When to Use Bare-Metal GPUs vs. Cloud GPU Instances

Cloud GPU instances from the hyperscalers have made ML training accessible to any team with a credit card, but the convenience comes at a price premium that many organizations underestimate.

Cloud instances excel in a specific set of use cases: short-duration experiments, inference serving with variable traffic, and workloads where the flexibility to scale to zero matters more than per-hour cost. For a team running occasional fine-tuning jobs or prototyping model architectures, on-demand instances are often the right choice.

But cloud instances are a poor fit for sustained training workloads. Training a foundation model for weeks at a time on cloud instances can cost 3-5x more than equivalent bare-metal capacity, because you are paying for the cloud provider's abstraction layer, multi-tenancy overhead, and margin — none of which improve your training throughput. You also face noisy-neighbor effects that introduce variance in training time, and availability constraints that can interrupt long runs.

At NewMachine, we help clients draw this line clearly. Our bare-metal GPU clusters deliver dedicated, non-contended compute with full InfiniBand bandwidth and direct storage access. For sustained training jobs longer than a few days, the total cost of ownership is typically 60-70% lower than equivalent cloud instances. For everything else, we support hybrid architectures that burst to cloud instances for experiments while running production training on bare metal. The hybrid approach gives our clients the best of both worlds without the complexity of managing two entirely separate infrastructure stacks.