Neocloud: On-demand infrastructure for the AI age
A neocloud is a specialized cloud provider that focuses on high-performance computing (HPC) and AI-intensive workloads, offering on-demand access to powerful hardware like GPUs. They are an evolution of cloud computing designed for the AI era, often providing more specialized, flexible, and potentially more affordable GPU capacity compared to traditional “hyperscale” cloud providers. Neoclouds act as a middle layer between hyperscalers and data center operators, serving needs like AI model training, inference, and large-scale data analysis.
What is GPU-as-a-Service (GPUaaS)?
To eliminate the substantial initial investments associated with hardware acquisition and the complexities inherent in maintaining physical GPU infrastructures, a cloud-based solution known as GPU-as-a-Service (GPUaaS) has emerged.
GPU-a-as-Service model offers both individuals and organizations on-demand access to Graphics Processing Units, thereby facilitating the utilization of high-performance computing resources. Such cloud services are particularly significant in deploying machine learning applications, where computational demands are often substantial.
Large-scale artificial intelligence (AI) models typically necessitate extensive computational workloads characterized by the parallel processing of tasks. This is essential for efficiently executing applications at the edge. GPU-as-a-Service model enables small enterprises to implement AI systems without the financial burden of procuring and maintaining hardware.
The flexibility of this cloud service permits users to select configurations that align optimally with their specific workload requirements, coupled with a pay-as-you-go pricing model. Furthermore, the deployment of cloud-based GPUs allows for the rapid provisioning of resources, which in turn accelerates project deployment and reduces time-to-market for various applications.
GPU-as-a-Service for LLMs
With the growing interest in large language models (LLMs), which demand considerable computational power for training due to their extensive parameter sizes and complex architectures, GPUs play an important role in these processes. However, the continuous operation of such GPUs can lead to significant costs.
GPU-as-a-Service addresses this challenge by providing on-demand access to powerful GPUs, allowing organizations to train LLMs without incurring significant hardware investments. Additionally, this model enhances scalability, as training LLMs frequently require distribution across multiple GPUs to handle the substantial data and computations involved.
Central to the GPU-as-a-Service framework are advanced cloud infrastructure and virtualization technologies. This cloud service permits cloud operators to provide multiple users with access to GPU resources from virtually any location, relying upon internet connectivity. Given the virtualized nature of these GPUs, a single unit can be divided into multiple virtual instances, enabling simultaneous utilization by multiple users without interference.
Difference between GPU Cloud and NeoCloud
- Focus: A GPU cloud provides a diverse range of GPU options suitable for various computing tasks, while NeoCloud is a more AI-centric version of the GPU cloud, specifically designed to deliver high-performance GPUs tailored for AI and machine learning workloads.
- Customization: Users have limited customization options with traditional GPU clouds, whereas NeoCloud offers extensive customization capabilities for tailored hardware and software stacks to meet specific needs.
- Use Cases: The applications for GPU clouds can be broad, including general AI tasks. In contrast, NeoCloud is primarily focused on large-scale AI training and real-time edge inference.
- Service Providers: Notable providers of GPU clouds include AWS, Google Cloud, and Azure, while NeoCloud providers include Crusoe, CoreWeave, Nebius Group, and Lambda.
Emerging market
According to Matt Bamforth, a senior consultant at STL, the GPU-as-a-Service market is still in its early stages. Amidst the buzz around generative AI, enterprises are exploring various GPU options that align with their specific use cases while also being cost-effective.
In this nascent phase of large language models (LLMs), companies are uncertain about the best solutions available. The recent attention on open-sourced DeepSeek generative AI comes from its development being significantly less expensive than OpenAI’s GPT. Much of the cost savings could be associated with the efficient use of GPUs. It will be interesting to see the role of GPU-as-a-Service in the expanding landscape of generative AI and LLMs.
Featured Neocloud providers:
1623 Farnam
1623 Farnam is the leading network interconnect point providing secure direct edge connectivity to fiber and wireless network providers
365 Data Centers
365 Data Centers is a leading provider of hybrid data center solutions in thirteen edge markets.
42U
42U Data Center Solutions has been providing server solutions for over 25 years, with 140,000+ clients
A5G Networks
A5G Networks is providing autonomous 4G, 5G, WiFi converged packet core that enables efficient edge deployments.
Aarna Networks
Aarna Networks solves enterprise edge and private 5G management complexity through zero-touch edge orchestration at scale.
Featured News: Juniper unveils AI-focused networking solution to speed GPUaaS deployments
Juniper Networks, a provider of secure network solutions, introduced a solution for GPUaaS and AIaaS providers to accelerate AI delivery, simplify operations, and reduce costs.
The solution includes QFX Series Switches, PTX Series Routers, and SRX Series Firewalls, managed via Juniper Apstra and Mist AI, offering high-performance, scalable, and secure networks for AI workloads.
Automation features reduce deployment time by up to 10x and operational costs by up to 85%, with additional benefits for Kubernetes environments through Red Hat OpenShift integration.
Zero Trust Security and multi-tenancy capabilities protect AI infrastructure, models, and data, with advanced firewall throughput and encryption for data in motion.
“Managed AI services, such as GPUaaS and AIaaS, have been growing rapidly,” says Praveen Jain, Senior Vice President & General Manager, Data Center & AI, Juniper Networks. “To capture the demand, neocloud, traditional SPs and other AI cloud providers need to move fast and deliver exceptional value. Juniper is excited to deliver a purpose-built solution for these providers.”
Juniper’s Ops4AI Lab and Validated Designs (JVDs) enable providers to validate AI models and deploy multi-vendor AI solutions. The solution avoids vendor lock-in, supports open technologies, and offers flexibility with multi-vendor support and scalability up to 1.6 Tbps/port switches.
Juniper leads the AI data center market with a 49% share in the 800G Ethernet switch market and powers networks for major AI cloud providers. By enabling efficient, secure, and vendor-agnostic AI infrastructure, Juniper strengthens real-time AI processing at the edge, driving the next wave of AI innovation.
