part of the same software.
Instead of using REST and sending textual requests (JSON), gRPC uses a compact binary format called Protocol Buffers (Protobuf). This is the key difference: gRPC is not designed to be human-readable - it’s designed to be fast for machines.
Why is it Faster?
- HTTP/2 vs. HTTP/1.1 In traditional REST (based on HTTP/1.1), each request requires opening a new connection to the server. This creates a “traffic jam” when there are many requests - each one waiting for its turn.
gRPC, on the other hand, uses HTTP/2, which allows multiple streams to run simultaneously over a single connection. This way, multiple requests can be sent and answered at the same time without opening thousands of sockets. The result: much lower latency and higher throughput under load.
- Protobuf vs. JSON In REST, data is sent as JSON - a textual format that’s easy to read but heavy for machines. Each value needs to be parsed into strings, numbers, etc.
In gRPC, data is sent as Protobuf, a compressed binary format with a predefined structure. The server and client “agree” on the message structure (schema) in advance, so there’s no need to parse fields repeatedly.
Simply put: JSON = Flexible and convenient, but slow. Protobuf = Rigid and fast.
What About Streaming?
gRPC also supports real-time data streaming, so the server doesn’t have to wait until all output is ready - it can start sending partial tokens during computation. This is what enables language models to “speak” in real time.
Why is This Important for Inference?
Inference systems handle thousands of requests simultaneously. If each one went through regular REST - the system would choke. gRPC enables:
- Huge savings in communication time,
- Reduced CPU load,
- And support for continuous streaming of model responses.
Conclusion
In a world where every millisecond impacts user experience, gRPC is the hidden language that allows AI systems to talk to each other - less slowly, and much more intelligently.