The AITECH Cloud Network (ACN) continues its monthly ecosystem token burn initiative, with October marking another step in its ongoing token management framework.

As part of this process, 125,322 ACN tokens have been permanently removed from circulation.

But beyond the monthly figures, it’s important to understand what these burns mean for the ecosystem and how the community can independently verify them.

Why Does ACN Conduct Monthly Token Burns?

ACN’s monthly token burns are part of its long-term token management framework, systematically reducing the circulating supply while keeping the process transparent and verifiable on-chain.

How Can the Community Verify Each Burn?

One of the key features of ACN’s approach is that the community doesn’t have to rely solely on announcements.

Through the ACN Burn Terminal, users can review ecosystem burn activity and independently verify transactions on-chain.

This allows community members to follow the process themselves rather than simply taking reported figures at face value.

Looking Ahead

ACN will continue executing monthly ecosystem token burns in accordance with its token management framework.

As the ecosystem develops, maintaining accessible and verifiable records remains an important part of keeping the community informed about token supply management.

October Burn Details

• Tokens Burned: 125,322
• Burn Period: October 2026

SpaceX locked in $40B to finance Nvidia GPUs: SpaceX signed a deal on October 24, made public on October 26, to finance about $40B of Nvidia Vera Rubin GPUs. Apollo’s fund buys the chips and leases them to SpaceX. AI computing power is now financed like infrastructure. Google unveiled Gemini 4 Carbon, but only defenders get it first: On October 28, Google launched Gemini 4 Carbon, its strongest model for cybersecurity and long coding tasks. Google says it ranks #1 on the Artificial Analysis index. For now, only select cyber defenders can use it. The strongest AI is reaching defenders first. Anthropic files to go public: On October 26, Anthropic filed to list on Nasdaq under the ticker ANT. It reported about $16B in revenue for the first half of 2026. The share price and number of shares are not set yet. AI labs are now moving from private funding to public markets.

Mistral opens the weights of its trillion-parameter model: On October 29, Mistral made its Mistral Large 4 model open to download. It is built for cybersecurity and coding, and companies can run it on their own systems. Open means more control, but a model this big needs heavy hardware.

That’s all for this week’s AI drop. Follow @AITECHio

for more innovation updates!

Why We Share These Updates: We share these AI roundups to keep our community plugged into the breakthroughs shaping the AI landscape we’re building. ACN isn’t just following the news; we’re part of the AI sector, creating the infrastructure, tools, and ecosystem that will power the next wave of AI innovation.

VCs have entered the conversation.

Great news for the ACN ecosystem!

Vision Makers, has successfully onboarded 10 VCs, including several top-tier venture capital firms.

Before making their commitments, these VCs were given an exclusive look at the latest Vision Makers platform and its functionality. Their confidence in what’s being built has led them to invest at this early stage.

Here’s why this is even bigger for ACN:

Vision Makers is built entirely on and powered by ACN’s AI infrastructure.

As Vision Makers scales, it brings a new wave of users, adoption, and demand to the infrastructure powering it all.

The Vision Makers team is currently putting the finishing touches on its new public-facing platform ahead of release.

More details about the VCs backing Vision Makers and the total amount raised will be revealed very soon.

Exciting times ahead for the entire ACN ecosystem!

Shopify opens its billion-product catalog to any AI shopping agent: On October 13, Shopify launched Shop MCP, letting any compatible AI agent search its catalog of more than a billion products, build a cart, and complete a purchase after the shopper approves it, no individual partnership deal required. It’s a direct extension of the agent-commerce standards push we covered last week, moving from “agents can identify themselves to a business” to “agents can actually check out.”

CoreWeave’s bond yields spike toward 15%, reviving questions about debt-funded AI infrastructure: CoreWeave shares fell roughly 15–16% on October 14 after bond yields climbed near 15%, with credit-default-swap spreads widening to levels markets typically reserve for distressed debt. The company carries around $52 billion in debt and lease obligations built up to fund its GPU buildout. It’s a reminder that “we have the GPUs” and “we can profitably finance the GPUs” are two different claims, and credit markets are now pricing that gap.

