EDITOR’ S QUESTION
4. Your maintenance overheads are zero. Installing and running GPUs is just the first step. They need to be maintained and any downtime can be financially crippling. By renting access to GPUs, companies can alleviate the operational burdens and focus entirely on building and scaling their AI models.
The flexibility, cost savings and speed to market make both traditional and bare metal cloud strategic choices for startups looking to grow efficiently and outpace the competition in the fast-moving AI industry.
Choosing the right orchestration tool
Accessing GPUs via bare metal cloud
Bare metal cloud offers the best of both worlds: the raw power of dedicated physical servers with the scalability and automation of cloud computing.
Renting a physical server that provides direct access to GPU hardware instead of relying on virtual machines running on shared hardware can be an alternative option for companies – particularly those prioritizing security, predictability, and customization.
The main features of bare metal cloud are:
1. High performance for workloads needing low latency and high compute power( like AI / ML) 2. Greater security with a dedicated physical server rather than a shared cloud resource 3. Better customization as users can configure the hardware, install specific operating systems and integrate APIs for easy scaling.
LLMs and ML are taking industries to the next level, but with larger, more complex models and datasets, there can be orchestration challenges.
In the case of large-scale GPU workloads deployed in cloud environments, choosing the right orchestration tool is vital for resource and cost efficiency.
To simplify distributed, large-scale training projects, companies can automate the management of computing resources( such as GPUs) across clusters of machines. These can assign workloads to available GPUs, balance computing power across servers, offer scalability based on demand, monitor performance, and detect failures for smoother operations.
There are two main orchestration models – Kubernetes and Slurm – that can handle large-scale GPU projects efficiently and reduce the need for manual management or intervention.
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