I’ve been debating the 'Cloud vs. Edge' future with colleagues, and the consensus is usually 'Cloud wins because it has more compute.'
I decided to test this by building a raw memory engine that bypasses the OS to stream data directly from consumer NVMe SSDs (like the one in a PS5) to the processor.
The Result: I’m getting sub-microsecond retrieval speeds locally. The speed of light alone makes the Cloud 50,000x slower (latency-wise) for this specific task.
I am really focused on solving the compute waste crisis, as quiet frankly it is disgusting the amount of output and destruction AI is causing behind the scene, however wonder if it is actually viable to solve, because all large AI companies have a sort of oligopoly on cloud pricing, that is mutually beneficial.
My Question for r/Futurology: If a single developer can build 'sovereign memory' that outperforms a massive cloud cluster on latency, does this mean the future of AI is actually decentralized? Or will the sheer capacity of the cloud always win, even if it's slower?
to add this is not a business, I am not selling or anything like that, just simply in early stages of trying to solve something that I think is deeply fundamental to us as humans.
Submission Statement: I want to discuss the potential end of the "Cloud Era" for Artificial Intelligence. As consumer hardware (specifically NVMe storage) reaches speeds that rival traditional RAM, the physical latency of the internet (speed of light) becomes the bottleneck.
I believe this will drive a future of "Sovereign Intelligence," where personal AI agents run locally for privacy and speed, rather than relying on centralized corporate servers. I am looking to discuss whether convenience will keep us tethered to the cloud, or if raw performance will force a return to decentralized, local computing.
https://ryjoxdemo.com/
5 Comments
We are currently sleepwalking into an environmental and architectural crisis with Artificial Intelligence, and the root cause is our obsession with the Cloud. For the last decade, the default assumption has been that more intelligence equals bigger data centers. We treat devices like our laptops, phones, and eventually robots as dumb terminals that must constantly ping a server farm in Virginia to think. This architecture is rapidly becoming unviable, both physically and environmentally.
The energy cost of Centralized AI is staggering. We are not just burning gigawatts to train models. We are burning massive amounts of energy simply moving data back and forth across the planet for every single inference query. It is the digital equivalent of shipping water by plane instead of using a tap. If we want AI to be ubiquitous and integrated into every drone, car, and home assistant, we physically cannot build enough data centers to process that traffic centrally without disastrous environmental consequences.
Beyond the energy problem, we have hit a hard physics barrier which is the speed of light. As we move toward Agentic AI and robotics, a 50ms round trip to the cloud is a lifetime. A robot cannot wait for a server to tell it not to crash. The Thin Client era is ending because physics demands it.
The post above highlights a critical shift where consumer hardware has finally caught up. With NVMe speeds hitting 14GB/s, we can now treat local storage as system memory. This means we can finally break the dependency on centralized cloud infrastructure. We are moving toward an era of Sovereign Intelligence where AI processing happens entirely on the device, using the energy and hardware that already exists, rather than renting it from a hyperscaler.
I believe this transition from Cloud First to Local First will be the defining architectural shift of the next decade, transforming AI from a rented service into a fundamental utility we own and control. I am interested to hear the thoughts of the community on whether the convenience of the Cloud is worth the latency and energy tax, or if a return to decentralized, offline computing is inevitable for the survival of AI.
The problem at the moment is that current good models do not fit in RAM of local devices for a cost that makes sense. In 5-10 years after moore’s law has kicked in, I would bet that most AI tasks will be run locally. These will use open source models, of which the best ones come from China right now.
There are models that run on consumer spec hardware that are reasonably decent. But when people want to use AI they currently want to use the best, which is cloud based.
I don’t really understand your use of terms or what you’re trying to test. Often “edge” refers to a CDN or similar, but it sounds like you actually mean on-device? Generally the popular AI models are way too big to run on consumer devices both because of RAM and processing speed; I’m not really sure what you mean by “memory engine” but it sounds like you’re basically saying “SSD seek times are faster than a network request” which seems both obvious and kinda irrelevant.
Presumably if a model is running on-device then the database it’s working with is on-device too. If it’s running in a datacenter then the data is also in the datacenter. Obviously you would never want those two things separated by the public internet unless you had no other choice.
AI models we use currently all suffer from a fundamental architecture problem – it stems from their method of aggregating data. Hallucinations, murky datasets, and “garbage in, garbage out, garbage in again” self-poisoning can only get continually worse as unwanted updates are pushed to all users.
Localized AI trained on extremely rigid, user-selected data sets are the only way we’ll get anything useful from these models, if we can *ever* get anything useful. The “off-the-shelf” starter kit should functionally be the Oxford English dictionary and nothing else.
Local has always been faster. The upfront cost to make it all work has also always been higher, and its currently trending UP instead of down. Unless the costs come down significantly, its gonna be more palatable for a user to spend 20$/month compared to $3k once.