io.net Re-poster
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io.net Re-poster
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The intelligent stack for powering AI workloads | https://t.co/hIYFLxle8l: decentralized GPUs | io.intelligence: inference & agents | https://t.co/EinR91I0wl
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Voir la traduction
2 million new GPUs.
That's the deal @awscloud and @nvidia announced this week.
It's also the problem.
That compute will get reserved before it's even built by whoever can write the biggest check.
And growing projects, indie builders, small labs, and researchers will still have to wait in line.
GPU capacity exists today, sitting idle, that doesn't require billions to build or buy.
@ionet is unlocking it.
2 million new GPUs.
That's the deal @awscloud and @nvidia announced this week.
It's also the problem.
That compute will get reserved before it's even built by whoever can write the biggest check.
And growing projects, indie builders, small labs, and researchers will still have to wait in line.
GPU capacity exists today, sitting idle, that doesn't require billions to build or buy.
@ionet is unlocking it.
That's the deal @awscloud and @nvidia announced this week.
It's also the problem.
That compute will get reserved before it's even built by whoever can write the biggest check.
And growing projects, indie builders, small labs, and researchers will still have to wait in line.
GPU capacity exists today, sitting idle, that doesn't require billions to build or buy.
@ionet is unlocking it.
69
Voir la traduction
2 million burned.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
2 million burned.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
40
Voir la traduction
2 million solana:BZLbGTNCSFfoth2GYDtwr7e4imWzpR5jqcUuGEwr646K burned.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
2 million solana:BZLbGTNCSFfoth2GYDtwr7e4imWzpR5jqcUuGEwr646K burned.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
Gone for good.
Because of real utility and real value flowing through the network.
Usage creates value. Value reduces supply.
The flywheel is turning. Powered by the IDE.
48
Voir la traduction
The cheapest GPU isn't always the cheapest GPU.
The GPU market has fragmented across hyperscalers, neoclouds, and decentralized networks.
A cheap hourly rate can quickly become expensive when you factor in SLAs, security requirements, data residency, and the cost of getting out.
The hourly rate is just the number on the pricing page.
The real cost of compute is everything underneath it.
The cheapest GPU isn't always the cheapest GPU.
The GPU market has fragmented across hyperscalers, neoclouds, and decentralized networks.
A cheap hourly rate can quickly become expensive when you factor in SLAs, security requirements, data residency, and the cost of getting out.
The hourly rate is just the number on the pricing page.
The real cost of compute is everything underneath it.
The GPU market has fragmented across hyperscalers, neoclouds, and decentralized networks.
A cheap hourly rate can quickly become expensive when you factor in SLAs, security requirements, data residency, and the cost of getting out.
The hourly rate is just the number on the pricing page.
The real cost of compute is everything underneath it.
62
Voir la traduction
Daily token usage in China went from 100B to 140T in 14 months.
Beijing's response:
- 100,000 GPUs
- $532B in infrastructure investment
- 9,800 EFLOPS by 2030
The AI race is a compute race. Access to GPUs will define who competes.
Governments and hyperscalers are solving this by building.
http://io.net is solving it by unlocking the underutilized compute that already exists.
Building takes years. Unlocking takes minutes.
Daily token usage in China went from 100B to 140T in 14 months.
Beijing's response:
- 100,000 GPUs
- $532B in infrastructure investment
- 9,800 EFLOPS by 2030
The AI race is a compute race. Access to GPUs will define who competes.
Governments and hyperscalers are solving this by building.
http://io.net is solving it by unlocking the underutilized compute that already exists.
Building takes years. Unlocking takes minutes.
Beijing's response:
- 100,000 GPUs
- $532B in infrastructure investment
- 9,800 EFLOPS by 2030
The AI race is a compute race. Access to GPUs will define who competes.
Governments and hyperscalers are solving this by building.
http://io.net is solving it by unlocking the underutilized compute that already exists.
Building takes years. Unlocking takes minutes.
59
Voir la traduction
38GW.
