Seeking Advice for University Workshop

Spaces
2

I wonder if Hugging Face Spaces is suitable for that purpose…?

Spaces is easy to use if it’s completely public or completely private…

Also, when using Zero GPU Spaces, if the model’s inference time is, say, 10 seconds, setting duration=10 means each use consumes 10 seconds of quota. But if the inference time is 120 seconds, you’d only be able to use it once or twice a day… Pro does extend it, but…

The choice of which service to use really depends heavily on the model’s inference time.:thinking:

Summary recommendation for your purpose

  • Workshop “hero demos” (diffusion / heavy vision / 3D-ish): use PAYG GPU Spaces (dedicated) and shard by duplicating 2–6 copies depending on class size. This avoids per-student credit limits and is the most predictable. (Hugging Face)
  • Optional / exploratory demos: use ZeroGPU Spaces (cheap for you, but variable queue/availability). (Hugging Face)
  • Office future-proofing: keep a Space as the UI, but move heavy inference to Inference Endpoints if you want controlled scaling and a cleaner “service” architecture. (Hugging Face)

Option A — PAYG GPU Spaces (dedicated GPU per Space)

What it is

You pick a hardware tier for a Space (T4/L4/A10G/L40S/etc.). It runs on dedicated hardware with predictable performance. Prices are published as hourly rates. (Hugging Face)

Cost (organizer)

Billing is effectively: hourly_price × hours_running × number_of_copies (charged by minute while running). Paused time is not billed. (Hugging Face)

Common GPU prices (Spaces): (Hugging Face)

  • T4 small: $0.40/hr
  • L4 (24GB VRAM): $0.80/hr
  • A10G small (24GB VRAM): $1.00/hr
  • L40S (48GB VRAM): $1.80/hr

Example budgets (you can scale linearly)

Assume 4-hour workshop time where you keep the GPUs running:

  • 2 copies of an L4 Space: 2 × $0.80 × 4 = $6.40
  • 4 copies of an L4 Space: 4 × $0.80 × 4 = $12.80
  • 2 copies of an L40S Space: 2 × $1.80 × 4 = $14.40
  • 4 copies of an L40S Space: 4 × $1.80 × 4 = $28.80

(Then pause immediately after.) (Hugging Face)

Cost (students)

Usually $0 if they just use your Spaces. They don’t need PRO unless you’re relying on ZeroGPU quotas or their own inference credits. (Hugging Face)

Pros

  • Most reliable workshop experience (you control capacity).
  • Easy scaling: duplicate the Space into 2–6 copies and split the class by links.
  • Avoids “students hit monthly inference credits” if inference runs inside the Space.

Cons / pitfalls

  • You pay while it’s running (so operational discipline matters: pause/sleep time). (Hugging Face)
  • “Thundering herd” cold-start downloads can still hurt if many copies start at once (mitigate with pre-warm + caching). (Hugging Face)

Option B — ZeroGPU Spaces (shared H200 slices)

What it is

A shared GPU pool that dynamically allocates NVIDIA H200 slices on demand; organizations can host these to avoid dedicated GPUs. (Hugging Face)

Cost (organizer)

Compute can be close to $0 for GPU time (you’re not renting a dedicated GPU 24/7), but your ability to host and the practical usability improves with PRO. HF’s PRO plan is $9/month and includes “ZeroGPU quota and highest priority in queues” and the ability to host ZeroGPU Spaces. (Hugging Face)

Cost (students)

  • Free account: can use ZeroGPU Spaces but can face more queue/limits.
  • PRO: $9/month; gets higher ZeroGPU quota and queue priority. (Hugging Face)

Pros

  • Very cost-effective for you for “optional stations” and exploration.
  • Strong GPUs available (H200 slices) without dedicated billing. (Hugging Face)

Cons (important for workshops)

  • Predictability is lower: queueing/availability varies with demand.
  • Compatibility is more constrained than “normal” paid hardware (ZeroGPU is its own mode; your app must fit its constraints). (Hugging Face)

Best use in your workshop: “backup lane” and optional demos, not your core hero demos.

Option C — Inference Providers (serverless routed inference)

What it is

Your app sends inference requests through HF’s router to providers; you pay provider rates (credits apply first). (Hugging Face)

Cost model (students or organizer)

Monthly included credits (shared pool per account): (Hugging Face)

  • Free: $0.10 / month, no pay-as-you-go
  • PRO: $2.00 / month, pay-as-you-go allowed
  • Team/Enterprise org: $2.00 per seat / month, pay-as-you-go allowed

Why students hit limits: $2/month can disappear quickly for bigger models or repeated runs. Real users report confusion over unexpectedly high per-request deductions depending on the model/provider. (Hugging Face Forums)

Pros

  • Very fast to integrate (no GPU infra to manage).
  • Good for lighter tools or when you can accept variable per-request pricing.

