What Is Neocloud? - Cisco

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What is neocloud?

Neocloud providers offer specialized, high-performance infrastructure designed to power AI workloads. They combine public cloud elasticity with dedicated GPU acceleration.

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What are neocloud providers?

The term neocloud refers to specialized cloud infrastructure providers dedicated to AI workloads. By leveraging high-performance hardware compute accelerators—primarily GPUs—these providers support the diverse and demanding AI application needs of any organization, including enterprises, model builders, and hyperscalers.

Neocloud platforms accommodate the entire AI lifecycle, from large-scale training and fine-tuning to inference, offering flexible consumption models such as on-demand access, reserved instances, and platform-as-a-service.

As AI adoption accelerates, neocloud providers address the need for the elasticity of public clouds as well as the performance characteristics of dedicated AI infrastructure.

AI cloud infrastructure models: Cloud vs. hyperscaler vs. neocloud

As AI moves from experimentation to production, the cloud market has evolved into three distinct models. While they often coexist in a multicloud strategy, they serve very different technical and business needs.

Traditional cloud

Built on general-purpose, CPU-centric architectures, these traditional cloud providers, whether managed or regional, prioritize abstraction and multi-tenancy. While highly flexible, the virtualization overhead can introduce 'hypervisor tax' and networking bottlenecks that hinder massive AI training jobs.

Hyperscalers

Hyperscalers refers to the massive global cloud providers (such as AWS, Azure, and GCP) that deliver global scale and integrated services. To meet AI demand, they now offer specialized consumption models:

Reserved instances : Fixed-term commitments for dedicated AI stacks, offering lower costs for steady-state workloads like sustained inference.<br>Serverless AI (PaaS) : Managed platforms that abstract infrastructure entirely, allowing developers to pay by token or request.<br>The trade-off : While convenient, their general-purpose roots may not always match the raw, deterministic performance of a purpose-built AI fabric.

Neoclouds

Neoclouds are built from the ground up for GPU-as-a-service. They prioritize raw performance and hardware visibility over broad service catalogs.

AI-first architecture : Utilizing dense GPU clusters and high-performance fabrics like RDMA and RoCE to handle massive "east-west" traffic.<br>Performance edge : By offering bare-metal access and 400G/800G networking, they provide the ultra-high bandwidth and predictable latency required for the fastest possible model training cycles.

As organizations navigate these choices, the priority remains consistent: ensuring that AI infrastructure remains performant, secure, and easy to manage regardless of where the GPUs live.

How neocloud works: Technical pillars of neocloud infrastructure

To deliver deterministic performance, neocloud infrastructure move away from the high abstraction of general-purpose clouds, focusing instead on three tightly integrated layers:

1. AI-optimized compute

Neoclouds prioritize raw throughput by minimizing the "hypervisor tax."

Bare-metal/minimally virtualized servers : Ensures maximum GPU access.<br>High-density nodes : Typically 4–8 GPUs per node (e.g., NVIDIA H100/B200) to support massive parallel processing.<br>Hardware visibility : Provides deeper visibility into hardware topology, allowing for better tuning of frameworks like PyTorch or TensorFlow.

2. High-performance AI networking

Networking is the primary differentiator of a neocloud platform. Neocloud providers typically deploy two distinct networks:

A front-end network : Standard Ethernet for management and user access.<br>A high-performance back-end/fabric network : A lossless Ethernet or InfiniBand specifically for GPU-to-GPU synchronization.

This dual-network architecture is a defining characteristic of a true AI cluster. Because AI training relies on constant synchronization (collective communication), the fabric must handle massive "east-west" traffic with zero packet loss.

High-bandwidth fabrics : Leveraging 400G and 800G Ethernet to prevent data bottlenecks between nodes.<br>Low-latency topologies : Utilizing non-blocking Spine-Leaf architectures for near-linear scaling of the cluster.<br>Advanced protocols : Implementing RDMA (Remote Direct Memory Access) and RoCE, allowing GPUs to communicate directly with each other’s memory to bypass CPU overhead and reduce latency.

3. Disaggregated and secure infrastructure

Modern neocloud infrastructure uses a modular design to maintain agility and security.

Independent scaling : Compute, storage, and networking scale separately based on workload demand.<br>Open standards : Frequent use of open...