That's the US data center power shortfall through 2028.
The hyperscaler solution is to build more data centers, substations, and power generation.
Then wait 4-7 years for it.
Meanwhile, millions of GPUs are already powered, cooled, connected, and underutilized.
AI doesn't just need more infrastructure. It needs better access to the infrastructure that already exists.
That's what http://io.net was built for.
38GW.
That's the US data center power shortfall through 2028.
The hyperscaler solution is to build more data centers, substations, and power generation.
Then wait 4-7 years for it.
Meanwhile, millions of GPUs are already powered, cooled, connected, and underutilized.
AI doesn't just need more infrastructure. It needs better access to the infrastructure that already exists.
That's what http://io.net was built for.
That's the US data center power shortfall through 2028.
The hyperscaler solution is to build more data centers, substations, and power generation.
Then wait 4-7 years for it.
Meanwhile, millions of GPUs are already powered, cooled, connected, and underutilized.
AI doesn't just need more infrastructure. It needs better access to the infrastructure that already exists.
That's what http://io.net was built for.
53
Voir la traduction
OpenAI's chief scientist just called for "extreme caution" on AI's pace.
Right after shipping their most powerful model yet.
And amid reports of @OpenAI agents carrying out real-world cyber-attacks, unprompted.
Their proposed fix is to build more AI agents to keep pace with AI.
There's a gap in that logic.
The better path isn't more centralized control. It's more transparency, more accessibility, more distributed oversight.
A safer future for AI won't come from massive corporations policing themselves.
https://www.bbc.co.uk/news/articles/cwyzrrd0kp7o
OpenAI's chief scientist just called for "extreme caution" on AI's pace.
Right after shipping their most powerful model yet.
And amid reports of @OpenAI agents carrying out real-world cyber-attacks, unprompted.
Their proposed fix is to build more AI agents to keep pace with AI.
There's a gap in that logic.
The better path isn't more centralized control. It's more transparency, more accessibility, more distributed oversight.
A safer future for AI won't come from massive corporations policing themselves.
https://www.bbc.co.uk/news/articles/cwyzrrd0kp7o
Right after shipping their most powerful model yet.
And amid reports of @OpenAI agents carrying out real-world cyber-attacks, unprompted.
Their proposed fix is to build more AI agents to keep pace with AI.
There's a gap in that logic.
The better path isn't more centralized control. It's more transparency, more accessibility, more distributed oversight.
A safer future for AI won't come from massive corporations policing themselves.
https://www.bbc.co.uk/news/articles/cwyzrrd0kp7o
41
Partiellement vrai
Voir la traduction
8 billion tokens.
That's what ran through @ionet models on @OpenRouter.
In a single day.
This isn't a benchmark or a demo. It's real inference and real demand running on affordable and accessible infrastructure.
This is what AI for the many, not the few, looks like.
8 billion tokens.
That's what ran through @ionet models on @OpenRouter.
In a single day.
This isn't a benchmark or a demo. It's real inference and real demand running on affordable and accessible infrastructure.
This is what AI for the many, not the few, looks like.
That's what ran through @ionet models on @OpenRouter.
In a single day.
This isn't a benchmark or a demo. It's real inference and real demand running on affordable and accessible infrastructure.
This is what AI for the many, not the few, looks like.
73
Voir la traduction
Not all decentralized compute is built the same.
@akashnet pioneered the space for CPU and containerized workloads.
@ionet was built for GPU-intensive AI, with native multi-GPU clusters, bare-metal performance, and deployment in under 2 minutes.
The result is enterprise-grade AI infra at a fraction of hyperscaler cost, without the multi-year lock-in.
Same decentralized compute category, different jobs.
Check out the full comparison.
https://io.net/blog/io-net-vs-akash-network-comparing-gpu-cloud-pricing-and-features
Not all decentralized compute is built the same.
@akashnet pioneered the space for CPU and containerized workloads.
@ionet was built for GPU-intensive AI, with native multi-GPU clusters, bare-metal performance, and deployment in under 2 minutes.
