AI Inference Pipelines – Building Low-Latency Systems With gRPC
summary
Built a production inference pipeline using gRPC + Protocol Buffers with persistent HTTP/2 channels, bidirectional async streaming and adaptive batching, achieving 5–10x lower latency versus JSON/REST in their benchmarks. Serving uses Triton/TensorFlow/Torch backends, GPU request batching, Redis-style model cache and Prometheus/Grafana/Jaeger for observability. What works: connection reuse, streaming, async handling; what fails: message marshalling adds small overhead and native browser support is limited, so gRPC‑Web or REST fallback is required. Roadmap includes multimodel routing, edge gRPC‑Web and ONNX/TensorRT/OpenVINO optimizations.
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chapters (13)
- 0:00Welcome
- 1:02AI inference pipelines matter
- 3:10Milliseconds count in AI
- 3:53REST vs gRPC for AI
- 6:25AI inference architecture
- 8:32Performance bottlenecks and fixes
- 9:17Observability in AI pipelines
- 10:20Benchmarks and hyperscaler use
- 12:30gRPC open-source contributions
- 13:34What worked and what didn’t in our gRPC setup
- 15:00Future of AI inference with gRPC
- 16:45Key takeaways
- 17:06Q&A: gRPC batching
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