Vibeleaderboard

Where should I rent GPUs for AI inference or fine-tuning?

For bursty inference, serverless GPU with per-second billing wins because you pay nothing while idle. For sustained training, reserved instances are far cheaper. Cold start time is the specification to compare first.

Surveyed 13 August 2026

Choose compute and GPUs

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No.Tool
  1. 01
    Lambda

    On-demand and reserved GPU cloud. GPU cloud for training and inference — on-demand instances, multi-GPU clusters, and reserved capacity. Billed per GPU-hour with no egress fees.

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  2. 02
    RunPod

    Per-second GPU pods and serverless. Rent GPU pods by the second across Community and Secure Cloud, or run autoscaling serverless GPU endpoints. Popular for cheap training and batch jobs.

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  3. 03
    Vast.ai

    GPU rental marketplace. Marketplace where independent hosts rent out GPUs, usually well below hyperscaler rates. Interruptible bidding trades reliability for the lowest price.

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  4. 04
    Modal

    Modal is a serverless cloud platform for AI and data teams — run Python functions, GPU workloads, agents, sandboxes, and scheduled jobs in the cloud by adding a decorator, with no infrastructure to manage.

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  5. 05
    Baseten

    Dedicated model deployments on GPUs. Deploy and autoscale models on dedicated GPUs with a managed serving stack. Billed per GPU-minute of active capacity.

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  6. 06
    CoreWeave

    Large-scale GPU infrastructure. Kubernetes-native GPU cloud built for large training and inference fleets. On-demand and reserved capacity across current-generation accelerators.

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Ordered by the VibeLeaderboard index, re-surveyed each edition. Something missing?

What to look for

  • 01Serverless or reserved? Duty cycle decides this, and the cost difference is large in both directions.
  • 02How long is a cold start? Loading a large model can dominate latency for infrequent requests.
  • 03Is the memory enough for your model at your precision, with room for the batch?

Common questions

Is it cheaper to run inference myself or use an API?
Hosted APIs are cheaper until utilization is high and steady. Self-hosting pays off with sustained volume, strict data-residency requirements, or a fine-tuned model no API offers.
What GPU memory do I need?
Roughly two bytes per parameter at 16-bit, half that at 8-bit, a quarter at 4-bit, plus overhead for context and batch. A 7B model at 4-bit fits comfortably in 8GB.

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