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.
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. I didn’t stop.
The Obsession: Quantization
“27μs is good,” I thought. “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.