Many years ago, Alan Kay famously said, "People who are really serious about software should make their own hardware." This statement reflects a profound truth about the relationship between software and hardware: when you control both, you unlock a level of optimization and customization that is simply not possible otherwise. Today, as we find ourselves on the precipice of the AI revolution, it’s becoming clear that those who are truly serious about AI should take a similar approach with their datacenters. Surprisingly, it’s more accessible than you might think.

The $8,000 Container Datacenter: A Game Changer

To the surprise of many, setting up a container datacenter is not a multi-million dollar endeavor. In fact, the cheapest container datacenters start at just $8,000. Yes, you read that correctly — for the price of a mid-range car, you could own a fully functional, self-contained datacenter capable of supporting the most demanding AI workloads. Even cheaper than somebody's game machine, huh?

A container datacenter, unlike traditional datacenter solutions, is modular, portable, and can be deployed virtually anywhere. It offers the flexibility to scale up or down according to your needs, without the typical constraints and overheads of renting space in a co-located datacenter or relying on expensive cloud services. In a world where AI models are growing exponentially in size and complexity, having your own container datacenter offers a significant strategic advantage.

The Next-Generation Datacenter

Container datacenters represent the next-generation approach to data infrastructure. Rather than building a massive, static facility, you start with a shipping container outfitted with all the essentials: servers, storage, networking equipment, and cooling systems.

These containers can be shipped and deployed anywhere — from a parking lot in the city to a remote location far from urban infrastructure. They are self-contained units, complete with their own power and cooling systems, and can operate independently or be linked together to form a larger, more powerful datacenter. This modular approach brings the benefits of rapid deployment, scalability, and flexibility, which are crucial in today’s rapidly evolving AI landscape.

Moreover, the container datacenter is more energy-efficient than traditional facilities, using advanced cooling technologies and compact designs to minimize power consumption. This efficiency is particularly important as the demand for AI computation grows, making energy management a key consideration for anyone serious about scaling AI capabilities.

The Scaling Issue of Traditional Racks

The traditional rack-based datacenter architecture, while still prevalent, comes with a host of scaling issues that make it less suitable for the dynamic needs of AI applications. Traditional datacenters are often limited by their physical space, power, and cooling capabilities. Expanding capacity usually involves significant infrastructure changes, which can be both time-consuming and expensive.

Additionally, rack-based architectures often suffer from inefficient use of space and power, with large portions of the facility taken up by cooling systems, power distribution units, and other infrastructure components. As the demand for more powerful AI models grows, so does the need for a more efficient, scalable solution that can support rapid changes in computational requirements.

Container datacenters solve these problems by offering a highly flexible, modular approach to scaling. Need more compute power? Simply add another container. Need to relocate your datacenter to take advantage of lower energy costs or better connectivity? Ship your container to a new location. This flexibility is impossible to achieve with traditional, static datacenter facilities.

OpenBMC: The Key to Next-Gen Infrastructure Automation

OpenBMC is an open-source Baseboard Management Controller (BMC) firmware stack. A BMC is a specialized microcontroller embedded on a server's motherboard, tasked with managing and monitoring the physical state of the hardware, such as power status, temperature, and other critical components. It is a vital part of the server that operates independently of the main CPU, providing remote management capabilities even when the server is powered off or unresponsive.

To utilize OpenBMC for infrastructure automation, I wrote Chiba—a flexible and extensible framework designed to automate the management of servers across a datacenter by leveraging the powerful capabilities of OpenBMC. Chiba aims to bridge the gap between hardware-level control and high-level orchestration by providing a centralized management solution that interprets server configuration scripts, handles dynamic scaling, enhances security, and ensures seamless integration with existing cloud technologies like Kubernetes and virtualization. This allows organizations to efficiently manage their infrastructure at scale, reduce operational overhead, and adapt quickly to evolving technology requirements.

How Does Chiba Work?

Chiba typically runs on a management server that communicates directly with the OpenBMC firmware on the servers inside the container datacenter. By interpreting SCL (Server Configuration Language) scripts, Chiba manages the servers, defining their configuration, monitoring them in real-time, and taking action based on monitoring results. These SCL scripts are executed by the Chiba server and converted into Redfish API calls, which are then sent to the OpenBMC firmware of all the servers in the container datacenter.

This level of automation not only simplifies management but also enhances security by ensuring all configurations are consistent and up-to-date across the entire infrastructure. Chiba is not just another project for controlling a single OpenBMC node; it’s designed to manage many, or even massive numbers, of OpenBMC nodes, making it ideal for building the next generation of cloud computing infrastructure.

Why You Should Consider Your Own Datacenter

If you're serious about AI, you should consider having your own datacenter. With container datacenters becoming affordable and frameworks like Chiba making management easier than ever, now is the perfect time to rethink your data strategy. Owning your own datacenter gives you complete control over your AI infrastructure, allowing you to scale up quickly, optimize for performance, and ensure security at every layer of your stack.

As Alan Kay suggested decades ago, those who are truly serious about their craft should control every aspect of their environment. For AI, this means building your own datacenter — and with solutions like container datacenters and the Chiba Project, that goal is more achievable than ever.