Start with the workload.

Training a model from scratch, or continuing pretraining at scale, runs for days or weeks across many nodes. Collective communication, data throughput, checkpointing, and node health decide how much of that time becomes real progress.

• Topology matched to your model and parallelism strategy
• RDMA scale-out network and fast checkpoint storage
• Validation with representative multi-node jobs

Fine-tuning trains an existing model on your data for a specific task. Runs are shorter and more frequent, so consistent environments and fast access to capacity matter more than cluster size.

• Repeatable images and runtime environments
• Capacity sized to the run, on instances or bare metal
• Dataset handling agreed up front

Post-training goes beyond a single task to shape how a model behaves, often across many experiments at once. Evaluation decides which version is good enough to release, so it belongs in the same workflow.

• Shared capacity across research and product teams
• Scheduling and quotas for many concurrent experiments
• Evaluation tied to model versions and release decisions

Request shape, context length, concurrency, and response targets determine both the serving architecture and what it costs to run.

• Traffic and latency profiling
• GPU memory, batching, and cache tuning
• Clear ownership of endpoints, releases, and operations

Scientific AI combines simulation, learned models, very large datasets, and repeatable experiments. ARC provides infrastructure and research collaboration for scientific programs, including cell and life-science research.

• Compute and data paths matched to scientific workflows
• Reproducible environments for interdisciplinary teams
• Domain-specific evaluation and data governance