Technology

Unleashing AI Potential: The Power of Your Own Local Supercomputer

Published

1 year ago

May 28, 2025

RIVERSIDE, Calif., May 28, 2025 /PRNewswire/ — In the rapidly evolving landscape of artificial intelligence (AI) and deep learning, access to robust computing power is paramount. While cloud-based GPU solutions offer undeniable flexibility, a growing number of AI professionals, researchers, and startups are discovering the profound benefits of investing in their own local GPU servers. This shift isn’t just about preference; it’s about unlocking a powerful, private, and predictable environment that can truly accelerate the pace of innovation.

Owning a local GPU server for deep learning and AI model training presents a compelling set of advantages that directly address many of the challenges faced when relying solely on external resources.

Long-Term Cost-Effectiveness: A Smart Investment

At first glance, the upfront cost of a dedicated GPU server might seem substantial, especially when compared to the pay-as-you-go model of cloud services. However, for sustained and intensive AI workloads, this initial investment quickly transforms into significant long-term savings. Unlike cloud GPUs, where every minute of usage, including idle time or unexpected interruptions, incurs charges, owning your hardware means your operational costs are dramatically reduced over time. Consider the example of Autonomous Inc.’s Brainy workstation: users can save thousands of dollars within just a few months compared to continuous cloud rentals, making it a financially astute decision for ongoing projects.

Enhanced Data Privacy and Security: Keeping Your Innovations Safe and Confidential

In an era where data breaches, intellectual property theft, and stringent regulatory compliance (like GDPR or HIPAA) are paramount concerns, the security and privacy advantages of a local GPU server are absolutely critical. This is perhaps one of the most compelling reasons for organizations and individuals dealing with sensitive information or proprietary algorithms to choose an on-premise solution.

Unrivaled Local Control: Your sensitive data, proprietary AI models, and confidential research remain entirely within your physical control. They reside securely within your own infrastructure, behind your own firewalls and security protocols. This dramatically reduces the inherent risks of data breaches, unauthorized access, or compliance issues that can arise when data is stored and processed on third-party cloud servers, where you have less direct oversight.Minimized Exposure to External Threats: By keeping your data and computations local, you significantly reduce the need for constant data movement between your environment and external cloud providers. Fewer data transfers inherently mean fewer points of vulnerability and a smaller attack surface, strengthening your overall security posture against external threats. This direct control ensures your most valuable assets are always under your watchful eye.

Unparalleled Performance and Responsiveness: Unleashing True AI Power

One of the most immediate and impactful benefits of a local GPU server is the sheer performance and responsiveness it offers. When your computing power is on-premise, you experience:

No Queuing: The frustration of waiting in line for available cloud resources becomes a thing of the past. You have immediate, dedicated access to your computing power precisely when you need it.Zero Internet Lag: All computations occur locally, eliminating any latency or slowdowns that can plague internet-dependent cloud connections. This is particularly critical for iterative prototyping, fine-tuning, and real-time inference where every millisecond directly impacts development speed.Consistent Power: Your AI models run without the threat of interruption from network fluctuations or contention with other users on shared cloud infrastructure. This translates to pure, uninterrupted AI processing power, allowing your training runs to complete efficiently and reliably.

Maximum Flexibility and Customization: Tailoring Your AI Environment

A local server grants you an unparalleled degree of control over your computing environment:

Hardware Control: You have the freedom to select and configure the exact hardware components—from the number and type of GPUs to RAM, storage, and CPU—that perfectly align with your specific deep learning tasks and budget. This allows for highly specialized setups optimized for your unique needs.Software Environment: You can meticulously set up and customize your entire software stack, including the operating system, drivers, AI frameworks (like TensorFlow or PyTorch), and libraries. This freedom from cloud provider limitations or pre-configured images enables deep optimization for unique and cutting-edge workflows.

Reliability and Predictable Operations: Peace of Mind for Critical Projects

For critical AI workloads, predictability is key, and a local server delivers just that:

No Spot Instance Shutdowns: Cloud “spot instances,” while often cheaper, come with the risk of unexpected shutdowns by the provider. A local server guarantees continuous operation for your crucial training runs, preventing lost progress and wasted time.Full Control Over Maintenance: You dictate when and how to perform system maintenance or updates, ensuring that your vital AI workloads are never interrupted by unforeseen actions from a third-party provider.