The result is enterprise-grade AI infra at a fraction of hyperscaler cost, without the multi-year lock-in.
Same decentralized compute category, different jobs.
Check out the full comparison.
https://io.net/blog/io-net-vs-akash-network-comparing-gpu-cloud-pricing-and-features
@akashnet pioneered the space for CPU and containerized workloads.
@ionet was built for GPU-intensive AI, with native multi-GPU clusters, bare-metal performance, and deployment in under 2 minutes.
The result is enterprise-grade AI infra at a fraction of hyperscaler cost, without the multi-year lock-in.
Same decentralized compute category, different jobs.
Check out the full comparison.
https://io.net/blog/io-net-vs-akash-network-comparing-gpu-cloud-pricing-and-features
83
Voir la traduction
$500B says GPUs are the new gold.
But gold you can buy on any exchange.
Not GPUs.
4 hyperscalers have next-gen capacity locked through 2027, and the rest of the market is left renting from landlords.
The real AI bottleneck is access.
Our Chief Growth Officer @jack_ionet breaks it down.
https://yellow.com/opinion/the-compute-economy-needs-a-trader-joes-not-another-landlord
$500B says GPUs are the new gold.
But gold you can buy on any exchange.
Not GPUs.
4 hyperscalers have next-gen capacity locked through 2027, and the rest of the market is left renting from landlords.
The real AI bottleneck is access.
Our Chief Growth Officer @jack_ionet breaks it down.
https://yellow.com/opinion/the-compute-economy-needs-a-trader-joes-not-another-landlord
But gold you can buy on any exchange.
Not GPUs.
4 hyperscalers have next-gen capacity locked through 2027, and the rest of the market is left renting from landlords.
The real AI bottleneck is access.
Our Chief Growth Officer @jack_ionet breaks it down.
https://yellow.com/opinion/the-compute-economy-needs-a-trader-joes-not-another-landlord
100 000+ GPUs.
C’est le nombre de GPUs nécessaires pour entraîner GPT-6 Astra d’@OpenAI.
Pendant ce temps, la plupart des développeurs ont du mal à accéder à quelques GPUs à peine, ou à se les offrir.
C’est là le cœur du problème en IA aujourd’hui.
D’un côté, des clusters disposant de calculs pratiquement illimités.
De l’autre, des startups, des chercheurs et des développeurs qui se battent pour l’accès et qui comptent chaque heure de GPU.
Ajouter simplement plus de GPUs ne suffira pas à résoudre ça.
Nous avons besoin d’une meilleure façon d’accéder au calcul déjà disponible et de l’exploiter.
C’est exactement le rôle de @ionet.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
100 000+ GPUs.
C’est le nombre de GPUs nécessaires pour entraîner GPT-6 Astra d’@OpenAI.
Pendant ce temps, la plupart des développeurs ont du mal à accéder à quelques GPUs à peine, ou à se les offrir.
C’est là le cœur du problème en IA aujourd’hui.
D’un côté, des clusters disposant de calculs pratiquement illimités.
De l’autre, des startups, des chercheurs et des développeurs qui se battent pour l’accès et qui comptent chaque heure de GPU.
Ajouter simplement plus de GPUs ne suffira pas à résoudre ça.
Nous avons besoin d’une meilleure façon d’accéder au calcul déjà disponible et de l’exploiter.
C’est exactement le rôle de @ionet.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
C’est le nombre de GPUs nécessaires pour entraîner GPT-6 Astra d’@OpenAI.
Pendant ce temps, la plupart des développeurs ont du mal à accéder à quelques GPUs à peine, ou à se les offrir.
C’est là le cœur du problème en IA aujourd’hui.
D’un côté, des clusters disposant de calculs pratiquement illimités.
De l’autre, des startups, des chercheurs et des développeurs qui se battent pour l’accès et qui comptent chaque heure de GPU.
Ajouter simplement plus de GPUs ne suffira pas à résoudre ça.