Nvidia releases Nemotron 3.5, a new family of open models built for always-on agents: On October 15, Nvidia released Nemotron 3.5 Super VL, a 120-billion-parameter open model, alongside two smaller edge models, Bolt 74B and a lighter variant, aimed at specialized, always-on agent workloads rather than general chat.

Nvidia pledges $250M in compute over five years for UK agentic AI safety research: Also on October 13, Nvidia announced a five-year, $250 million compute donation to strengthen UK research into agentic AI safety and security. Paired with this week’s CoreWeave story, it’s a useful snapshot of where the money is actually flowing in AI infrastructure right now: into both scaling it and trying to keep it safe.

That’s all for this week’s AI drop. Follow @AITECHio

for more innovation updates!

Why We Share These Updates: We share these AI roundups to keep our community plugged into the breakthroughs shaping the AI landscape we’re building. ACN isn’t just following the news; we’re part of the AI sector, creating the infrastructure, tools, and ecosystem that will power the next wave of AI innovation.

AITECH Cloud Network, previously Solidus AI Tech, is a high-performance computing ecosystem powering a decentralized compute network. Its core audience: Web3 teams, decentralised AI builders, and enterprises looking for tokenised compute options.

Runpod is built for developers first: machine learning engineers, data scientists, AI production teams and runs as a centralised GPU cloud platform. That means containerised instances (Pods), serverless API deployments, and multi-node GPU clusters, all in one place.

Pricing Structure Breakdown

Understanding the pricing model of each GPU cloud provider ensures predictable operational costs and prevents unexpected budget overruns.

• AITECH Cloud Network (Tokenised & Web3 Model): Offers hybrid Web2/Web3 billing where payments made using native $ACN tokens unlock platform discounts alongside automatic token-burn mechanics.
• Runpod (Per-Second & Serverless Billing): Uses precise per-second billing for container Pods alongside scale-to-zero serverless API endpoints that charge strictly during active execution time.
• Cost Optimisations: AITECH Cloud Network reduces costs through eco-friendly HPC data centres and distributed node networks, while Runpod eliminates internet egress fees and offers lower-tier Community Cloud nodes.

Deployment Flexibility and Ease of Use

Evaluating deployment flexibility and ease of use helps determine how quickly developer teams can iterate, manage containers, and scale AI workloads.

• Runpod (One-Click Setup): Launches instances instantly with standard Docker templates, web SSH, and integrated Jupyter Notebooks.
• Runpod (Serverless Endpoints): Provides API-driven container deployments with zero-infrastructure management and rapid auto-scaling.
• Runpod (DevOps Simplicity): Focuses on standard Web2 developer tooling, intuitive dashboard controls, and frictionless onboarding.
• AITECH Cloud Network (Agent Forge): Features no-code/low-code Agent Forge tools for rapidly building and orchestrating AI workflows.
• AITECH Cloud Network (Compute Marketplace): Offers a decentralised portal to rent, monitor, and manage GPU resources using Web3 auth or web portals.
• AITECH Cloud Network (Developer Integration): Delivers flexible API endpoints tailored for Web3-native platforms, smart contract interactions, and custom dApps.

Ultimately, Runpod provides a seamless turnkey experience for standard containerised AI pipelines, whereas AITECH Cloud Network excels for teams integrating Web3 workflows and automated agent orchestration.

Final Verdict: Which Platform Fits Your Needs?

AITECH Cloud Network and Runpod each hold their own against the traditional cloud giants when it comes to GPU performance; the real difference lies in who each one is built for.

• Choose Runpod if you want standard developer tools, instant PyTorch/Jupyter container launches, auto-scaling serverless API endpoints, and traditional per-second Web2 billing.
• Choose AITECH Cloud Network if you prioritise Web3-native workflows, all around the world, automated AI agent building, and payment flexibility through tokenised discounts.