Hands-On Learning and Experimentation: Deepening Your Expertise

For those looking to truly master the intricacies of AI development, a local server offers an invaluable educational experience:

Deeper Understanding: Owning and managing your hardware provides a hands-on opportunity to learn about system administration, hardware optimization, and the fundamental workings of AI workflows.Unrestricted Experimentation: You can freely experiment with different hardware configurations, driver versions, and software stacks without incurring additional costs or worrying about impacting a shared environment. This fosters a deeper understanding and encourages innovative problem-solving.

“We’re seeing innovative companies recognize the need and engineer solutions specifically to address the cloud’s limitations for many businesses,” says Mr. Dhiraj Patra, a Software Architect and certified AI ML Engineer for Cloud applications. “The ability to have dedicated, powerful GPU workstations on-site, like the Brainy workstation with its NVIDIA RTX 4090s, provides that potent combination of performance, cost-effectiveness, and data security that is often the sweet spot for SMBs looking to seriously leverage AI and GenAI without breaking the bank or compromising on data governance.”

Experience Brainy Firsthand: The Test Model Program

To give developers, researchers, and AI builders a chance to experience the power of Brainy before committing, Autonomous Inc. has just announced that their sample of Brainy, the supercomputer equipped with dual NVIDIA RTX 4090 GPUs are now open for testing, giving a fantastic opportunity to see firsthand how your models perform on this supercomputer.

How the Test Model Works:

Brainy functions as a high-performance desktop-class system, designed for serious AI workloads like hosting, training, and fine-tuning models. It can be accessed locally or remotely, depending on your setup. Think of it as your own dedicated AI workstation: powerful enough for enterprise-grade inference and training tasks, yet flexible enough for individual developers and small teams to use without the complexities of cloud infrastructure.

Simply by clicking the “Try Now” button and filling a form on Autonomous’ website, the testing will be ready within a day. This hardware trial program allows participants to book a 22-hour slot to run their inference tasks on these powerful GPUs. Whether you’re building AI agents, running multimodal models, or experimenting with cutting-edge architectures, this program lets you validate performance on your own terms—with no guesswork. It’s a simple promise: use it like it’s yours—then decide.

In conclusion, a local GPU server like Autonomous Inc.’s Brainy is more than just powerful hardware; it’s a strategic investment in autonomy, efficiency, and security. By providing a private, predictable, and highly customizable environment, it empowers AI professionals to iterate faster, safeguard sensitive data, and ultimately accelerate their journey in the exciting world of deep learning and AI innovation.

Availability

Brainy is available for order, making enterprise-grade AI performance accessible to startups and innovators For detailed specifications, configurations, and pricing, please visit https://www.autonomous.ai/robots/brainy.

About Autonomous Inc.

Autonomous Inc. designs and engineers the future of work, empowering individuals who refuse to settle and relentlessly pursue innovation. By continually exploring and integrating advanced technologies, the company’s goal is to create an ultimate smart office, including 3D-printed ergonomic chairs, configurable smart desks, and solar-powered work pods, as well as enabling businesses to create the future they envision with a smart workforce using robots and AI.

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SOURCE Autonomous Inc.

Technology

Pepperstone Appoints Andrew Turnbull to Lead Africa Strategy as Trading Markets Mature

Published

9 minutes ago

September 1, 2026

Nairobi-based appointment strengthens Pepperstone’s investment in Africa as mobile trading grows and regulators across the continent raise standards.

MELBOURNE, Australia, Sept. 1, 2026 /PRNewswire/ — Pepperstone, a global online trading provider serving clients in more than 160 countries, has appointed Andrew Turnbull as Head of Africa, strengthening its focus on one of the world’s fastest-evolving online trading regions. Based in Nairobi, Turnbull will lead Pepperstone’s strategy across the continent as traders increasingly turn to mobile-first platforms and regulators move to strengthen oversight of the sector.

Turnbull brings more than 20 years of experience in financial services, including senior roles at ODL Securities and FXCM Europe, where he led institutional sales and partnerships. His experience spans regulated FX and CFD markets, institutional relationships and business development across international markets.

The appointment also comes as Pepperstone invests in owning more of its technology, giving the business greater control over the trading experience and allowing it to respond more closely to the different needs of clients across individual markets.

“Africa is dozens of distinct regulatory environments and trader profiles,” said Marc Boever, Head of EMEA at Pepperstone. “That is why we are putting more resources on the ground and investing in people who understand the region. Andrew’s experience across regulated financial services and institutional partnerships, combined with his growing first-hand understanding of markets like Kenya, makes him the right person to lead our growth across the continent.”

