Edge inference is just cloud denial pretending to be innovation
Everyone's rushing to put models on edge devices whilst ignoring the fundamental problem that most applications don't actually need it.
The industry has convinced itself that cramming language models onto every piece of silicon is revolutionary progress. We’re optimising 450-million parameter models to run on embedded hardware and celebrating sub-250ms inference times as if latency was the only thing standing between us and AI ubiquity. It’s not innovation, it’s just expensive local processing for applications that work fine with network calls.
The latency theatre
Most edge inference projects solve problems that don’t exist. Your smart doorbell doesn’t need a vision-language model running locally when it could just send an image to a proper server and get results in 200ms over WiFi. The obsession with edge processing treats network connectivity like it’s 1995 dial-up rather than the reliable, fast connections most devices actually have.
We’re burning engineering effort and silicon real estate to avoid a network hop that takes longer to optimise away than it would to just make the API call.
When edge actually matters
Don’t get us wrong, edge inference has legitimate use cases. Medical devices in remote areas, autonomous vehicles, military applications, anything where network connectivity genuinely can’t be guaranteed. But cramming compressed models into consumer electronics just because you can isn’t solving real problems.
The computational overhead of maintaining local models, updating them, and dealing with their inevitable failures often exceeds the cost of cloud inference anyway. We’ve turned “runs locally” into a feature when “works reliably” should be the goal.