24–32 GB class
L4, A10, RTX 4090 or RTX 5090. Consider for smaller models only after measuring total memory, including runtime and cache.
AI training
AI training: model selection, memory requirements, storage and deployment choices without invented performance claims.
Choose a configuration in Console to review its resources and estimated cost.
Training stores activations, gradients and optimizer state in addition to model weights. Dataset throughput and checkpoints also matter.
Measure a representative batch. Multi-GPU training requires an explicit interconnect and distributed setup; this version checks single-GPU offers.
L4, A10, RTX 4090 or RTX 5090. Consider for smaller models only after measuring total memory, including runtime and cache.
L40S, A40, A100 or H100. More memory may accommodate larger working sets; it does not guarantee concurrency or token speed.
H200, B200 or B300 and explicitly sized multi-GPU systems. Confirm exact model, precision, topology and serving framework.
Weight-only memory ≈ parameters × bytes per parameter. Runtime, KV cache, activations and optimizer state are additional. This is a sizing method, not a deployment recommendation.
Check the current price and configuration in Console. Your choice is saved through registration. The current lookup uses mock adapters; no real capacity is reserved.
Compare GPU memory and architectures, from RTX to data-center accelerators, then check configurations in one console.
Explore →Plan infrastructure for model hosting, inference, training and fine-tuning with explicit memory and workload requirements.
Explore →Compare object, block and file storage, backups and snapshots by access pattern, durability needs and restore workflow.
Explore →Cloud backup: access model, capacity planning and recovery considerations before choosing a configuration.
Explore →Hardware and model documentation; these sources do not confirm rental availability.
Official documentation 1 →