Neocloud
Cloud & Sovereign ComputeA neocloud is a specialized cloud computing provider focused on delivering high-performance GPU infrastructure for artificial intelligence, machine learning, and other compute-intensive workloads. Unlike traditional cloud providers that offer a broad range of general-purpose services, neoclouds prioritize GPU computing capacity, high-speed networking, and infrastructure optimized for AI training and inference.
Eleveight AI Neocloud gives teams on-demand access to NVIDIA B300 GPUs, alongside CPU instances, shared storage and managed Kubernetes, without buying or maintaining their own hardware.
Overview
A neocloud is a cloud infrastructure provider built primarily around the computational demands of artificial intelligence and high-performance computing. These providers typically offer access to advanced GPUs, large-scale GPU clusters, high-bandwidth memory, and specialized networking infrastructure. While traditional hyperscale cloud providers such as AWS, Microsoft Azure, and Google Cloud support a wide variety of applications, neoclouds concentrate on delivering GPU-intensive computing environments.
This specialization allows neocloud providers to optimize their infrastructure for workloads requiring substantial parallel processing power, including large language model training, generative AI, scientific simulations, and large-scale inference. Neocloud services commonly include dedicated GPU servers, on-demand GPU instances, scalable GPU clusters, and infrastructure management capabilities.
How it works
Neocloud providers operate AI factories equipped with high-performance GPU servers, fast storage systems, and high-bandwidth networking. Customers access these computing resources remotely, typically through cloud platforms, APIs, or infrastructure management interfaces.
When an organization requires GPU computing capacity, the neocloud allocates the appropriate resources based on workload requirements and available infrastructure. For example, an AI company training a large language model may require multiple interconnected GPUs. A neocloud can provide a cluster of GPU servers, allowing the company to distribute computational workloads across multiple devices.
Technologies such as containerization, orchestration, and GPU scheduling help manage resource allocation, workload deployment, and infrastructure utilization. Depending on the provider, customers may rent GPUs on demand, reserve dedicated capacity, or deploy workloads on larger GPU clusters.
Why it matters
Neoclouds play an important role in expanding access to high-performance AI computing infrastructure. Training and deploying advanced AI models require significant computational resources, specialized hardware, and substantial infrastructure investment.
By providing GPU capacity as a service, neoclouds allow organizations to access advanced computing technology without the upfront costs and operational complexity of building their own GPU infrastructure. Their specialization can also provide competitive advantages in GPU availability, infrastructure configuration, performance optimization, and pricing, depending on the provider and workload.
Use cases
Use cases
- Training large language models and generative AI systems
- Deploying high-performance AI inference services
- Renting dedicated GPU computing capacity
- Running distributed machine learning workloads
- Supporting AI startups and research organizations
- Performing scientific simulations and high-performance computing
- Scaling GPU infrastructure without purchasing physical hardware