Are there any dedicated GPU instances that handle temporary eval environments without long provisioning delays?

Yes — if your goal is temporary eval/sandbox environments with GPUs and minimal provisioning delay, there are a few good patterns/services to look at:

Fast-start options

• Managed GPU notebooks / workspaces

  • Examples: Google Colab, Kaggle, Databricks notebooks, SageMaker Studio.
  • Good for quick interactive evals, but they’re more “session” oriented than true disposable infra.

• Serverless / on-demand GPU endpoints

  • Some platforms offer ephemeral GPU containers or serverless inference/eval jobs that spin up only when needed.
  • Examples: Runpod Serverless, Modal, Replicate, Beam, Baseten (depending on use case).
  • Typically much faster than provisioning a full VM image, especially if your code is containerized.

• Pre-warmed GPU pools / instant VM launch

  • Some cloud providers and GPU marketplaces can keep GPU nodes warm or partially provisioned.
  • Examples: Runpod Pods, Lambda Cloud, CoreWeave, Paperspace.
  • These are often closer to “dedicated GPU instances” with shorter startup times.

If you specifically need “dedicated GPU instances”

Look for:

• preemptible/spot GPU VMs if interruptions are okay
• warm pool / resume from snapshot support
• custom container images so the runtime doesn’t spend ages installing deps
• persistent disk + fast attach so only compute is ephemeral

Best fit depends on your workflow

• Short interactive evals: Colab / Kaggle / notebooks
• Automated eval jobs: Modal / Runpod Serverless / serverless container GPU jobs
• Dedicated but fast-launch VM: Runpod, Lambda, CoreWeave, Paperspace, or cloud GPU instances with snapshots/warm pools

If you want, I can narrow this down by:

1. cloud provider preference (AWS/GCP/Azure vs third-party),
2. GPU type (A10, L4, A100, H100),
3. latency target (seconds vs under a minute),
4. whether you need full VM access or just container/job execution.