Nous avons besoin d’une meilleure façon d’accéder au calcul déjà disponible et de l’exploiter.
C’est exactement le rôle de @ionet.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
49
Voir la traduction
100,000+ GPUs.
That's what it took to train @OpenAI's GPT-6 Astra.
Meanwhile, most devs struggle to access or afford even a handful of GPUs.
That's the big problem in AI today.
At one end, clusters with effectively unlimited compute.
At the other, startups, researchers, and developers fighting for access and watching every GPU hour.
Simply adding more GPUs won't fix that.
We need a better way to access and utilize the compute that already exists.
That's what @ionet is for.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
100,000+ GPUs.
That's what it took to train @OpenAI's GPT-6 Astra.
Meanwhile, most devs struggle to access or afford even a handful of GPUs.
That's the big problem in AI today.
At one end, clusters with effectively unlimited compute.
At the other, startups, researchers, and developers fighting for access and watching every GPU hour.
Simply adding more GPUs won't fix that.
We need a better way to access and utilize the compute that already exists.
That's what @ionet is for.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
That's what it took to train @OpenAI's GPT-6 Astra.
Meanwhile, most devs struggle to access or afford even a handful of GPUs.
That's the big problem in AI today.
At one end, clusters with effectively unlimited compute.
At the other, startups, researchers, and developers fighting for access and watching every GPU hour.
Simply adding more GPUs won't fix that.
We need a better way to access and utilize the compute that already exists.
That's what @ionet is for.
https://www.cnet.com/tech/services-and-software/openai-gpt-6-astra-release-ai-agi-chatgpt/
93
Voir la traduction
The UK's chief AI adviser was just hired by @AnthropicAI.
But he's staying on as chair of the government body that funds AI research.
Same person playing both sides.
The people writing AI policy are now employed by the labs that policy is supposed to check.
Compute concentration is a problem for GPUs and for governance.
https://www.theguardian.com/technology/2026/sep/02/architect-of-uks-ai-strategy-joins-anthropic
The UK's chief AI adviser was just hired by @AnthropicAI.
But he's staying on as chair of the government body that funds AI research.
Same person playing both sides.
The people writing AI policy are now employed by the labs that policy is supposed to check.
Compute concentration is a problem for GPUs and for governance.
https://www.theguardian.com/technology/2026/sep/02/architect-of-uks-ai-strategy-joins-anthropic
But he's staying on as chair of the government body that funds AI research.
Same person playing both sides.
The people writing AI policy are now employed by the labs that policy is supposed to check.
Compute concentration is a problem for GPUs and for governance.
https://www.theguardian.com/technology/2026/sep/02/architect-of-uks-ai-strategy-joins-anthropic
58
Le cloud computing est facile à adopter.
C’est en sortir que cela devient coûteux.
Les frais de sortie, les outils propriétaires, les engagements à long terme, ainsi que la reconstruction et la revalidation des charges de travail peuvent coûter des dizaines de milliers de dollars.
L’enfermement chez un fournisseur est la taxe cachée de l’infrastructure IA.
L’alternative n’est pas un autre jardin fermé.
C’est le calcul ouvert.
Le cloud computing est facile à adopter.
C’est en sortir que cela devient coûteux.
Les frais de sortie, les outils propriétaires, les engagements à long terme, ainsi que la reconstruction et la revalidation des charges de travail peuvent coûter des dizaines de milliers de dollars.
L’enfermement chez un fournisseur est la taxe cachée de l’infrastructure IA.
L’alternative n’est pas un autre jardin fermé.
C’est le calcul ouvert.
C’est en sortir que cela devient coûteux.
Les frais de sortie, les outils propriétaires, les engagements à long terme, ainsi que la reconstruction et la revalidation des charges de travail peuvent coûter des dizaines de milliers de dollars.
L’enfermement chez un fournisseur est la taxe cachée de l’infrastructure IA.
L’alternative n’est pas un autre jardin fermé.
C’est le calcul ouvert.
