Dashboard Lied: 99% of AI Requests Actually Failed @QAInsights
Dashboard Lied: 99% of AI Requests Actually Failed  @QAInsights
Uploaded May 2026 | Updated September 2026, 3 weeks ago
Is your LLM "production-ready," or is it just hiding a massive queue problem? In this video, I demonstrate how a single-user baseline can give you false confidence and how to use NVIDIA AIPerf to reveal the truth.

qainsights.com/99-of-requests-failed-and-my-dashboard-showed-green

What you’ll learn:

The Setup: Running Granite4:350m locally via Ollama.

The Trap: Why a single-user baseline (Concurrency 1) is a "baseline that lies."

The Reality Check: What happens to TTFT (Time to First Token) when you jump to 50 concurrent users.

The "Goodput" Metric: Using the --goodput flag to measure how many requests actually meet your SLO (and why 99% might be failing).

The Hidden Insight: Why ITL (Inter-Token Latency) staying solid means you have a queue problem, not a model problem.

Tools Mentioned:

NVIDIA AIPerf: github.com/ai-dynamo/aiperf

Ollama: Local LLM inference server.

Model: IBM Granite 4 350M.

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Dashboard Lied: 99% of AI Requests Actually Failed

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