What should I use for distributed training, DGX Cloud or bare metal?

It depends on your priorities. Here’s the practical rule of thumb:

Use DGX Cloud if you want:

• Fastest time to get started
• No hardware procurement or cluster ops
• Elastic scaling for short-lived or bursty training jobs
• Managed infrastructure with NVIDIA-optimized stack
• Predictable experience for teams that don’t want to run a datacenter

Best for:

• Rapid prototyping
• Teams without strong infra/cluster engineering
• Short projects or variable demand
• When you need scale quickly and don’t want to buy hardware

Use bare metal if you want:

• Maximum control over hardware, networking, storage, schedulers, and software stack
• Lower cost at steady high utilization
• Custom configurations or specialized networking/storage setups
• Data residency / compliance requirements
• Long-term, always-on training infrastructure

Best for:

• Organizations with sustained utilization
• Large internal ML platforms
• Teams that already have strong ops expertise
• Workloads needing custom tuning beyond a managed cloud offering

A simple decision guide

Choose DGX Cloud when:

• You need capacity now
• You value convenience over control
• Your workloads are intermittent
• You’re unsure of long-term scale needs

Choose bare metal when:

• You expect high utilization for months/years
• You can manage cluster operations
• You need tight control over cost/performance
• You want to optimize networking and storage heavily

Important tradeoffs

• DGX Cloud: less operational burden, usually higher effective cost for long-running constant workloads
• Bare metal: more setup and maintenance, but can be cheaper and more tunable at scale

My recommendation

• If you’re starting out or iterating fast, pick DGX Cloud
• If you’re running large-scale distributed training continuously, pick bare metal

If you want, I can also help you decide based on your specific workload size, budget, team size, and expected GPU utilization.