BitCloud Atlas: Plan compute around your models and hardware

Before deploying a model, teams often have some conditions in place. They may have chosen a target model and be looking for suitable hardware. Or they may already have equipment and want to identify models and configurations worth evaluating. Turning these conditions into a deployment plan means considering resource requirements, business workloads and configuration constraints together.

BitCloud Atlas is an AI infrastructure planning platform for model and compute requirements. It brings together capacity assessment, hardware and model selection, and option comparison to help teams understand their choices and the evidence behind them.

Start with the conditions you already know

If you have selected a model, you can assess memory and resource requirements using its precision, context length and expected workload, then compare candidate hardware and deployment configurations.

If you already have hardware, you can use device types, quantities and operating conditions as constraints when considering models and configurations. This provides a starting point for discussions about resource use and expansion.

If both the model and hardware are fixed, you can check capacity, parallelism settings and other deployment conditions, and examine why a configuration fails to meet a constraint and what might need to change.

These three starting points address different planning questions. Teams can begin with the information they have without defining every condition in advance.

Compare options on a common basis

Evaluating a deployment involves more than checking whether model weights fit in memory. Runtime caches, context length and concurrency also affect resource requirements. Device counts, parallelism strategies and requirements for complete replicas can further constrain configuration choices.

Atlas organizes evaluation around capacity and workload, configuration and deployment constraints, and cost and usage conditions. Compare candidate options using consistent inputs, and retain the parameters and sources behind each estimate so that presales, engineering and delivery teams can work from the same facts.

Understand what each result can tell you

Theoretical estimates, official deployment configurations and measured results provide different kinds of evidence. Estimates support initial planning. Official configurations help explain deployment approaches. Measured results must be considered alongside the specific devices, models, versions and test workloads involved.

Atlas distinguishes these sources. A model or device appearing in a catalog does not mean that the combination has been adapted and validated. Before deployment, its availability and behavior still need to be checked in the target environment.

Move from planning to validation

Atlas focuses on selection, capacity assessment and deployment planning. BitTune focuses on environment checks, deployment, tuning and validation. Teams can choose the appropriate tool for the task; these complementary roles do not imply an automated execution chain between the two.

To discuss a plan, begin by gathering the models or devices already selected, the expected context and concurrency requirements, and the problem you need to solve. Available capabilities and access methods depend on the version and deployment environment in use.