One work session
$9.52
1 GPU × 8h · compute only
AI training infrastructure
Rent GPUs for LLM pre-training and continued training.
Direct answer
Plan LLM training from parameter count, precision, optimizer state, activations, sequence length, parallelism strategy, checkpoint throughput and measured time to checkpoint. Compare A100, H100, H200 and B200 by total run cost, not theoretical peak specifications.
Reviewed by the GPURento infrastructure team · September 1, 2026
Transparent planning windows
Example rental cost on one A100 80GB.
These are compute rental windows, not predictions of job duration or performance. Replace the hours with a measured pilot and add storage in the calculator.
Forty-hour window
$47.60
1 GPU × 40h · compute only
One-hundred-twenty hours
$142.80
1 GPU × 120h · compute only
| Start with | Peak VRAM | Weights alone understate training memory. |
|---|---|---|
| Measure | Time to checkpoint | Use one fixed model, dataset and quality target. |
| Include | Storage + restart time | They affect total run cost and reliability. |
| Available path | A100 · H100 · H200 · B200 | Each model has a public hourly price. |
Best for
- Pre-training and continued pre-training
- Large-scale transformer training with measured memory
- Multi-GPU training with explicit topology
- Teams that can benchmark a representative step
Not the best fit when
- Unscoped experiments with no budget ceiling
- Jobs that fit a cheaper fine-tuning or inference path
- Runs without checkpoint and failure-recovery planning
Decision method
What to verify before you deploy capacity.
The content below separates published specifications, GPURento catalog references and decisions that still require a workload benchmark.
01
Estimate training memory in layers
Account for weights, gradients, optimizer states, activations, communication buffers and framework overhead. Mixed precision, activation checkpointing and sharding change the result, so every estimate needs safety margin and a short validation run.
- Define parameter count and precision
- Choose FSDP, ZeRO or another state-sharding strategy
- Test peak memory before reserving the full run
02
Design around the checkpoint
The useful unit is often cost per accepted checkpoint. Record samples per second, checkpoint duration, storage throughput, restart time and failure policy. Faster GPUs cannot fix a pipeline blocked by input data or slow checkpoint storage.
03
Match topology to the parallelism strategy
FSDP or ZeRO, data, tensor, pipeline and expert parallelism stress different links. Record tokens per second, communication share and scaling efficiency, then compare cost per checkpoint at the intended GPU count.
Catalog shortlist
Relevant GPU options.
AmpereAvailable
NVIDIA A100 80GB
80 GB HBM2e · $1.19/hr
Training, fine-tuning and HPC
EU West · ParisEU Central · Frankfurt
HopperAvailable
NVIDIA H100 SXM
80 GB HBM3 · $2.69/hr
Intensive training and FP8 inference
EU West · ParisEU Central · Frankfurt
HopperAvailable
NVIDIA H200 SXM
141 GB HBM3e · $3.59/hr
Large-model inference and HPC
EU West · ParisEU Central · Frankfurt
BlackwellAvailable
NVIDIA B200
180 GB HBM3e · $5.98/hr
Frontier training and inference
EU West · ParisEU Central · Frankfurt
Questions
Clear answers, including the limits.
Which cloud GPU is best for LLM training?+
There is no universal winner. A100 can minimize hourly cost, H100 can shorten transformer runs, H200 helps memory-bound jobs, and B200 is a capacity-planning choice. Benchmark the same training step.
How do I estimate LLM training cost?+
Multiply the measured end-to-end hours by GPU count and rate, then add persistent storage, checkpoint overhead and expected retries. Use a pilot run rather than theoretical FLOPS.
Can I train before adding funds?+
Wallet deposits start at $50. Choose the amount to add. This is prepaid credit, not an access fee. Monthly GPU rentals are paid separately at checkout.
Sources and scope
- NVIDIA A100 product pageA100 architecture and 80 GB configuration from the manufacturer.
- NVIDIA H100 product pageArchitecture and memory specifications from the manufacturer.
- NVIDIA H200 product pageH200 memory capacity and bandwidth from the manufacturer.
Last reviewed September 1, 2026. GPURento rates are current catalog references; provisioning state remains visible in the workspace, and manufacturer specifications do not substitute for workload benchmarks.
Continue the research
Deployment plan
Turn the training plan into a capacity request.
Create a workspace, add the required wallet credit and save the GPU count, image, region and storage configuration.