Affordable cloud computing for AI/ML and Analytics

A cost-efficient solution for computing to maximize productivity and accelerate innovation.

Compute Solutions

Achieve your AI/ML and Advanced Analytics Goals

Reduce your cloud infrastructure

No need to build a cloud environment. Deploy and execute AI/ML inference and analytics workloads in a secure environment, optimize your resource allocation, and enable your cloud operations team to focus on more critical work.

Lower the cost of executing workloads

Benefit from cost-efficient and competitively priced cloud computing enabled by Sailion’s streamlined computing platform. Competitive pricing for compute, and decreased management and operational requirements drive a lower total cost of ownership than leading providers.

Accelerate your time to value

Simplify the process of provisioning your workloads. Offloading the provisioning and execution of your analytics workloads dramatically reduces the time to go from data to insights.

Decrease expenses and accelerate innovation with Sailion edge-based cloud computing

Monetize Devices

Safely, securely, and seamlessly generate revenue from your devices, allowing you to offset your cost of ownership.

Create a secure private cloud from your underutilized devices

Save money by executing your AI/ML inference and analytics workloads on your private cloud.

Create a secure cloud to rent to other businesses

Make money by securely and seamlessly providing your devices’ processing capabilities to third parties that require compute resources.

Contribute to a more environmentally sustainable world

Utilizing the surplus processing capacity of devices outside the data center decreases the need to produce more servers and data centers, which have a significant negative impact on the environment. By securely registering your devices as compute nodes on Sailion’s platform, you can help the sustainable computing initiative.

Contact us to see if your device is supported on Sailion’s Platform

Cloud Computing Market Challenge

Demand for computation is increasing at an exponential rate, outpacing supply, and driving up costs.

If this compute supply-demand imbalance continues, there will be significant negative societal effects such as the monopolization of compute supply, digital inequality, the slowdown of AI innovation, negative environmental effects from data centers, and increased compute costs.

Compute supply and accessibility is a growing concern

Read what others are saying and doing about the problem Sailion is addressing

US National Artificial Intelligence Research Resources Initiative

While AI research and development (R&D) in the United States is advancing rapidly, opportunities to pursue cutting-edge AI research and new AI applications are often inaccessible to researchers beyond those at well-resourced companies, organizations, and academic institutions. A NAIRR would change that by providing AI researchers and students with significantly expanded access to computational resources, high-quality data, educational tools, and user support—fueling greater innovation and advancing AI that serves the public good.

NSF

Sam Altman - AI Infrastructure Goal

Altman posted on X (formerly Twitter) that OpenAI believes “the world needs more AI infrastructure--fab capacity, energy, data centers, etc--than people are currently planning to build.” He added that “building massive-scale AI infrastructure, and a resilient supply chain, is crucial to economic competitiveness and that OpenAI would try to help."

CNBC

Masayoshi Son - SoftBank Founder

SoftBank Group’s Masayoshi Son has made no secret of his intent to double down on the red-hot artificial intelligence industry. Now he’s fundraising for his next move in that strategy. According to a report in Bloomberg, the SoftBank founder is seeking $100 billion to build a new venture that would compete with the likes of Nvidia in the area of AI chips...Nvidia currently dominates the AI chip market with its GPU chips. But with the need for AI processors projected only to grow — and with a lot more work to be done to improve efficiency and cost — there’s a clear opening for others to compete with alternatives, whether they are like-for-like GPUs, new approaches to GPUs or an entirely different processing approach altogether.

Techcrunch

Mark Zuckerberg - Meta Compute Infrastructure

Zuckerberg said the company’s “future roadmap” for AI requires it to build a “massive compute infrastructure.” By the end of 2024, Zuckerberg said that infrastructure will include 350,000 H100 graphics cards from Nvidia...Analysts at Raymond James estimate Nvidia is selling the H100 for $25,000 to $30,000, and on eBay they can cost over $40,000. If Meta were paying at the low end of the price range, that would amount to close to $9 billion in expenditures. Additionally, Zuckerberg said Meta’s compute infrastructure will contain “almost 600k H100 equivalents of compute if you include other GPUs."

CNBC

Patrick Gelsinger - Intel CEO

"At that scale", he went on, "training the next big AI model will cost a staggering amount of money. We’re already saying we may spend a couple billion dollars training the most advanced models today,” Gelsinger said—a striking figure in itself. “Plus, you know, the math in the $7 trillion also includes power and data centers.” Suddenly $7 trillion for an AI chip project was beginning to sound reasonable. Gelsinger’s acceptance of the idea that executing one software program could cost multiple billions of dollars would’ve been unthinkable a decade ago—or perhaps even 18 months ago. But the recent progress in AI has relied on using previously inconceivable amounts of data to train new algorithms, and inspired new hunger for ever larger models and datasets.

Wired

Positive Impact Goals

Enabling businesses and organizations greater access to computational resources through lower costs and enhanced user experiences.

Unlock Greater Access

Cost effective computing

Lower financial barriers for access to computing.

Lower operational barriers to entry

After account setup, it’s just a few clicks to executing a workload

Lower financial barriers for acquiring compute devices/advanced hardware

Compute-enabled devices running part-time on the Sailion network are more financially feasible to acquire because they can be utilized to a greater extent, generating revenue over time while being an “active” node on the Sailion network

Sustainable Computing

Reduce the need for more data centers

Data centers require an enormous amount of energy to build, operate, and maintain. The fewer data centers that need to be built, the more sustainable the cloud ecosystem becomes.

Upcycle and utilize existing devices with surplus processing capacity

Compute devices require energy to manufacture and build. Society should attempt to up-cycle and utilize these devices to the greatest extent possible

Execute compute workloads on non-datacenter devices that holistically require less energy to operate

Data center operations use an enormous amount of energy, and release significant carbon primarly produced by cooling the data center computers and equipment. Devices outside data centers are designed for operating at room temperature, thereby eliminating the HVAC cooling system energy requirement associated with traditional data center computing

Contact Us

Get Early Access

Reach out to the team at Sailion for all inquiries regarding executing workloads, registering your devices, or general questions.