Neocloud refers to a specialized cloud computing environment focused on delivering high-performance computing (HPC) capabilities, particularly GPU-as-a-Service (GPUaaS), to support artificial intelligence (AI), machine learning (ML), and other computationally intensive workloads. These providers differentiate from traditional hyperscale clouds by emphasizing raw compute power, low-latency networking, and flexible access to advanced GPUs.
Key Characteristics of Neoclouds
- AI-first focus. Infrastructure is purpose-built for AI and HPC workloads, typically featuring top-tier GPUs and accelerators.
- Optimized infrastructure. High-speed interconnects such as NVLink or InfiniBand enable rapid communication between GPUs.
- Flexible deployment and access. Customers gain fast access to GPU capacity without long procurement cycles.
- Transparent pricing. Pricing structures are often simpler and more predictable for intensive GPU usage.
- Scalability and performance tuning. Workloads can be scaled efficiently while maintaining low-latency performance.
Examples of Neocloud Providers
- CoreWeave. A prominent GPU cloud provider serving AI and VFX workloads, backed by major industry partnerships.
- Lambda Labs. Offers cloud-based and on-premises GPU infrastructure with a strong focus on developers and research.
- Crusoe. Emphasizes sustainability by powering GPU data centers with stranded or renewable energy.
- Nebius. Provides AI infrastructure with a focus on data sovereignty, primarily serving European markets.
Implications for Data Center Professionals
Neocloud adoption affects physical infrastructure planning and operational oversight across hybrid environments.
- Distributed compute visibility. Teams must track infrastructure and workloads across cloud, colocation, and on-premises environments.
- Network performance management. Ensuring high-bandwidth, low-latency connectivity to GPU clusters becomes a critical requirement for AI training.
- Capacity and power planning. Growth in AI workloads often increases on-premises power density demands, even as some compute shifts to neoclouds.
- Cost and efficiency governance. Monitoring GPU utilization, energy consumption, and spend across providers requires unified reporting.
Where DCIM Plays a Role
Even as organizations incorporate neocloud compute into hybrid architectures, on-premises and colocation facilities remain essential for core business systems, regulated data, and workloads that depend on local processing. Data center teams must continue operating these environments efficiently and ensuring they evolve alongside changing workload demands.
To support this operational foundation, DCIM software enhances visibility and provides useful insight in several ways:
- Inventory and workload alignment. DCIM software helps maintain accurate records of where assets reside and how capacity is being utilized, enabling more informed decisions about what to run locally versus in the cloud.
- Power and cooling management. As densities evolve, DCIM software provides insight into power draw and thermal conditions that inform safe and efficient placement of new hardware.
- Network documentation. DCIM software maps physical network connectivity, which supports troubleshooting and performance planning for links into external AI infrastructure.
- Capacity planning. DCIM software provides data that supports decisions about expanding on-premises resources or shifting workloads to alternative environments.
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