Cons (for your workshop)

  • If students authenticate with their own accounts, they can hit credit limits.
  • If you pay centrally, you need guardrails (rate limits, queue, per-user caps).

Best use: non-hero features (captioning, embeddings, small LLM helpers) where failure is acceptable.

Option D — Inference Endpoints (dedicated API, autoscaling)

What it is

A dedicated deployed model behind an endpoint with hourly instance pricing, billed by minute while initializing and running. (Hugging Face)

Cost (organizer)

Endpoint GPU pricing examples (AWS) include: (Hugging Face)

  • L4 x1: $0.80/hr
  • A10G x1: $1.00/hr
  • L40S x1: $1.80/hr
  • A100 80GB x1: $2.5/hr
  • H200 x1: $5/hr

You can set autoscaling min/max replicas; HF’s pricing doc provides formulas and examples. (Hugging Face)

Example workshop cost (endpoint backend)

If you run L4 x1 for a 4-hour workshop with min replicas = 1:

  • 1 × $0.80 × 4 = $3.20 (+ any scale-ups)

If traffic spikes and you scale to 3 replicas for 1 hour:

  • $0.80 × ((4 hours × 1) + (1 hour × 2 extra replicas)) = $0.80 × 6 = $4.80 (Hugging Face)

You’d typically still run a lightweight Space as UI (often CPU). (Hugging Face)

Cost (students)

Usually $0 (they use your UI; you pay endpoint usage).

Pros

  • More “office-grade”: stable API backend, clean separation of UI and inference.
  • Autoscaling handles workshops + client demos better than a single Space.

Cons

  • More setup than Spaces-only (deployment + endpoint config + auth).
  • If you scale-to-zero for cost, you can get cold-start delays (generally undesirable for live workshops).

Option E — External serverless GPU (Runpod / Modal) + your own UI

Runpod

Runpod publishes pay-per-second/per-hour rates; e.g., their pricing page shows GPU hourly pricing such as H200 ~$4.31/hr (plus other GPUs) and also per-request products. (Runpod)

Modal

Modal publishes GPU pricing in per-second terms; e.g., H100 $0.001097/sec (~$3.95/hr). (Modal)

Pros

  • Strong burst scaling; good economics if usage is spiky.
  • More control than HF Spaces in some cases.

Cons

  • More “real cloud” surface area (containers, endpoints, auth, monitoring).
  • More moving pieces on workshop day.

Option F — Student-side compute (Colab) for some activities

Colab paid options include:

Pros

  • Shifts cost/compute away from your infra.
  • Great for “learn by coding” notebooks.

Cons

  • Less consistent performance; resource availability varies.
  • Setup time/support burden in a classroom.

A practical “good options” package with estimated costs

If you want the simplest reliable workshop

  1. PAYG GPU Spaces for 1–3 hero demos

    • Start with L4 ($0.80/hr) for diffusion-ish demos; go L40S ($1.80/hr) if you need 48GB VRAM. (Hugging Face)

  2. Duplicate each hero demo into 2–4 copies and split students by links.

  3. Pause immediately after to stop billing. (Hugging Face)

Estimated organizer cost (typical):

  • Two hero demos, each with 2 copies on L4, 4 hours:

If you want lowest organizer spend (accept variability)

  • Use ZeroGPU for most demos + keep one paid GPU Space as the “guaranteed lane.”
  • Organizer may choose PRO $9/mo for better ZeroGPU quota/priority and ZeroGPU hosting. (Hugging Face)
    Student cost: free or PRO $9/mo if you want them prioritized. (Hugging Face)

If you want to future-proof for your office

  • Keep one Space UI, move heavy inference to Inference Endpoints (L4/A10G/L40S), keep min replica = 1 during live sessions. Pricing is published and scales with replicas. (Hugging Face)
    Student cost: $0.

Pros/cons at a glance (for your use)

Option Organizer cost predictability Student limit risk Workshop reliability Best use
PAYG GPU Spaces High (known hourly) (Hugging Face) Low High Hero demos
ZeroGPU Low/medium (cheap but variable queues) (Hugging Face) Medium (quota/priority) (Hugging Face) Medium Optional demos / backup
Inference Providers Variable (per-request) (Hugging Face) High if students pay Medium Lightweight helpers
Inference Endpoints High (known hourly + autoscaling) (Hugging Face) Low High Office + workshops
Runpod/Modal Medium (provider rates) (Runpod) Low Medium–High (more ops) When you outgrow HF
Colab None (you) (colab.research.google.com) Medium (availability) Medium Coding notebooks