Your final decision comes down to your operational environment: select Runpod for rapid, containerised machine learning production, or choose AITECH Cloud Network for decentralised compute integration and token-backed infrastructure.

Conclusion

Both AITECH Cloud Network and Runpod hold their own against traditional cloud hyperscalers, sidestepping the heavy overheads and bandwidth markups that typically come with scale. So which one’s right for you? It really depends on your integration priorities. If you’re running a standard ML pipeline and need serverless endpoints with fast container launches, Runpod is the more natural choice. If your work leans toward eco-friendly European infrastructure, automated AI agent building, or Web3 payment ecosystems, AITECH Cloud Network is the stronger fit.

Training advanced AI models and using them requires a lot of computing power. When you need to rent a GPU, cloud services let you rent it easily without having to pay a lot of money upfront to buy expensive equipment like an H100. This on-demand method reduces the costs of maintaining equipment, removes concerns about equipment losing value, and provides you with quick access to the latest graphics cards that are perfect for your project.

Why Teams Rent GPUs Instead of Buying

Creating and training big deep learning systems needs special computer equipment that is hard to justify buying forever.

• Massive Upfront Savings: Investing in high-end equipment such as the H100 costs more than $25,000, whereas affordable consumer models such as the RTX 4090 cost $1,600 without considering additional servers and uninterrupted power supplies.
• Elastic Scalability: AI GPU servers can be quickly set up for big tasks and can be scaled down to nothing right after the job is done.
• Eliminating Depreciation: Hardware generations cycle quickly, so graphics cards we own can become outdated in just one to two years. Renting lets you tap into cutting-edge architectures without holding depreciating physical assets.
• Zero Infrastructure Overhead: Skip procurement wait times, driver troubleshooting headaches, and complex data-center maintenance routines.

How to Rent a GPU for AI: Step-by-Step

When you are ready to rent a GPU for AI, having a process ensures that you will not have misconfigured instances and unnecessary costs. Follow this efficient process when renting a GPU for AI without overspending:

1. Assess your workload: See the size of your model, number of parameters, and expected batch sizes. Calculate your required memory before picking an instance.
2. Choose your GPU tier: Use normal graphics cards (RTX series) for easier testing and work, while selecting strong data center GPUs (A100 or H100 cluster) for massive training.
3. Configure the instance: Select the number of CPU cores, memory (try to get twice as much as video memory), and fast NVMe SSD drives.
4. Launch and connect: Access your instance safely through SSH, JupyterLab, or VS Code Remote Container Terminal.
5. Optimize runtime: Turn on mixed precision (FP16/BF16) and watch your live hardware usage with nvidia-smi to make sure that your GPUs aren’t sitting idle.
6. Stop when done: Since GPU rental platforms are charged per-minute or per-hour usage, you’ll blow through your budget quickly if you forget to shut down.

Billing Models Explained

Understanding how cloud GPU rental providers structure their invoices lets you shave significant overhead off your monthly compute budget.

• Per hour: Simple and perfect for quick scripts, brief experiments, or short debugging cycles.
• Per day: Usually 15%-20% cheaper than regular hourly rates, making it a great option for training sessions lasting over 8 hours.
• Per week or month: Offers discounts of 30% to 50%, designed for regular work and large-scale model improvements.
• Spot or preemptible instances: Cuts costs by 50% to 80% on costs, but you need to make sure that your machine learning process can handle mistakes and that you save your work regularly in case the computer gets taken away.

GPU Rental by Use Case

Different AI architectures place completely different demands on hardware. Knowing how to rent a GPU for machine learning tasks will enable you to save money and achieve good results.

