🔧 AI Hardware

10k QPS on Locked-Down GPUs: The Batching Blueprint That Delivers

GPUs idle on single requests — that's 80% waste at peak loads. This batching system flips the script, stuffing 64 requests per inference run while hitting 500ms p99 latency.

Architecture diagram of dynamic GPU inference batching system handling 10k QPS

⚡ Key Takeaways

  • Dynamic 'wait-or-full' batching with EWMA hits 10k QPS and 500ms p99 by balancing latency and throughput. 𝕏
  • Partitioned queues and Protobuf internals eliminate contention, scaling horizontally without locks. 𝕏
  • Feedback loops and DLQs ensure 99.9% uptime — prod ML demands graceful degradation over crashes. 𝕏
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Originally reported by Dev.to

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