Kenya, where Pepperstone is licensed under the Capital Markets Authority (CMA)*, was one of the first African countries to introduce a formal regulatory framework for online forex trading. That early move has helped create a more mature market, with regulated, licensed brokers increasingly trusted by traders, while Kenya’s experience offers a model for other African regulators looking to bring greater oversight to the sector.

“Kenya’s traders were among the first in Africa to get a properly regulated market to trade in, and that head start shows,” said Andrew Turnbull, Head of Africa at Pepperstone. “There is a growing appetite for online trading across the continent, but every market is different. I’m looking forward to building on Pepperstone’s presence here and working with our teams and partners to better understand and serve the different trading communities across Africa.”

Ends

* Pepperstone Markets Kenya Limited is licensed and regulated by Kenya’s Capital Markets Authority under licence number 128.

About Pepperstone: Pepperstone is a global fintech and CFD broker serving traders in more than 160 countries. The company provides access to forex, indices, commodities, shares, ETFs and digital asset markets through industry-leading platforms, competitive pricing and a strong regulatory framework.

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Technology

Cherubic Ventures Closes $68.88 Million Fund VI as AUM Surpasses $500 Million

Published

9 minutes ago

September 1, 2026

Early Investment Sudo AI Valued at Nearly $2B

TAIPEI, Sept. 1, 2026 /PRNewswire/ — Cherubic Ventures today announced the close of its sixth fund (Fund VI) at $68.88 million. The fund size reflects the auspicious meaning of the number eight in East Asian cultures, where it is traditionally associated with prosperity and good fortune. With this close, assets under management across the firm’s six funds have surpassed US$500 million.

Investors across all six funds include leading global institutional investors and foundations, as well as publicly listed companies, family offices, successful entrepreneurs and high-net-worth individuals.

Fund VI maintains the firm’s early-stage focus, investing in AI-native companies across infrastructure, developer tools, enterprise software, healthcare, physical AI and robotics. Sudo AI, a robotics startup in the portfolio, has reached a valuation of nearly $2 billion two years after its founding, joining the ranks of unicorns.

“After ten years, I am more certain than ever about why I chose to invest at the earliest stages,” said Matt Cheng, Founder & Solo GP of Cherubic Ventures. “Working alongside exceptional founders, finding a path through uncertainty, and ultimately changing an industry is what keeps driving me.”

Investing Across AI, From Infrastructure to Industry Applications

As AI reshapes industries, Cherubic Ventures continues to look for founders using the technology to build new products and redefine markets. Since 2024, the firm’s AI-native investments have spanned infrastructure, developer tools, enterprise software, healthcare, physical AI and robotics.

In robotics, Sudo AI was co-founded by Hao Su, a leading researcher in embodied AI and 3D vision and co-author of PointNet, and serial entrepreneur Robin Han. Its sudo R1 robotic system is trained through virtual simulation and can reliably handle objects it has never encountered without relying on real-world manipulation data. This addresses a key bottleneck to deploying robotics at scale. Cherubic Ventures was its earliest institutional investor.

Cherubic Ventures is also an early investor in Entire, the developer platform founded by former GitHub CEO Thomas Dohmke. The company raised US$60 million earlier this year, the largest seed round ever for a developer tools startup.

While Fund VI is still at an early stage, its portfolio companies have already raised more than $500 million in subsequent funding. Other notable investments include AI-powered patent technology platform Patlytics, along with healthcare and drug development companies Max AI, Generation Lab and therapiAI.

A Decade Alongside Founders, Supporting the Next Generation

Founded in 2015, Cherubic Ventures was among the first venture firms in the world to adopt the solo GP model. It has invested in more than 200 companies globally, with early investments including Hims & Hers, Flexport, Calm, Paidy, 91APP and Astranis.

Across its portfolio, Cherubic Ventures has been the earliest institutional investors in dozens of companies that went on to become unicorns. Hims & Hers is listed on the New York Stock Exchange and 91APP on the Taipei Exchange, while Paidy was acquired by PayPal for US$2.7 billion.

Fund VI marks the beginning of Cherubic Ventures’ second decade. “The past ten years have made me more certain that believing in founders before the answers are clear, and backing them through uncertainty, is at the heart of early-stage investing,” Cheng said. “In the next decade, we will continue to ‘Stay Early’ and work with the most exceptional founders to build the future we want to see.”