• Stable Diffusion / Image Generation: Requires either an RTX 3090 or 4090 graphics card with 12GB to 24GB of RAM. It is useful for better management of high-quality image processing.
• LLM Fine-Tuning (7B to 13B parameters): Performs best on RTX 4090 or A100 40GB. It uses LoRA or QLoRA methods to reduce memory usage significantly.
• LLM Training (70B+ parameters): Demands multi-GPU A100 80GB or H100 setups wired with NVLink, alongside distributed frameworks like DeepSpeed or FSDP.
• Computer Vision Training: Depends a lot on RTX A5000 or RTX 4090 computers that use a mix of different types of calculations to handle large groups of visual data.
• Inference / Model Serving: Performs well on cheaper graphics cards such as the RTX 2080 Ti and 3080 with a process referred to as INT8 quantization and applications like vLLM.

Common Mistakes to Avoid

Renting a GPU for the first time has some hidden costs. Here are the mistakes that you should avoid making when renting a GPU:

• Renting oversized hardware: Always test your script locally or on an entry tier before allocating an expensive enterprise cluster. Scale up only if GPU utilization sits consistently above 90%.
• Leaving instances active overnight: Set up strict billing alerts and automated stop scripts. Unattended containers rack up silent charges instantly.
• Ignoring mixed precision: Using full FP32 format instead of FP16 or BF16 slows down computations by half on new tensor cores.
• Failing to save checkpoints: Large-scale training tasks are ultimately going to crash or time out. Always save your model weights every epoch or after some fixed number of steps.
• Ignoring storage bottlenecks: A slow data loading process hampers your graphics card. Always ensure the use of fast NVMe disks, prefetchers, and caching of your data to increase GPU efficiency.

Conclusion

Knowledge of how to rent GPUs on the cloud makes it possible for you to effectively scale up your AI tasks without having to pay huge upfront costs for purchasing infrastructure. Matching your model and billing parameters to cloud service providers ensures efficient performance and cost savings.

OpenAI unveils “dots,” its first always-on AI agent, at DevDay 2026: At its September 29 DevDay keynote, OpenAI introduced dots, a new category of “always-on” AI agents, alongside GPT-6.1 Sol, a model update released just a week after its predecessor. CFO Sarah Friar also addressed the company’s approach to pacing frontier model releases against safety, and CEO Sam Altman reiterated that OpenAI eventually wants to go public, though not yet.

Nvidia launches an open AI agent safety platform with 100+ partners: Nvidia and more than 100 partner companies launched an open platform built around runtime sandboxing and hardware-level monitoring, designed to rapidly quarantine AI agents that start behaving unexpectedly. Altman, asked about it at DevDay the same day, called it a “good thing,” but not a full solution on its own.

Restate raises $20M to build durable infrastructure for AI agent workflows: Restate closed a $20 million Series A on October 1, led by Singular with participation from other venture firms, to expand its platform for durable, fault-tolerant AI agent workflows. It’s one more signal that the market sees workflow reliability, not just model quality, as the next thing worth funding.

OpenAI reportedly targets $30B in new funding at a $1.4 trillion valuation: OpenAI is seeking at least $30 billion from investors at a roughly $1.4 trillion valuation, Bloomberg reported on September 29, after pushing back its IPO plans. If it closes, the valuation would put OpenAI back above Anthropic’s most recent private market mark.

That’s all for this week’s AI drop. Follow @AITECHio

for more innovation updates!

Why We Share These Updates: We share these AI roundups to keep our community plugged into the breakthroughs shaping the AI landscape we’re building in. ACN isn’t just following the news; we’re part of the AI sector, creating the infrastructure, tools, and ecosystem that will power the next wave of AI innovation.

Another week of steady progress across the AITECH Cloud Network ecosystem, with continued development across the platform and another milestone reached in ACN staking.

Strengthening the Core Infrastructure

This week, the development team continued focusing on strengthening the core of the AITECH Cloud Network platform.

Work centered around stability improvements, runtime optimizations, and broader performance enhancements designed to keep the infrastructure reliable as platform activity and usage continue to grow.

These improvements may happen largely in the background, but they remain an important part of preparing the ecosystem for greater scale across ACN products and infrastructure.

As more users, developers, AI agents, and partners interact with the network, maintaining a stable and efficient foundation remains a key priority.

ACN Staking Crosses 255M+

Alongside this week’s development work, ACN reached another staking milestone.

