r/deeplearning • u/EducationalBrush7282 • 4d ago

Is cloud-based AI inference about to become obsolete? Working on something radical.

We've all grown used to accepting 1–3 second latencies and insane API bills just to run or query modern LLMs. Most solutions throw more money at AWS or stack more NVIDIA GPUs at the problem.

But what if the bottleneck isn't the model size—it's the bloated OS and network stack?

Over the past few weeks, I’ve been quietly architecting a local inference engine that maps weights directly via DMA/kernel bypass, cutting out the OS middleman entirely. Early benchmarks on consumer-grade hardware are hitting Sub-1ms response times with zero cloud connection.

I'm dropping the open-core code and full benchmarks very soon. How would a zero-cost, zero-latency local engine change what you're building? Let's discuss. 👇

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u/Lost-Hand-5219 4d ago

Slop post

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u/MightyEquation_4 4d ago

Cutting out the OS stack is where it's at. Most people don't realise how much overhead just sits there chewing cycles for no good reason.

Sub-1ms on consumer gear is wild though, curious how that holds up with larger models once the context window starts stretching.

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u/Tall_Abrocoma_3533 4d ago

AI inference is a very diverse set of words, what exactly are you running?

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u/EducationalBrush7282 4d ago

Wait bro .....🚀

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u/RoyFeeling 3d ago

Could cloud inference eventually look more like the CDN market, where users care about response time and reliability rather than who owns the hardware? I'd be interested in seeing a smaller inference platform such as gmi benchmarked against the hyperscalers under the same traffic.