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Real-Time Inference

Benched.ai Editorial Team

Real-time inference targets sub-second latency so that model outputs feel instantaneous in interactive applications.

  Latency Budget (chat, 200 tokens)

ComponentTarget
Network RTT≤50 ms
Queue & batch≤80 ms
Inference≤300 ms
Post-process≤20 ms
Total p95≤500 ms

  Optimization Techniques

AreaTechnique
ModelQuantization, LoRA distilled models
SchedulerDynamic batching with 10–50 ms windows
TransportgRPC over HTTP/3 streams
GPUFP8 kernels, tensor parallel

  Current Trends (2025)

  • Speculative decoding + vLLM achieves 2× speed-up.
  • Edge GPU pods serve 50 ms RTT for gaming companions.
  • Async I/O in Python servers (FastAPI + uvloop) cuts queuing.

  Implementation Tips

  1. Prefer streaming token APIs to show partial output.
  2. Pin threads to CPU NUMA node adjacent to GPU.
  3. Measure tail latency (p99) not just average.