More than 255 million ACN is now staked and locked on-chain, increasing from the 250 million+ ACN reported in our previous update.

The continued growth in staking represents increasing participation from holders choosing to lock their ACN within the ecosystem.

Every new milestone further strengthens the level of long-term participation surrounding the network as the wider ACN ecosystem continues to develop.

Continuing to Build

Progress across AITECH Cloud Network is not defined by individual updates alone, but by the continuous development taking place across the infrastructure, products, and ecosystem.

From strengthening platform performance to expanding staking participation, each week contributes another layer to the network we are building.

A huge thank you to our community, partners, builders, and everyone continuing to stake and participate in the ecosystem.

255M+ ACN staked and locked on-chain — and we keep building.

Keep building. Keep exploring.

Verda raised $189M to expand AI cloud infrastructure: Finnish AI cloud startup Verda raised $189 million on September 22, led by Emergence Capital with participation from Super Micro Computer, MUFG Innovation Partners, Varma Mutual Pension Insurance, and Lifeline Ventures. The company is now valued at at least $1 billion, underscoring continued investor appetite for the infrastructure powering the AI boom.

Anthropic and OpenAI cut flagship prices within a day of each other: Anthropic released Claude Opus 5.5 on September 22 as its new default flagship, priced at $4 per million input tokens and $20 per million output tokens, down from $5 and $25 for Opus 5. OpenAI answered the same day with GPT-6 Sol and Luna, priced at $2 and $0.10 per million input tokens, respectively, positioned below its flagship GPT-6 Astra.

Island raised $400M at a $6.4B valuation to secure enterprise AI agents: Enterprise security startup Island raised $400 million in a Series F on September 24, led by Evolution Equity Partners with participation from Sequoia Capital, Coatue Management, and J.P. Morgan Growth Equity Partners. The company said the funding will fuel its next phase as businesses scale AI agents and modernize how work gets done, a reminder that agent security is becoming its own category.

Cisco Talos discloses the first fully autonomous AI malware: Cisco Talos disclosed CLOSEDQUORUM on September 22, describing it as the first reported fully autonomous multi-model AI command-and-control implant operating with no human operator, and released a toolkit called CAIRN for detecting it. As agents take on more real work, this is exactly the kind of story that makes “who’s accountable when it fails” a live question, not a hypothetical one.

That’s all for this week’s AI drop. Follow @AITECHio for more innovation updates!

Why We Share These Updates: We share these AI roundups to keep our community plugged into the breakthroughs shaping the AI landscape we’re building. ACN isn’t just following the news; we’re part of the AI sector, creating the infrastructure, tools, and ecosystem that will power the next wave of AI innovation.

Another week of building, improving, and pushing Agent Forge forward.

This week, our focus was split between an important conversation around safer agentic systems and continued improvements to the platform’s core infrastructure.

Safer Agentic Workflows with Concordium

We co-hosted an AMA on X Spaces with Concordium, titled “Safer Agentic Workflows with ACN.”

The discussion focused on how safer and more accountable agentic workflows can be built within Agent Forge, particularly as AI agents become more autonomous and begin interacting with increasingly complex systems.

Together with Concordium, we explored the role that identity, verification, and accountability can play in creating a stronger foundation for the next generation of AI agents.

A big thank you to everyone who joined the Space, listened in, and contributed to the conversation.

Strengthening the Core of Agent Forge

Alongside the AMA, development continued across the platform.

This week’s work focused primarily on stability and runtime improvements, strengthening the underlying infrastructure to keep Agent Forge performing reliably as usage continues to grow.

These improvements may happen behind the scenes, but they remain an important part of ensuring creators can build, deploy, and run their agents on a solid foundation.

Keep the Feedback Coming

Community feedback continues to play an important role in how we improve the platform.

If you’ve noticed something that needs fixing, have feedback on your experience, or have a topic you’d like us to explore during a future X Space, reach out to the team via PM or email.

We’d love to hear from you.

Keep building. Keep exploring.