RTX 3050 - Order Now
Home / Blog / Tutorials / AI Soak Testing Pre-Launch
Tutorials

AI Soak Testing Pre-Launch

Soak testing for AI services — sustained-load testing that catches memory leaks, thermal issues, KV cache fragmentation.

Soak testing — sustained load over 24-72 hours — catches issues that short load tests miss: slow memory leaks, gradual thermal throttling, KV cache fragmentation, log volume bottlenecks. For production AI deployments, run a soak test before launch; production traffic for the first week is essentially the soak test if you skip it.

TL;DR

Run synthetic production-like traffic at 70% of capacity for 24-72 hours. Watch for: GPU memory drift, thermal throttling onset, p99 latency degradation over time, log volume / disk fill, error rate accumulation. Resolve any drift before user-facing launch. Standard SRE practice; particularly important for AI given GPU thermal characteristics.

Why soak

Issues that short load tests don't catch:

  • Memory leaks: vLLM / Python / CUDA leaks over hours
  • Thermal accumulation: GPU temp climbs over 30+ minutes; eventual throttling
  • KV cache fragmentation: gradual buildup affects performance
  • Log volume disk fill: structured logs at full volume can fill disks faster than expected
  • Connection pool exhaustion: PostgreSQL / Redis connections leak under load
  • Cron / scheduled job interaction: nightly jobs vs sustained load

Setup

  • Synthetic traffic generator: k6, Locust, or custom Python with realistic prompt distribution
  • Target: 70% of expected peak production load
  • Duration: 24-72 hours minimum; weekend run is convenient
  • Log aggregation captures all metrics during run
  • Alert on degradation thresholds during soak

What to watch

  • GPU memory: should be stable; drift indicates leak
  • GPU temperature: stable steady-state expected; climb indicates cooling issue
  • p99 TTFT / TPOT: stable over time
  • Error rate: 0% baseline expected; drift indicates accumulating issue
  • vLLM queue depth: bounded; sustained growth indicates capacity issue
  • Disk usage: log volume sustainable for retention window
  • Connection pool sizes: stable

Verdict

Soak testing pre-launch is cheap insurance against the kind of incident that takes down production a week after deploy. ~£20 of GPU time + a weekend of synthetic traffic prevents the "everything was fine yesterday" class of failure. Standard SRE practice; particularly worthwhile for AI given GPU thermal and KV-cache dynamics.

Bottom line

Run a soak test before launch. See load test guide.

Need a Dedicated GPU Server?

Deploy from RTX 3050 to RTX 5090. Full root access, NVMe storage, 1Gbps — UK datacenter.

Browse GPU Servers

gigagpu

We benchmark, deploy, and optimise GPU infrastructure for AI workloads. All data in our guides comes from real-world testing on our UK-based dedicated GPU servers.

Ready to deploy your AI workload?

Dedicated GPU servers from our UK datacenter. NVMe storage, 1Gbps networking, full root access.

Browse GPU Servers Contact Sales

Have a question? Need help?