Define the workload first
A GPU choice is not a technical preference; it is the output of a workload definition. Training and fine-tuning are long-running, memory-intensive and often multi-GPU jobs, while inference is short, frequent and latency-sensitive. The same model name implies a different server configuration in these two scenarios.
Write the definition down: which model family, which precision, which batch size, which data volume and which completion target. Once these five fields are filled in, the candidate list narrows on its own and the discussion moves from product names to measurable requirements.
- Will you run training, fine-tuning or inference only?
- Do model weights and working data fit into a single GPU memory space?
- What is the acceptable completion time or response latency?
- Will the workload run continuously or be triggered periodically?
GPU model decision table
The table below places PortvMind Cloud GPU / AI Compute models against workload profiles. Each row shows which workload the model fits.
| GPU model | Primary workload | Memory approach | When to evaluate |
|---|---|---|---|
| NVIDIA H100 | Large-scale AI training and HPC | High-bandwidth data center memory | When a broad training and HPC range must run on one platform |
| NVIDIA H200 | Memory-intensive training and large-context inference | Expanded memory capacity and bandwidth over the H100 architecture | When the model or context does not fit in GPU memory and the job must be partitioned |
| NVIDIA L40S | Inference, visualization and mixed AI workloads | General-purpose accelerator memory | When running continuous inference, media processing or multi-tenant services |
A GPU alone is not enough: the resource chain
The speed of an AI workload is set by the slowest link in the chain, not by the GPU. If data loading is CPU-bound, if the dataset arrives slowly over the network, or if checkpoints cannot be written to storage, the GPU sits idle. A faster GPU then raises cost without shortening runtime.
After selecting the GPU model, size CPU cores, system memory, persistent storage and network capacity in the same table. PortvMind Cloud GPU / AI Compute can relate GPU resources to Compute, VKE/Kubernetes and S3-compatible Object Storage inside one workload architecture, so the chain can be planned as a single architectural decision.
- CPU and RAM: can data preparation, augmentation and loader processes keep the GPU fed?
- Storage: do checkpoint writes and dataset reads stall the training step?
- Network: does inter-node transfer become the bottleneck in multi-node training?
- Runtime: does the job run continuously, or is the resource allocated periodically?
Close the decision with a representative trial
Close the selection with a representative trial that uses your own model and your own data. General comparisons leave out your pipeline, precision and batch behaviour. A representative trial ties the decision to a measurable result.
Record not only total runtime but also GPU utilization, memory occupancy and pipeline wait times. If GPU utilization is low, the problem is not the GPU model but another link in the chain, and that link should be fixed before the selection is changed.
- Run the trial with the production data pipeline and precision settings.
- Record GPU utilization, memory occupancy and wait time together.
- If the target is missed, change the bottleneck first and the GPU model second.
- Store the decision note together with its date, configuration and measurement result.
Frequently asked questions about AI and GPU
Which GPU model should I choose for an AI workload?
It depends on workload type and memory requirement. H200 is evaluated for memory-intensive training and large-context inference, H100 for a broad training and HPC range, and L40S for continuous inference and visualization-heavy scenarios. Close the decision with a representative trial on your own data.
Should I start GPU selection from memory?
Yes. If model weights, activations and working data do not fit into GPU memory, the job either fails to run or must be partitioned. Memory requirement determines which models stay on the list before any performance discussion.
Does a more powerful GPU always deliver a faster result?
No. If the workload is CPU-, storage- or network-bound, the GPU waits idle and a faster GPU will not shorten runtime. Measure GPU utilization first; if it is low, the bottleneck is elsewhere in the chain.
Can the same server configuration serve training and inference?
It can, but it is rarely the most efficient option. Training is long-running and memory-intensive, while inference is short, frequent and latency-sensitive. Measure utilization and latency targets separately before running both on one configuration.
Should you prefer cloud GPU or a physical server?
For short experiments, periodic training and variable inference demand, cloud access can reduce hardware procurement and operations effort. For continuous, predictable high utilization, physical investment should be modelled against total cost of ownership.
Research sources
Technical explanations are based on the primary sources below.
- NVIDIA H100 Tensor Core GPU
NVIDIA's positioning of H100 architecture and its intended AI and HPC workloads.
- Publisher
- NVIDIA
- Type
- Official technical documentation
- Accessed
- NVIDIA H200 Tensor Core GPU
NVIDIA's description of H200 memory design and memory-intensive target workloads.
- Publisher
- NVIDIA
- Type
- Official technical documentation
- Accessed
- NVIDIA L40S GPU
NVIDIA's positioning of L40S for inference, visualization and mixed workloads.
- Publisher
- NVIDIA
- Type
- Official technical documentation
- Accessed
- NVIDIA Nsight Systems
Official tooling documentation for measuring GPU utilization and pipeline wait times.
- Publisher
- NVIDIA Developer Documentation
- Type
- Official technical documentation
- Accessed