Introduction
As the demand for artificial intelligence (AI) continues to grow, organizations are facing a significant challenge in managing their computing resources to meet the needs of their AI workloads. With the increasing power of compute, it’s essential to optimize resource utilization to ensure efficient and cost-effective deployment of AI models. In this article, we’ll explore the concept of multi-tenancy in computing and its benefits in managing AI workloads.
As Compute Gets Increasingly Powerful
As compute gets increasingly powerful, the fact of the matter is: most AI workloads do not require the entire capacity of a single GPU. Computing power required across the model development lifecycle looks like a normal bell curve – with some compute required for data processing and ingestion, maximum firepower for model training and fine-tuning, and stepped-down requirements for ongoing inference.
Despite that reality, organizations will continue making significant investments in compute because they cannot fully optimize existing resources. Splitting and slicing GPUs is one way to tackle the issue of getting more from each chip (if they know how to do it). By harnessing the power of fractional GPUs, AI infrastructure managers can pre-determine partition sizes for their teams and require stakeholders to use the slice best-suited for the size of their job. With the right setup and process, teams can improve workload throughput up to a factor of 10, significantly speeding up the timeline for model development and reducing time to production. But while fractional GPUs are a fantastic solution for teams with models in different stages of development or production, when managing multiple teams using the same shared infrastructure, you need a more macro-level control mechanism.
Multi-tenancy in Computing
In computing, multi-tenancy is a software architecture where a single instance of an application (or in this case, a computing resource) serves multiple individual user groups, often referred to as tenants. It offers a way to share resources efficiently amongst those users.
What Is Secure Multi-tenancy?
Since multi-tenancy is implemented with efficiency and security in mind, complete network separation is a vital aspect. Each tenant should operate within their own distinct network, safe from the potential threats of other tenants. By isolating each tenant on their own network, organizations can guarantee the utmost privacy and data integrity, allowing tenants to fully utilize their allocated GPU resources without any concerns of unauthorized access or interference. With complete network separation, multi-tenancy GPU clusters offer an unrivaled level of control and protection, giving tenants and the infrastructure owner peace of mind.
The Benefits of Multi-tenancy
- Cost Effectiveness and Transparency– Investing in cloud compute, whether on-prem or cloud, is a significant line item in the IT budget. Sharing existing compute amongst more users is an easy way to stretch resources without additional investment. Granular usage-level reporting can also show how each tenant consumes compute, data, and microservices, making it easier for organizations to do internal billing (chargebacks) to their AI stakeholders.
- Higher Utilization – Despite most organizations believing that they need more computing firepower, in a recent survey of 1,000 IT leaders, 68% of respondents estimated GPU utilization to be under 70% (or even under 50%!) during peak periods. Consider the lost opportunity for more AI development, not to mention the inefficient ROI. To maximize the utilization of those GPUs, infrastructure engineers should layer in fractional GPU capabilities to right-size computing power for AI workloads in addition to multi-tenancy. Through well-setup partitions, it’s possible to do “smaller jobs” such as data pre-processing or inference on the same chip.
- Data Security – Multi-tenancy ensures no data leakages between tenants. No one wants R&D’s models made visible to Sales (except the sales team, of course). Multi-tenancy is another mechanism to secure data by creating silos for isolated computing and data storage connections.
- Scalability – When your infrastructure is already set up for sharing, it is easy to shift resources around because you can see who is fully using their compute allocation versus teams that do not. The elasticity of your infrastructure makes it simple to “rack and stack.” Adding more clusters or additional cloud providers to increase computing power overall is an easier task and will not affect users with system downtime.
Not all Multi-tenancy is Created Equal
True multi-tenancy controls access to each tenant’s own isolated network, storage, and compute allocations. It’s important to ensure tenants are secured and cannot access the control plane that maintains the silos.
Conclusion
Infrastructure leaders embarking on the multi-tenancy journey for their AI infrastructure have many considerations and challenges for security and resource optimization, but successful implementation will ultimately increase utilization and ROI. For large organizations with centrally-managed compute, it’s a no-brainer.
Frequently Asked Questions
Question 1: What is multi-tenancy in computing?
Multi-tenancy in computing is a software architecture where a single instance of an application (or in this case, a computing resource) serves multiple individual user groups, often referred to as tenants. It offers a way to share resources efficiently amongst those users.
Question 2: What are the benefits of multi-tenancy?
The benefits of multi-tenancy include cost effectiveness and transparency, higher utilization, data security, and scalability. It allows organizations to share resources efficiently, increase utilization, and improve ROI.
Question 3: How does multi-tenancy ensure data security?
Multi-tenancy ensures data security by creating silos for isolated computing and data storage connections. Each tenant operates within their own distinct network, safe from the potential threats of other tenants.
Question 4: Can multi-tenancy be implemented with fractional GPUs?
Yes, multi-tenancy can be implemented with fractional GPUs. By harnessing the power of fractional GPUs, AI infrastructure managers can pre-determine partition sizes for their teams and require stakeholders to use the slice best-suited for the size of their job.
Question 5: How does multi-tenancy improve scalability?
Multi-tenancy improves scalability by allowing organizations to shift resources around easily. With complete network separation, it is simple to add more clusters or additional cloud providers to increase computing power overall without affecting users with system downtime.
