Improve performance/watt by 20% or more, guaranteed.

Here is a typical solution to data center configuration complexity, tailored to meet the need for efficient performance/watt, while acknowledging that configuration of complex systems is very hard and problematic.

Use the best available system architecture, with the best chips and configure using the best tools, including AI training and inference support for your typical workloads. Fine tune the software dynamically to operationally run clusters for various clock speed/accuracy trade-offs or the like and integrate battery technologies and high-performance cooling technology to help improve power performance.

Do the best you can, consistent with your standards and practices, as time and budgets permit. Build the biggest data centers you can, using as much of the cheapest energy you can acquire. Then, build them as fast as you can, flip the on switch and run each data center full out all the time.

All good. But let’s also take advantage of some computational breakthroughs that marry control theory to manage dynamics, AI to understand complexity and active manipulation of configuration spaces to goals. With these tools, we’ll use the combined capability to place goals at each integration point of an existing design or system.

Self-Aware AI Data Centers

Welcome to the world of Self-Aware™ AI data centers. High performance, scalable, resilient and optimally efficient. (Also works great when applied to legacy systems.) 

We now can deploy a state-of-the-art data center that can reason about its performance continually and dynamically configure each unique configuration space to optimize that interface of complexity to its best goal-driven solution. Best of all, the data center can be future-proofed to changing requirements, by simply changing goals, avoiding the cost of extensive retraining or re-engineering.

AI data centers are replete with opportunities for applying Self-Aware™ and AdaptiveAI™ technologies, using the Sequitur™ Platform. Initially, let’s take a step back to explore the possibilities. 

Opportunities For Improvement Are Limitless

The energy demands of AI are staggering. Legacy data centers and planned hyperscale AI data centers will account for a sizable portion of global electricity consumption, while classical and AI workloads will continue to grow exponentially with no limit in sight. 

What we can be sure of is that every AI data center will be operating at capacity, each under their own unique energy constraints. These requirements mean that configuration and operational strategies, matched to the varied computational workloads they serve, will need to be optimized for delivering maximum performance per watt; and the management of thermal loads will continue to be extremely complex.

This is difficult to achieve with conventional technologies, as data centers typically operate under static configurations that are optimized during setup but struggle to adjust in real time to fluctuating workloads, resulting in inefficient use of resources like CPU, GPU, memory and power. 

Likewise, these same data centers often have distributed architectures where different servers or clusters handle different AI workloads. Managing and scaling these resources to handle varying workloads across servers can be difficult and inefficient. 

Complicating these configuration decisions, AI data centers must ensure continuous operation but are prone to disruptions due to hardware failures, software bugs, or unexpected workload surges. They often over-provision resources to handle peak loads, resulting in higher operational costs.

Even today, in the most modern of AI data centers, the configuration of thousands of Blackwell, Cerebras, Intel or other nodes, the management of communications buses for managing massively parallel compute strategies, the management of the O/S interface with client applications, the addition of new battery technologies and the physical management of the cooling environment add complexity and efficiency losses. 

And then there are the maintenance strategies to consider, the degradation in performance of electronics over time, code conflicts, mix and match effects of meshed data centers with different architectures, regulations and environmental concerns, security and the list goes on.

Think Workloads and Goals

Self-Aware™ and AdaptiveAI™ data centers apply goals and enable dynamic management of both the physical hardware and workload compute, as the system reasons about its performance to goals and adjusts its configuration spaces accordingly. Here are just some possibilities to consider that we have already verified on one of the World’s fasted supercomputers:

• If a spike in demand is unanticipated, the system can scale up and dynamically manage resources, ensuring that the data center meets performance demands without unnecessary over-provisioning. Conversely, during periods of low demand, the system can downscale resources to save energy, consistent with its goals. This broad, new goal-responsive capability applies uniquely to managing within the dynamic changes while a specific computational workload is running, while other goals are responsive to their targeted workloads.
• Resource allocations are based on real-time feedback from workloads. For instance, a Self-Aware™ system will always adapt to conditions with a configuration state most responsive to the established goals.
• In a distributed, Self-Aware AI data center, goals can ensure that each subsystem or server operates at optimal efficiency, balancing the workloads across available resources. It can shift tasks between systems or redistribute power and computational resources depending on overall system demand, balancing local (server-level) goals with global (datacenter-level) ones.
• A Self-Aware data center can monitor the health of system components and adjust configurations in real time to mitigate the impact of component failures. For example, if a GPU fails or becomes overloaded, goals can enable routing to other processing units, rebalancing the load dynamically. It can also adjust inference configurations to maintain service continuity, even at reduced performance levels.
• Goals can enable AI data centers to differentiate between workloads and adapt system configurations to meet specific performance or energy targets for each task. For example, video encoding or batch processing tasks can be run in energy-efficient modes, while real-time inference tasks can use high-performance configurations.
• Goals can also enable client specific SLA’s that are offered within tailored and customized pricing constraints. SLA’s can also be created for fulfillment of client required levels of security or a desired carbon footprint for compute alternatives.
• Goals can also be used to manage latency and computational strategies in clusters of AI data centers, operated as nodes in an advanced mesh system.
• See more here about Goals.

Uniquely, Config’s Sequitur platform enables all the above capabilities in a single package by using our proprietary APIs to specify the relevant goals and configuration parameters.

As Config’s prime market focus, we welcome inquiries about collaborations to build the best, most performant Self-Aware™ and AdaptiveAI™ AI data centers in the world; your designs, together made Self-Aware™ and adaptive.