A100 vs H100 vs consumer GPUs: which should you rent?
Choose a rental GPU using model memory, measured throughput and the cost of finishing your workload—not the hardware name alone.
· Arvica Cloud · Analysis & buying guide
Which GPU should I shortlist?
Start with a configuration that can hold your workload at the required precision and context length. A smaller workload may justify a consumer GPU trial; a workload that needs more device memory or multi-GPU communication deserves a data-centre configuration trial. Request the exact SKU, usable memory, GPU count, CPU, RAM, storage and interconnect. NVIDIA lists distinct H100 form factors and specifications; an H100 label alone is not a complete server quote. [1]
Is the faster card always cheaper to use?
Compare the price of completing the same accepted job. For example, a configuration that needs four GPU-hours at rate A costs 4A; one that needs two GPU-hours at rate B costs 2B — the second is cheaper whenever B is less than twice A. Repeat the same workload, precision and quality checks on both configurations, then add setup time and other charges. For inference, compare cost at your response-time target and expected concurrency, not only maximum tokens per second.
Can several smaller cards replace one larger card?
Only if the model and serving or training framework support the required partitioning. Treat memory fit and speed as separate acceptance checks. Communication, CPU resources and the exact topology can change the outcome. Ask for a representative multi-GPU test before paying for a long commitment; adding cards is not a guarantee of proportional speed.
What should I include in a quote request?
Send the model name and revision, training or inference task, precision, peak concurrency or batch size, required location, GPU count if known, start date and rental term. Ask Arvica to compare feasible configurations against those requirements. Confirm availability, commercial terms and trial acceptance in the written quote.
Download an editable worksheet to agree scope, compare configurations and decide whether to stop, adjust or scale. An evaluation method, not a customer case study.
Compare scope, allocation, storage, networking and pilot acceptance before committing to a B300 node. Includes a worked cost formula, not a supplier price.
Build a representative inference test with model, precision, context, concurrency and quality targets. Connect the measured result to a transparent GPU budget.
