Can I run training and inference on the same GPU setup?

Yes — you can run both training and inference on the same GPU setup, but there are a few tradeoffs and constraints.

When it works well

• Single model / light load: You can train a model and also run inference jobs on the same GPU(s), especially if inference is occasional or low-throughput.
• Separate processes: Training and inference can run as separate processes or services, sharing the same GPU resources.
• Batching/queueing: If you schedule inference requests between training steps or during idle periods, this can work reasonably well.

Main limitations

• GPU memory contention: Training usually uses much more VRAM than inference. If training nearly fills the GPU, inference may fail or slow down.
• Performance interference: Inference can slow training, and training can make inference latency unpredictable.
• Resource isolation: Without careful orchestration, one workload can starve the other.

Common approaches

• Time-slicing: Run training at certain times, inference at others.
• Multi-GPU split: Dedicate some GPUs to training and others to inference.
• MIG / partitioning: On supported NVIDIA GPUs, you can partition the GPU to isolate workloads.
• Containers / schedulers: Use Kubernetes, Slurm, or Docker-based orchestration to manage contention.

Practical advice

If you want both on the same GPU:

1. Start by measuring VRAM usage for training.
2. Leave headroom for inference and CUDA context overhead.
3. Prefer smaller inference batch sizes.
4. Monitor latency, utilization, and OOM errors.
5. If needed, use a second GPU or separate inference endpoint for reliability.

If you tell me your GPU model and workload type, I can suggest the best setup.