io.net (@ionet)
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Cheaper tokens should mean cheaper AI bills.
They don't.
Cheaper tokens unlock agentic workflows, and agents burn 5-30x more tokens per task.
Consumption is outpacing the price drop.
The real problem is idle GPUs. With average enterprise utilization sitting around 5% most companies aren't paying too much for tokens, they're paying for compute they're not using.
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Day zero, and we're already running it.
GLM-5.3-Flash: 320B params, 18B active. Frontier intelligence at flash-tier cost.
Efficient architecture needs compute that scales.
That's where comes in.
More capable and affordable models and accessible compute is where the AI industry is headed. And we're helping to make it happen.
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Introducing GLM-5.3-Flash
- Leading capabilities at a highly competitive price
- Natively multimodal with a 1M-token context window
- A 320B-A18B model released under the MIT License
- Previously previewed as Ox Alpha, running entirely on Chinese AI chips
Blog:
Available now across all official platforms:
Weights:
API:
Coding Plan:
ZCode:
Chat:
AutoClaw:
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The AI race isn't just about who has the best ideas.
It's about who has access to compute.
@ionet CEO @gaurav_io joined the @RealAllinCrypto Podcast to talk about the infrastructure battle happening underneath AI:
→ Why GPU demand keeps accelerating
→ How DePIN unlocks unused compute around the world
→ Why crypto found its most important real-world use case
→ What happens if AWS, Google and Microsoft control AI's infrastructure
When a handful of companies control the infrastructure, they also control who gets access to intelligence.
But there is another way.
Watch the full conversation ↓
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This sums up the problem with AI today.
Full-stack works if you're @OpenAI. They control the chips and the models, and have the capital for both.
Normal AI teams don't have that luxury.
They need throughput and low latency without owning the silicon.
That's the gap closes.
Access to high-performance compute, not just for the few who can build their own chip.
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Since announcing Jalapeño, our first custom inference chip, we’ve been testing it and the system around it.
The results show a major advance: more intelligence from every watt and faster responses, delivering both higher throughput and lower latency in one architecture without sacrificing efficiency.
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$157,680.
That’s the annual difference between running 8x A100s 24/7 on AWS vs.
Same GPUs. Very different outcome.
Centralized clouds offer quotas, lock-in, and opaque pricing.
Decentralized compute offers global supply, instant access, and full control and flexibility.
Centralized clouds still have their place. But for most of AI workloads, paying a hyperscaler tax doesn’t.
We break down the architectures, costs and tradeoffs ↓
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H200 beats H100 for AI inference.
But not for the reason you think.
We ran the same DeepSeek model on both GPUs with the same traffic for 10 days. The H200 delivered 2.5× more tokens for just 33% more rental cost.
But the biggest lesson wasn't about the GPUs. It was about how they're connected.
NVSwitch vs PCIe changed which workloads and configurations were actually possible.
So, when you're choosing AI infrastructure, don't just read the GPU spec sheet. Check the interconnect.
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A 5% government stake in the world's most valuable AI lab isn't oversight. It's ownership.
Concentrated compute was already a bottleneck. Now it's a jurisdiction.
When access to frontier AI depends on one company's relationship with one government, you don't have infrastructure.
You have a permission slip.
Builders deserve better. @ionet is here to make sure it happens.
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The Incentive Dynamic Engine (IDE) changed the economics of AI compute.
Explorer is how you know it's working.
On-chain emissions. Live burn data. Network activity in real time.
Decentralized infrastructure that shows receipts.
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OpenAI just built its own chip.
@Google has TPUs. @awscloud has Trainium. Now @OpenAI has Jalapeño.
Tech giants controlling every part of AI is not the solution to the compute crisis.
That's what @ionet is here for.
Open, distributed GPU infrastructure. No proprietary silicon. No closed ecosystem.
Any model, any team, any workload.
Open compute. Affordable. Accessible. That's how AI scales.
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Real infrastructure.
Real utility.
The results speak for themselves.
#DePIN# Leaders Just Dropped $43.74M in 30D Revenue
($12.35M) & #Helium# ($12.20M) dominating as real-world infrastructure turns into real cash on-chain. Decentralized compute, networks & geospatial the #tokenization# of physical assets is here.
This is the backbone of #Web3#.
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At least 12 million $IO burned in year one alone.
Real usage. Real scarcity. Real infrastructure.
The Incentive Dynamic Engine.
Watch the explainer.
Fixed emissions were always DePIN's flaw.
Token price drops. Suppliers go offline. Capacity shrinks. Downward spiral.
The IDE is a new way forward. @TheBlockCo breaks it down.
Revenue-linked payouts. Burns when there's surplus. Infrastructure that stays online regardless of market conditions.
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A big thanks to @ionet for commissioning this report. 🤝
Full article:
If you enjoyed this thread, consider subscribing to our free daily newsletter for more insights delivered straight to your inbox:
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Centralized clouds charge you up to 70% more than you need to pay for GPU compute.
Not because their chips cost more. Because you're also paying for their electricity, cooling headaches, software bundles, and egress fees.
We broke down every option for GPU compute with the actual numbers.
Full guide:
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The future of AI depends on who controls compute.
@ionet CEO @Gaurav_ionet is joining leaders from @nosana_ai, @akashnet_, and @AethirCloud for a live panel on one of the biggest questions in AI infrastructure right now.
They'll tackle centralized vs decentralized compute, and what it means for builders, startups, enterprises, and autonomous agents.
GPU costs are rising, access is tightening, and the stakes couldn't be higher.
Sign up below 👇
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The question every AI builder is asking: centralized or decentralized compute?
We’re bringing together experts from @nosana_ai, @akashnet, @AethirCloud, and @ionet to dig into it live.
📆 June 2, 5PM CET
Register:
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It sounds like the beginning of a joke.
Gandalf and the Pope walk into an AI conference...
Except it isn't a joke.
The concentration of power among AI companies has become such a threat that even the Pope felt he needed to comment on it.
And use a quote from Lord of the Rings to do it.
But the problem isn't AI. It's that too few people control it.
AI needs to move beyond @OpenAI , @AnthropicAI , @Google, and the like to become a force for the many, not the few.
How do we get there? Affordable compute. Instant access. Open-source models.
That's @ionet
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It's only May, and @Uber has already burned through its entire AI budget for the year.
With costs as high as $2,000 per engineer per month, it's easy to understand why.
And they aren't alone.
Less than 1% of executives report 20%+ ROI from AI.
Overpriced infrastructure, soaring token usage, and runaway costs make AI almost unaffordable, even for the largest companies.
But you don't need an enterprise budget to build with AI. You need affordable compute and leading open-source models.
That's exactly what @ionet delivers.
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