The official Ecosystem account of @AethirCloud - Powerful Partnerships, Endless Potential | @AethirCloud - All things #Aethir
Aethir
Joined April 2024
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Gartner expects worldwide AI spending to rise 47% in 2026, to roughly $2.6 trillion, with the AI platforms and models market up 63% on its own.
Demand climbing at that slope outruns anything a construction schedule can deliver, which is why capacity keeps getting rationed by quota, region, and waitlist.
Aethir scales differently: 430,000 GPU containers across 200+ locations in 94 countries, added by connecting supply that already exists and already has power.
With Aethir ACCELERATE, we are also building our own mid-sized data centers to support compute-heavy AI workloads that need access to premium-quality, affordable compute, without the multi-year data center buildout process.
A curve that steep needs an elastic answer, not a bigger campus.
Aethir ACCELERATE Presents AI ACCESS Spotlight. Featuring @Sogni_Protocol
Creative AI has a bottleneck, and it’s the meter. Every new variation costs credits. Expensive hardware and heavy censorship add more friction.
So creators settle for work they’re not happy with, or never finish at all.
Here’s how Sogni is fixing that🧵
Aethir’s approach:
More creators and bigger models mean one thing: more GPU capacity.
Aethir ACCELERATE is how we plan to add it, by bringing GPU compute online in future AI data centers across the US and Europe so AI builders can keep shipping.
Aethir Ecosystem retweeted
Major ATH tokenomics update: we’re introducing daily ATH burns, funded by a share of network fees.
Every burn goes to a public burn address, where tokens are permanently removed from supply. The mechanism goes live in November.
We’re also replacing the fixed platform fee with a floating network fee, set by a published formula that reads market conditions. When the market weakens, the fee falls and the share that gets burned rises.
Cloud Hosts are protected by design: the fee can never exceed the value of Cloud Host rewards.
The underlying host emissions schedule remains unchanged. The update changes the fee mechanism applied around those rewards.
A dashboard tracking daily, monthly and cumulative ATH burned will be available in November.
Burns that follow real network activity, and you can check every one of them.
Read the full announcement below👇
Most inference is memory-bound rather than compute-bound.
Producing each token means reading the model weights and a steadily growing KV cache out of high-bandwidth memory, so the accelerator spends much of its time waiting rather than calculating.
That is why a serving fleet can look poorly utilized while running flat out, and why adding accelerators to a memory-bound step often changes very little.
The levers that do work are batching, cache reuse, quantization, and running the model near the request.
Aethir Ecosystem retweeted
There is a real dial between latency and energy in model serving, and most teams never touch it.
Measurements show that pushing a fixed volume of prompts through fewer model instances raises the effective request rate and lowers the energy spent per answer, while making each individual response slower.
Batch harder and you serve more work per watt. Batch less and you answer faster.
Which side to sit on is a product decision long before it is an infrastructure one, and it deserves an earlier conversation.
An accelerator retires from frontier training long before it stops being useful.
Serving a mature model is a different job: a smaller memory footprint per request, real tolerance for looser interconnect, and throughput that matters more than tight synchronization.
Hardware displaced by the next training generation usually has years of productive inference ahead of it.
The industry talks about GPUs aging on a calendar. They age against a workload, and inference is the workload that keeps older silicon busy.
Aethir coordinates a great deal of that second career.
Aethir Ecosystem retweeted
AMD Helios is shipping: 72 MI455X GPUs per rack, 6 GW from OpenAI, up to 2 GW from Anthropic, and a $1.2B Vultr order.
It brings a second-source GPU to a market that has run almost entirely on NVIDIA.
A second supplier adds capacity and pricing pressure, but software and networking still decide how easily workloads move.
However, GPU portability decides how far lock-in loosens.
For Aethir’s decentralized GPU cloud, this means routing work across a more diverse market.
Read our new blog for all the details👇
Aethir Ecosystem retweeted
Axe Compute has more than $3.45B in contracted customer AI infrastructure projects to bring into production.
Axe signed @WeAre_Penguin as a multi-year strategic partner to speed up Axe Compute Build, its build-to-order program🧵
An H100 bought in 2024 is not the asset it was.
Hardware depreciates on a schedule set by Nvidia's release cadence, not by your roadmap, and every new generation shortens the runway on the last one.
Owning silicon means carrying that curve on your own balance sheet.
Renting access on Aethir means someone else carries it across 430,000 GPU containers in 94 countries, at 40% to 80% below hyperscaler pricing, with no residual value left to write down.
Buy the output, not the depreciation. Compute should be an operating cost, instead of an aging asset.
Congrats to the @bstocksfinance team on reaching the $1B AUM milestone and pioneering tokenized stock trading.
@AxeCompute's AGPUB is part of this exciting journey as a tokenized GPU compute stock.
Inference demand follows human waking hours.
A fleet concentrated in one region has a busy afternoon and a very quiet 3 am, every day, and the quiet hours still consume rack space, cooling and capital.
Grid engineers have noticed the same property from the other side: inference is one of the few workloads that can be dynamically routed, which makes it unusually good at chasing whatever capacity happens to be free.
Aethir containers sit in 94 countries, so somewhere on the network it is always working hours.
Our upcoming Aethir ACCELERATE data centers will support further expansion of our AI compute capabilities across the US and Europe.
Aethir Ecosystem retweeted
Faster builds, more capacity.
Congrats to @AxeCompute on a multi-year strategic partnership with @PenguinSolutions to speed up Axe Compute Build and take on more projects.
Axe designs, owns and operates dedicated AI infrastructure for enterprises. Penguin now adds expert deployment, its ClusterWareAI software and 24x7 network operations across multiple Axe sites, including large GPU clusters.
Looking forward to Axe bringing $3.45B+ in contracted customer projects into production faster!
Demand for Axe Compute Build is surging. Today AGPU announced a multi-year partnership with Penguin Solutions (NASDAQ: PENG) to expand capacity and accelerate delivery to meet it.
Penguin will support multiple sites as we work to bring $3.45B+ in contracted AI infrastructure projects to production globally.
#AIInfrastructure $AGPU $PENG #AIInfrastructure @WeAre_Penguin
ASML just reported €11.7B in Q3 2026 net sales, up 56% from €7.5B a year earlier, at a record 57.5% gross margin.
Every leading-edge AI GPU is printed with ASML lithography. And ASML’s customers don’t expect supply to meet demand until at least 2028.
ASML says even 30% capacity growth in 2027 and again in 2028 won’t be enough.
For neoclouds, that means GPU supply stays tight well past next year.
Aethir works on two lines: A decentralized network that puts already-deployed GPUs to work, and upcoming ACCELERATE contracted clusters for enterprises that need dedicated capacity.
Measured on real hardware, a data center GPU idles near 72 watts and peaks around 696.
A sixteen-node training run swung between roughly 12 and 48 kilowatts as it stepped through batches.
Grid operators plan, price and approve interconnections around steady demand, which is precisely what AI workloads are not.
The volatility matters more than the average, and utilities have grown cautious accordingly.
A load spread over many modest sites is far easier for a grid to absorb than the same load in one place, which is the shape Aethir capacity takes, spread across 200+ locations and 430,000+ GPUs.
Gartner expects enterprises to use specialized small models roughly three times more than general-purpose large ones by 2027.
That is a change in the shape of the workload. A fleet of narrow models serving scoped tasks means many small concurrent jobs, short contexts, tight latency budgets, and a lot of places to run.
It looks nothing like the monolithic cluster most people still picture when they hear AI infrastructure.
Aethir was assembled the way that workload is arriving: distributed, across 94 countries and 200+ locations.