About Cherubic Ventures
Founded in 2015, Cherubic Ventures is a global early-stage venture capital firm that started in Taipei and has built a strong presence in the U.S. market. The firm backs outstanding founders from day one and was among the first venture firms in the world to adopt the solo GP model. Notable investments include Hims & Hers, Calm, Flexport, 91APP, Paidy, Formation Bio and Astranis. To date, Cherubic Ventures has invested in more than 200 startups and brings together more than 500 founders and investors in a distinctive global community.

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SOURCE Cherubic Ventures

Technology

Agentic AI Has Arrived. Is Your Workforce Ready to Leverage It?

Published

9 minutes ago

September 1, 2026

Enterprises are deploying AI agents faster than they are building the certified talent to run them. Closing that gap is now the real differentiator.

Authored by, Vikas Mathur, Vice President, Trainocate India

MUMBAI, India, Sept. 1, 2026 /PRNewswire/ — Across the enterprise programs we run every week at Trainocate, the conversation has changed. A year ago, leaders asked us what generative AI could do. Today they ask why their agentic pilot has not reached production. Agentic AI has arrived — the question is no longer whether it works, but whether the workforce is ready to leverage it.

The platforms have done their part. AWS, Microsoft, Google Cloud, Databricks and others have moved agent frameworks, orchestration layers and governance tooling into general availability. What has not kept pace is the workforce. Adoption forecasts keep climbing; the cancellation forecasts climb with them, and for reasons that have little to do with the models themselves.

40%+

of agentic AI projects are forecast to be scrapped by the end of 2027 — on escalating cost, unclear business value and inadequate risk controls.

Source: Gartner

Our own view, formed across thousands of enterprise learners, is simpler than any forecast: Technology is not the constraint. The certified, deployment-ready workforce is.

India’s AI Talent Equation: One Million Roles, One in Six Skilled

India has the demand and the ambition. The constraint is supply. Estimates put the national AI talent pool at 1.25 million by 2027 — real growth, but well short of a market compounding at 25–35% a year. On current trajectories the gap widens before it closes.

We see the consequence directly in client conversations. Skills mismatch, not headcount, is what delays deployment — and on most enterprise shortlists, demonstrable and certified capability now outranks the degree.

Figure: The agentic readiness gap — adoption is outpacing certified capability.

From Prompt Engineering to Agent Orchestration: Three Capability Shifts

From operator to orchestrator. Every prior automation wave asked people to use a tool. Agentic AI asks them to direct one. The working skill is decomposition — mapping a process into the steps an agent may own, the tool-calling boundaries it must respect and the human-in-the-loop checkpoints between them. That is delegation and process design before it is programming, which makes it teachable well beyond the engineering bench.

From reviewing output to governing outcomes. When AI drafts an email, a human reads it before it goes. When an agent provisions infrastructure or triggers a payment, reading it afterwards is too late. Enterprises need people fluent in least-privilege identity, data lineage and governance, evaluation harnesses, escalation thresholds, observability and cost control. In our experience, this is where most agentic programs are thinnest.

From individual courses to cross-functional readiness. One production agentic workflow touches data engineering, application development, identity and security, LLMOps and the business function it serves. Certifying one persona while the rest stand still guarantees the pilot dies at handover. The unit of skilling must become the team.

What we see

Agentic pilots rarely stall on model quality. They stall because too few people can scope what an agent may own, design its guardrails, and stay accountable when it acts alone.

Trainocate enterprise delivery experience

Why Vendor-Authorized Certification Is the New Deployment Prerequisite

Credentials are often said to date quickly in a field moving this fast. We find the opposite. Agentic concepts are universal; implementation is not. Identity and access design, data governance, retrieval and grounding, model selection, evaluation and cost management behave differently on AWS, Microsoft Azure, Google Cloud and Databricks — and those differences decide whether an agent survives production.

Vendor-authorized certification remains the only independently verifiable proof that an engineer can build and operate on a given stack. Foundational credentials also give HR, finance, risk and procurement a shared vocabulary with engineering — and agentic decisions are risk decisions as much as technical ones.

2 in 5

Employers now prefer demonstrable AI skills and certifications over academic degrees. Skills-based hiring is no longer emerging — it is the default.