81
Voir la traduction
NVIDIA knows there’s a GPU access problem.
But its solution won’t fix it.
Giving startups compute in exchange for a cut of their future revenue just creates another gatekeeper AI doesn’t need.
AI needs a market where nobody needs permission to build.
@ionet CEO @Gaurav_ionet explains why the next layer of AI infrastructure must be built around access.
NVIDIA knows there’s a GPU access problem.
But its solution won’t fix it.
Giving startups compute in exchange for a cut of their future revenue just creates another gatekeeper AI doesn’t need.
AI needs a market where nobody needs permission to build.
@ionet CEO @Gaurav_ionet explains why the next layer of AI infrastructure must be built around access.
But its solution won’t fix it.
Giving startups compute in exchange for a cut of their future revenue just creates another gatekeeper AI doesn’t need.
AI needs a market where nobody needs permission to build.
@ionet CEO @Gaurav_ionet explains why the next layer of AI infrastructure must be built around access.
67
Voir la traduction
$31.6 trillion.
That's what the world will spend on data centres by 2050 to keep up with AI demand.
And it's more than the combined GDP of 174 countries.
The current model of build, borrow, repeat isn't sustainable. It prices out most of the world's developers. And it harms the communities these data centers get built on top of.
A future where AI is accessible to the many, not the few, needs a new way of building.
That's exactly what http://io.net is doing.
$31.6 trillion.
That's what the world will spend on data centres by 2050 to keep up with AI demand.
And it's more than the combined GDP of 174 countries.
The current model of build, borrow, repeat isn't sustainable. It prices out most of the world's developers. And it harms the communities these data centers get built on top of.
A future where AI is accessible to the many, not the few, needs a new way of building.
That's exactly what http://io.net is doing.
That's what the world will spend on data centres by 2050 to keep up with AI demand.
And it's more than the combined GDP of 174 countries.
The current model of build, borrow, repeat isn't sustainable. It prices out most of the world's developers. And it harms the communities these data centers get built on top of.
A future where AI is accessible to the many, not the few, needs a new way of building.
That's exactly what http://io.net is doing.
80
Voir la traduction
You don't need a GPU.
You need multiple GPUs that talk to each other like they're in the same rack.
That's the difference between a marketplace and actual AI infrastructure.
Access is one thing.
Orchestration is another.
And for serious AI workloads, orchestration is everything.
@ionet was built to orchestrate.
You don't need a GPU.
You need multiple GPUs that talk to each other like they're in the same rack.
That's the difference between a marketplace and actual AI infrastructure.
Access is one thing.
Orchestration is another.
And for serious AI workloads, orchestration is everything.
@ionet was built to orchestrate.
You need multiple GPUs that talk to each other like they're in the same rack.
That's the difference between a marketplace and actual AI infrastructure.
Access is one thing.
Orchestration is another.
And for serious AI workloads, orchestration is everything.
@ionet was built to orchestrate.
74
33 millions+ d’heures de calcul servies sur http://io.net.
Un réseau en direct, qui effectue le travail pour lequel les hyperscalers et les néoclouds facturent une prime.
Chaque heure de plus est une preuve. Vous n’avez pas besoin d’une infrastructure coûteuse et fermée pour exécuter des charges de travail IA sérieuses.
Le calcul décentralisé n’est pas un récit. C’est l’avenir de l’infrastructure IA.
33 millions+ d’heures de calcul servies sur http://io.net.
Un réseau en direct, qui effectue le travail pour lequel les hyperscalers et les néoclouds facturent une prime.
Chaque heure de plus est une preuve. Vous n’avez pas besoin d’une infrastructure coûteuse et fermée pour exécuter des charges de travail IA sérieuses.
Le calcul décentralisé n’est pas un récit. C’est l’avenir de l’infrastructure IA.
Un réseau en direct, qui effectue le travail pour lequel les hyperscalers et les néoclouds facturent une prime.
Chaque heure de plus est une preuve. Vous n’avez pas besoin d’une infrastructure coûteuse et fermée pour exécuter des charges de travail IA sérieuses.
