User Intent Prediction #5: 3,700x Faster. Still Not Fast Enough.
I optimized a model so fast the universe politely asked me to fuck off.
I spent 6 hours optimizing a model that was already 3,700 times faster than a blink of an eye.
I had a model. It ran in 27 microseconds. That’s 0.027 milliseconds. That’s fast enough to handle the traffic of Amazon, Google, and Facebook combined (on a single laptop).
Real-time funnel adaptation needs <50ms. Not <1ms. Definitely not <0.03ms.
But I’m an engineer. So I thought: “I can make it faster.”
The result: I crashed the operating system.
The Benchmark: 27 Microseconds
I converted my model to ONNX.
- Mean latency: 25μs
- Throughput: 28,000 predictions per second
To put that in perspective:
- A blink of an eye: 100,000μs
- A standard API request: 50,000μs
- My model: 27μs
I had solved the latency problem. The ticket was closed. But I didn’t stop.
The Obsession: Quantization
“27μs is good,” I thought. “But if I quantize it to INT8, I could hit 10μs!”
I wanted to optimize for the sake of optimization. I wanted the high score. My funnel had a 1.5% conversion, and I was here fighting microseconds.
The experiment: I ran the standard quantization tools.
The result: Segmentation fault (core dumped)
The Crash
I tried again. Different tool. Different library.
Exit code 139 (SIGSEGV)
My optimization script was crashing the kernel.
The reason: My model was too small. It had 48 parameters. Total.
The overhead of the quantization logic—setting up the lookup tables—was larger than the model itself.
I was packing a sandwich into a shipping container and calling it “efficiency.” The computer was literally rejecting my stupidity.
The Lesson
Optimization has a stopping point.
This wasn’t high-frequency trading. It was a diet quiz. I was optimizing for vanity metrics, not business value.
- Goal: < 50,000μs (50ms)
- Reality: 27μs
- Margin: 1,850x faster than required
When to stop optimizing:
- When you hit your SLA.
- When the optimization costs more than the compute savings.
- When your tools start segfaulting because your problem is too small.
The Outcome
I deployed the un-quantized, “slow” 27μs model. It runs in production. It handles every user event.
Nobody noticed. Because nobody buys faster just because your matrix multiplies faster.
This connects to when calibration fixed the real problem—while I was obsessing over microseconds, the model was 90% overconfident. I was optimizing the wrong thing.