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DATASHEET // MODAL

Modal

Serverless Python for GPU workloads.

FREEMIUM16–80 GB VRAMSelf-hosted server
Actually FreeWatermark-FreeHobbyist-OKAPI
Visit ModalUPDATED 2026-05-09 · DIRECT LINK
modal.com
Modal — preview image

HARDWARE REQUIREMENTS //

Self-hosted server · Datacenter GPU (80 GB+)

16–80 GB VRAM
Min VRAM
16 GB
Rec. VRAM
80 GB
Min RAM
16 GB
Rec. RAM
128 GB
Disk
100 GB
GPU class
Datacenter GPU
Provided by their imagesNo Apple SiliconCPU-Capable

T4 → H100 available. B200 in preview.

[ EDITORIAL PICK ]

Why we recommend Modal

DERIVED FROM METADATA — NOT SPONSORED
  • Genuinely free

    Has a free tier you can actually finish a project on, not the 3-credits-then-paywall pattern.

  • Top-tier pick

    Power-user score 85/100 — consistently rated highly by people who use this every day, not just benchmark chasers.

  • Beginner-friendly

    You don't need to read a paper before getting your first result — sensible defaults and a quick install.

  • Hosted API too

    Both self-hostable and available as a hosted API — prototype on someone else's GPU, deploy on yours.

[ EVIDENCE NOTE ]

Documentation-led datasheet

This page summarizes upstream documentation, release information, and editorially reviewed catalogue fields. It is not presented as a hands-on benchmark. Verify changing requirements at the official project; report stale data through our corrections channel.

AT-A-GLANCE SIGNALS //

DERIVED FROM THIS PAGE'S DATA
  • Install difficulty
    Easy

    Runs CPU-only — no CUDA / driver gymnastics required.

  • Hardware comfort
    Enthusiast

    Needs 16 GB minimum — RTX 3090 / 4090 territory.

  • Ecosystem
    API-first

    Exposes a stable API — you can build on top of it programmatically.

  • Verification
    Recent

    Catalogue entry last updated 68 days ago — re-verification due soon.

[ MORE IN THIS NICHE ]

Other orchestration & apis tools we rate

Three picks across different tradeoffs — so you don't end up with three near-clones of Modal.

What is Modal?

Define functions in Python; Modal runs them on-demand on GPUs. Excellent for batched ComfyUI workflows, fine-tunes, and exposing models as HTTPS endpoints with autoscaling.

Pros & cons

✓ PROS

  • Cleanest dev experience for serverless GPU
  • Autoscaling to zero
  • Good cold-start mitigations

– CONS

  • Locked to their SDK
  • Less raw control than a VM

What's actually free?

$30/month free compute credit on signup.

✓ Actually FreeWatermark-Free

Alternatives

RunPod

On-demand GPU pods for ComfyUI, vLLM, training.

PAID8–80 GB VRAM
VRAM fit8–80 GB

Beam

Serverless GPU functions — deploy a Python file, get an HTTPS endpoint.

PAIDCLOUD · NO GPU