Le calcul décentralisé n’est pas un récit. C’est l’avenir de l’infrastructure IA.
93
Certaines choses font exactement ce qu’elles disent.
Render Network est excellent dans ce pour quoi il a été conçu : le rendu.
Mais les charges de travail liées à l’IA demandent autre chose.
Inférence persistante. Entraînement multi-nœuds. Des clusters qui évoluent en quelques secondes.
C’est de l’orchestration, pas seulement de l’accès GPU.
Et c’est précisément pour cela qu’http://io.net a été conçu dès le premier jour.
https://io.net/blog/io-vs-render-and-alternatives-comparing-gpu-cloud-pricing-and-features
Certaines choses font exactement ce qu’elles disent.
Render Network est excellent dans ce pour quoi il a été conçu : le rendu.
Mais les charges de travail liées à l’IA demandent autre chose.
Inférence persistante. Entraînement multi-nœuds. Des clusters qui évoluent en quelques secondes.
C’est de l’orchestration, pas seulement de l’accès GPU.
Et c’est précisément pour cela qu’http://io.net a été conçu dès le premier jour.
https://io.net/blog/io-vs-render-and-alternatives-comparing-gpu-cloud-pricing-and-features
Render Network est excellent dans ce pour quoi il a été conçu : le rendu.
Mais les charges de travail liées à l’IA demandent autre chose.
Inférence persistante. Entraînement multi-nœuds. Des clusters qui évoluent en quelques secondes.
C’est de l’orchestration, pas seulement de l’accès GPU.
Et c’est précisément pour cela qu’http://io.net a été conçu dès le premier jour.
https://io.net/blog/io-vs-render-and-alternatives-comparing-gpu-cloud-pricing-and-features
1
84
Nvidia fabrique les puces.
@Nvidia possède la pile logicielle.
À présent, il semblerait qu’elle veuille aussi @huggingface.
Une offre de 12,9 Md$ pour franchir la porte et ouvrir l’IA en open source.
Peut-être que les modèles resteront ouverts. Mais si le calcul, les outils et la distribution dépendent tous d’une seule entreprise, l’écosystème, lui, ne l’est pas.
L’avenir de l’IA a besoin d’une infrastructure ouverte. Pas d’une intégration verticale maquillée en quelque chose d’unique.
https://www.forbes.com/sites/siladityaray/2026/08/27/nvidia-has-reportedly-agreed-to-buy-ai-model-hosting-platform-hugging-face-for-13-billion/
Nvidia fabrique les puces.
@Nvidia possède la pile logicielle.
À présent, il semblerait qu’elle veuille aussi @huggingface.
Une offre de 12,9 Md$ pour franchir la porte et ouvrir l’IA en open source.
Peut-être que les modèles resteront ouverts. Mais si le calcul, les outils et la distribution dépendent tous d’une seule entreprise, l’écosystème, lui, ne l’est pas.
L’avenir de l’IA a besoin d’une infrastructure ouverte. Pas d’une intégration verticale maquillée en quelque chose d’unique.
https://www.forbes.com/sites/siladityaray/2026/08/27/nvidia-has-reportedly-agreed-to-buy-ai-model-hosting-platform-hugging-face-for-13-billion/
@Nvidia possède la pile logicielle.
À présent, il semblerait qu’elle veuille aussi @huggingface.
Une offre de 12,9 Md$ pour franchir la porte et ouvrir l’IA en open source.
Peut-être que les modèles resteront ouverts. Mais si le calcul, les outils et la distribution dépendent tous d’une seule entreprise, l’écosystème, lui, ne l’est pas.
L’avenir de l’IA a besoin d’une infrastructure ouverte. Pas d’une intégration verticale maquillée en quelque chose d’unique.
https://www.forbes.com/sites/siladityaray/2026/08/27/nvidia-has-reportedly-agreed-to-buy-ai-model-hosting-platform-hugging-face-for-13-billion/
72
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