NodeShift integrated the Akash Supercloud to power enterprise AI development with high-density GPUs — including NVIDIA H200, H100, and A100.
Benefits for developers deploying AI models and agents on NodeShift with Akash compute:
• On-demand GPU access
• Significantly reduced costs
• Enterprise-grade scaling
Learn how @nodeshiftai integrated Akash in the case study linked below.
A leading Node-as-a-Service provider @NodeOpsHQ leveraged NodeShift’s compute platform to scale, improve reliability, and reduce costs, enabling expansion into the DePIN Cloud Compute market. Here’s how it worked 🧵👇
1/ AI is evolving faster than ever, but @manusai has captivated industry leaders for a very specific reason. It doesn’t just generate, it autonomously manages a swarm of task-optimized sub-agents with minimal prompting. This is a new chapter in human efficiency.
6/ Even better: No API key is required. Using Ollama models, you can install and run OpenManus locally using @nodeshiftai - this levels the playing field for developers, researchers, or anyone curious about AI autonomy
We’ve teamed up with @storj at @nodeshiftai, and now our users can store and run AI workloads without all the headaches that come with centralized cloud providers. It’s pretty awesome: you get S3-compatible decentralized storage, GPU, and compute instances hooked up directly, better performance, and lower costs. Honestly, I’m excited to see what developers and businesses do with this.
NodeShift has integrated with @storj to bring decentralized hot storage + scalable compute to AI & LLM deployments!
Now you can seamlessly connect Storj's S3-compatible storage with NodeShift's GPU/compute infrastructure, enabling:
👉 Cost-effective, secure AI/ML workloads
👉 High-performance decentralized storage
👉 Scalable compute + storage for data-heavy applications
Early adopters, including hospitals & geospatial firms, are already leveraging this for mission-critical AI & analytics.
🔗 https://t.co/qj3pfeXDV8
When we started building @nodeshiftai, it was clear to me: you can’t trust AI infrastructure if you can’t verify what’s running. That’s why we went with TEEs. They ensure private, tamper-proof AI execution critical for what we’re building. If you’re working with AI and still ignoring this, now’s the time to catch up.
1/ You know that feeling when you enter your credit card on a shady website and think, "Can I trust you with this?". That’s how most AI & cloud computing works today. Once you hand over your data, you have no control over where it goes or who sees it. You hope your data is safe but you have zero proof.
That’s where TEEs aka Trusted Execution Environments come in. So what exactly are these? 🧵
Everyone worries about AI replacing jobs, but barely anyone worries about AI quietly storing and repurposing their dataDumping sensitive company info into a black-box model >> bad OpSec
Using AI for confidential reports without knowing where the data goes >> risky at bestPrivacy and security aren’t optional. If you don’t control the compute, you don’t control the data, easy as that.
You. Yes, you. The one using ChatGPT, DeepSeek, and all the latest AI tools every day. Do you ever stop to think about what companies are doing with your data?
Every prompt you type, every voice command you give, every interaction you have with AI - it’s all being logged, stored, and fed into models you don’t own and can’t control.
Big Tech AI isn’t just "learning”, it’s extracting. Your data fuels billion-dollar models, while you get… what? A slightly better chatbot?
Meanwhile:
> You have no idea where your data actually goes.
> You don’t know who has access to it.
> You definitely don’t profit from it.
AI should be sovereign, not surveilled. The future belongs to open models, private compute, and user-owned AI, not black-box algorithms hoarding your information.
If data is the new oil, why are you still giving yours away for free?
Excited to announce our partnership with @akashnet , created by Overclock Labs. Their decentralized and cost-effective approach to cloud infrastructure perfectly aligns with our mission. After connecting with @gregosuri, it was clear we share the same vision for decentralization and innovation.
We're announcing a major step forward for AI infrastructure! NodeShift has integrated @akashnet decentralized GPU marketplace.
This means instant access to H200, H100, and A100 GPUs, no blockchain knowledge needed. Here’s why this is so exciting 🧵👇
NodeShift has just unlocked a major milestone 🚀 We've joined @hub71ad#Cohort16, receiving investment and strategic backing from @Mubadala, one of the world’s largest sovereign wealth funds.
This means more decentralized AI infrastructure, lower costs for builders, and easier access to secure, scalable AI tools. AI should be affordable, private, and accessible - and with one-click deployment, we’re making that happen.
Selected from over 1,300 applicants, joining Hub71 gives us access to capital, mentorship, and market opportunities in the UAE and beyond. A step closer to becoming the ultimate decentralized AI layer—and we’re just getting started.
Full details 👉 https://t.co/etHBPhFBe6
@deepseek_ai has launched a brand-new model, and we're taking our LLM game to the next level!
DeepSeek-VL2, is a state-of-the-art vision-language model designed to tackle complex multimodal tasks such as visual question answering, OCR, document analysis, and object localization. Built on an advanced Mixture-of-Experts (MoE) architecture, DeepSeek-VL2 offers:
#deepseek #OpenSourceAI #ArtificialIntelligence #LLM