What do we mean by ‘AI native’?

As with so many of today’s AI-related buzzwords, ‘AI native’ means different things to different people. 

Many people ascribe the notion to human beings, either individually or in organizations, as Mark Zuckerberg’s notorious failure to make Meta AI native would attest.

A more useful definition of the term might apply to the hardware and software that AI runs on: designing infrastructure from the ground up for AI workloads.

For any organization building out such infrastructure, whether it be an enterprise equipping its own data centers or one of the numerous neoclouds springing up, ensuring their efforts are AI native is a top of mind concern.

For either type of organization, AI native infrastructure means far more than a data center full of GPU-equipped servers. Here’s what’s missing.

Designing infrastructure from the ground up for AI workloads

To build out AI native infrastructure, the starting point is unquestionably filling a data center with GPU-enabled servers, as well as all the supporting technology they require (power, cooling, and networking in particular).

Such infrastructure at the hyperscale that AI requires costs money. Lots of it.

As a result, both enterprises and neoclouds require an efficient use of capital, which in the case of GPUs, means high levels of efficient utilization – GPU utilization that delivers high-throughput, low-latency compute.

Neoclouds in particular, however, often make the mistake of focusing exclusively on GPU utilization. It’s true that utilization ties directly to profit – but without the rest of the AI infrastructure story, it will be difficult to sustain utilization goals.

It is for this reason that the AI native story is so important, both for neoclouds as well as enterprises deploying AI-centric data centers. 

Understanding what makes infrastructure AI native is the key for profitability for neoclouds, and the central enabler of efficient use of capital for all organizations building out AI data centers.

The eleven key characteristics of AI native infrastructure

Such an understanding, therefore, requires ten eleven characteristics that define AI native infrastructure:

1. Efficient scheduling and workload placement – high-throughput, low-latency compute requires GPU-aware workload scheduling for rapid scaling. This scheduling must support training, inferencing, and dynamic agentic workloads. Workload placement and scaling should also be model-aware.
2. Fast movement of data – the network should never be a bottleneck. AI native infrastructure requires high-bandwidth networking and storage access, as well as efficient GPU-to-GPU and GPU-to-storage transfers that minimize data loading and synchronization bottlenecks.
3. Support for AI-specific elasticity requirements – AI native infrastructure must be able to dynamically provision GPUs and other AI acceleration technologies while prioritizing heterogeneous workloads and handling different utilization patterns (for example, both bursty inference traffic as well as long-running training jobs that require high availability).
4. AI-specific orchestration of infrastructure components – AI native infrastructure must understand the full gamut of AI-centric assets, including models, agents, datasets, pipelines, MCP servers, and the rest of the AI toolbox. 
5. Efficient inference – AI native infrastructure should support dynamic batching and key-value cache management. It should also base autoscaling on tokens per second and latency metrics, not merely on GPU utilization.
6. Multi-level observability – system observability is necessary but not sufficient. AI native infrastructure also requires AI-level observability. Such telemetry includes metrics around model latency, cost per token, model pipeline bottlenecks, requirements drift, in addition to traditional utilization and memory metrics.
7. Reliability and fault tolerance – given the scale of AI native data centers, GPU failure is a normal, everyday occurrence. AI native infrastructure must therefore provide automated job recovery, failure-aware scheduling, replica management, and sophisticated checkpointing techniques. AI orchestration in particular should also support automated recovery from failed nodes as well as individual GPUs, without requiring the restart of any training workloads.
8. Security and governance – For AI infrastructure to be AI native, security and governance should be an integral part of the infrastructure, including bulletproof tenant isolation, identity management, fine-grained and purpose-specific access control, as well as data lineage and auditability capabilities.
9. Comprehensive cost optimization – AI native infrastructure must include continual monitoring and optimization of any metric that can lead to excess costs and include the proper mitigations, including energy efficiency, power-aware scheduling, workload right-sizing, and fine-grained cost attribution.
10. Monetization (neoclouds) or chargeback infrastructure (enterprises) – Neoclouds must bill for AI services, while enterprises require the ability to charge back various departments for internal AI capabilities. AI native infrastructure must be able to provide token-metered, self-service AI capacity and associated services, including granular usage metering. Neoclouds also require billing and CRM integration as part of their AI native infrastructure.
11. A working abstraction that supports developer requirements – the best infrastructure is technology that developers can rely upon without needing to know the details, and the same is true of AI native infrastructure. To be fully AI native, therefore, the infrastructure should be declarative, with reproducible environments and an appropriate cloud native architecture.

In conclusion: to make a GPU-based data center AI native, deploy a control plane layer that transforms the underlying GPU infrastructure into a self-service, multitenant, monetizable (or capital efficient) cloud service from a company like Rafay.

The Intellyx Take

Regardless of whether an organization is an enterprise building out its own data center or a neocloud looking to provide GPU resources to its customers, deploying AI native software infrastructure on top of the GPU hardware layer is the difference between efficiently supporting the AI requirements of end-users vs. pouring money into a black hole.

As the length of the list of requirements above suggests, deploying AI native infrastructure is a complicated task. There are many elements that must work together to avoid bottlenecks and deliver value on invested capital.

Selecting a control plane vendor like Rafay is a critical step in building such cost-effective infrastructure. Rafay provides the configurable GPU utilization, multitenancy capabilities, and a self-service user experience that abstracts the substantial complexity of the AI native environment, thus enabling both neoclouds and enterprises to squeeze the most value from their substantial AI infrastructure investments.

Copyright © Intellyx BV. Rafay is an Intellyx customer. Intellyx retains final editorial control of this article. A human wrote every word of this article.

Image credit: Craiyon, https://www.craiyon.com/