Source: NASSCOM–Indeed India AI Talent Report, 2026

Experiential Learning: Turning Training Investment into Production Capability

Nobody learns to supervise an autonomous system from a slide. Trainocate’s Experiential Learning Model was built on that premise — one continuous journey rather than a catalog of courses:

Learn from practitioners. Instructor-led and virtual instructor-led training delivered by vendor-authorized, actively certified instructors.Reinforce on demand. Self-paced digital learning and curated learning paths that keep pace with quarterly platform releases.Build in live environments. Hands-on labs in real cloud sandboxes — agents, tool-calling, guardrails and failure modes, not screenshots.Prove it on real work. Capstone projects mapped to the organization’s own agentic and cloud use cases.Certify the capability. Structured exam preparation and readiness checks that convert learning into a verifiable credential.Measure the outcome. Governance dashboards tracking completion, certification attainment and skill progression for L&D and business sponsors.

That model now runs through our AI Mastery Program, which spans foundational to advanced tracks for both business and technical roles across AWS, Microsoft, Google Cloud, Databricks and vendor-neutral content — with agentic system design, multi-agent orchestration and AI governance sitting in the advanced tiers, and sandbox labs and industry capstones throughout.

The results hold up: Close to 80% certification attainment across enterprise programs and a 4.90/5.00 delivery CSAT. As an authorized training partner for AWS, Microsoft, Google Cloud, Databricks and more, operating across 24 countries, we have run this model at scale — over one lakh professionals certified within a single global enterprise account, and agentic AI labs delivered across six Indian cities this year. Four consecutive AWS Global Training Partner of the Year awards and six appearances on the Training Industry Top 20 suggest the model travels.

30%

of enterprise application software revenue will be driven by agentic AI by 2035 — up from 2% in 2025.

Source: Gartner

A Twelve-Month Skilling Blueprint for CHROs and L&D Leaders

Assess against use cases, not catalogs. Benchmark capability against the specific agentic workflows the business intends to run.Build a spine, not a stack. Foundational AI and cloud fluency organization-wide; certified specialization for those who will design, secure and govern agents.Skill the workflow, not the individual. Move cross-functional cohorts together — data, application, security, business — so nothing stalls at handover.Instrument on outcomes. Track certification attainment, time-to-productivity and pilot-to-production conversion. Seat-hours measure activity, not readiness.

Two Budget Cycles: The Window for Workforce Readiness

15%

of day-to-day work decisions will be made autonomously by 2028 — up from effectively zero in 2024.

Source: Gartner

That is not a distant horizon. It is two budget cycles away.

Models are becoming a commodity; every enterprise buys them at roughly the same price. The durable differentiator is the depth of certified talent that can point those models at the right problems and stay accountable for what they do. Treat skilling as infrastructure — continuous, measured, certified — and your agents scale. Treat it as an event and the pilot stays a pilot.

Agentic AI has arrived. The question every board should be asking is whether its workforce is ready to leverage it.

Build a Certified, Agent-Ready Workforce

Trainocate partners with enterprises to build agentic AI and cloud capability at scale — from foundational fluency to certified specialization across AWS, Microsoft, Google Cloud, Databricks and more, delivered through our Experiential Learning Model and AI Mastery Program. To design a skilling roadmap for your workforce, write to cloudacademy@trainocate.com or call +91 9223361686.

About Trainocate

Trainocate is a global IT training and workforce skilling organization and an authorized training partner for AWS, Microsoft, Google Cloud, Databricks and more, operating across 24 countries. Trainocate delivers cloud, data and AI capability to enterprises through its Experiential Learning Model and AI Mastery Program, combining instructor-led training, self-paced digital learning, hands-on sandbox labs, industry capstones and vendor-authorized certification. The company is a four-time consecutive AWS Global Training Partner of the Year and has appeared six times on the Training Industry Top 20. Trainocate India operates as Networks India Pvt Ltd. For more information, visit www.trainocate.com/in.

About the Author

Vikas Mathur is Vice President at Trainocate India, where he leads the Cloud, Data & AI competency business. He works with enterprise L&D and technology leaders across India and Asia on cloud and AI workforce readiness, and can be reached at cloudacademy@trainocate.com or +91 9223361686.

Data sources referenced: Gartner (agentic AI adoption, project cancellation, governance maturity, autonomous-decision and market-share forecasts, 2025–26); McKinsey (State of AI, agent pilot-to-production); NASSCOM and MeitY (India AI job demand and AI-skilled share); NASSCOM–Deloitte (AI talent pool projection); NASSCOM–Indeed India AI Talent Report 2026 (skills-based hiring). Trainocate figures are from our own enterprise delivery data.

Contact: cloudacademy@trainocate.com | +91 9223361686

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