# Seeking Advice for University Workshop

**URL:** </static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fseeking-advice-for-university-workshop%2F173622%3E%5C
**Category:** Spaces\
**Created:** [February 18, 2026, 3:58pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fseeking-advice-for-university-workshop%2F173622 "2026-02-18T15:58:01Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![noahtheboa](https://avatars.discourse-cdn.com/v4/letter/n/35a633/32.png) [@noahtheboa](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fnoahtheboa)%5C
**Post date:** [February 18, 2026, 3:58pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fseeking-advice-for-university-workshop%2F173622%2F1 "2026-02-18T15:58:01Z")

</div>

Hi everyone. I am hosting a workshop for architecture students and am planning to use quite a bit of huggingface spaces to introduce them to what AI can do for architecture and design.

I have intermediate skills and knowledge when it comes to pulling and deploying some of these models and containers etc etc.

i am concerned on hardware limitations and usage limits for my workshop. i have encouraged students to signup for pro but i still worry they will hit their usage limits. I am thinking of duplicating a space or two and just paying for the server so the students can use their limits on some of the less used spaces.

Is it this simple\>? how could i future proof this for my office. i know little about deploying to cloud/server, docker, i am comfortable with CLI, and python but am facing the scale issue.

anything helps

cheers

NK

---

<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [February 19, 2026, 3:02am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fseeking-advice-for-university-workshop%2F173622%2F2 "2026-02-19T03:02:23Z")

</div>

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](https://huggingface.co/docs/hub/en/spaces-zerogpu#duration-management). 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.🤔

* * *

## 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](https://huggingface.co/docs/hub/en/spaces-gpus "Using GPU Spaces"))
- **Optional / exploratory demos:** use **ZeroGPU Spaces** (cheap for you, but variable queue/availability). ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-zerogpu "Spaces ZeroGPU: Dynamic GPU Allocation for Spaces"))
- **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](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))

* * *

## 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](https://huggingface.co/docs/hub/en/spaces-gpus "Using GPU Spaces"))

### Cost (organizer)

Billing is effectively: **hourly\_price × hours\_running × number\_of\_copies** (charged by minute while running). Paused time is not billed. ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))

Common GPU prices (Spaces): ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-gpus "Using GPU Spaces"))

- **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](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))

### 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](https://huggingface.co/pricing "Pricing"))

### 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](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))
- “Thundering herd” cold-start downloads can still hurt if many copies start at once (mitigate with pre-warm + caching). ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))

* * *

## 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](https://huggingface.co/docs/hub/en/spaces-zerogpu "Spaces ZeroGPU: Dynamic GPU Allocation for Spaces"))

### 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](https://huggingface.co/pricing "Pricing"))

### 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](https://huggingface.co/pricing "Pricing"))

### Pros

- Very cost-effective for you for “optional stations” and exploration.
- Strong GPUs available (H200 slices) without dedicated billing. ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-zerogpu "Spaces ZeroGPU: Dynamic GPU Allocation for Spaces"))

### 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](https://huggingface.co/docs/hub/en/spaces-zerogpu "Spaces ZeroGPU: Dynamic GPU Allocation for Spaces"))

**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](https://huggingface.co/docs/inference-providers/en/pricing "Pricing and Billing"))

### Cost model (students or organizer)

Monthly included credits (shared pool per account): ([Hugging Face](https://huggingface.co/docs/inference-providers/en/pricing "Pricing and Billing"))

- 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](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Finference-providers-3-cents-per-request%2F142396 "Inference Providers: 3 cents per request? - Beginners"))

### 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](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))

### Cost (organizer)

Endpoint GPU pricing examples (AWS) include: ([Hugging Face](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))

- **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](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))

#### 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](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))

You’d typically still run a lightweight Space as UI (often CPU). ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))

### 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](https://www.runpod.io/pricing "Pricing"))

### Modal

Modal publishes GPU pricing in per-second terms; e.g., **H100 $0.001097/sec (~$3.95/hr)**. ([Modal](https://modal.com/pricing "Plan Pricing"))

### 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:

- **Colab Pro:** $9.99/month
- **Pay-as-you-go:** $9.99 for 100 compute units ([colab.research.google.com](https://colab.research.google.com/signup "Colab Paid Services Pricing"))

### 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**

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

3. Pause immediately after to stop billing. ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-overview "Spaces Overview"))

**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](https://huggingface.co/pricing "Pricing"))  
**Student cost:** free or PRO $9/mo if you want them prioritized. ([Hugging Face](https://huggingface.co/pricing "Pricing"))

### 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](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing"))  
**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](https://huggingface.co/docs/hub/en/spaces-gpus "Using GPU Spaces")) | Low | High | Hero demos |
| ZeroGPU | Low/medium (cheap but variable queues) ([Hugging Face](https://huggingface.co/docs/hub/en/spaces-zerogpu "Spaces ZeroGPU: Dynamic GPU Allocation for Spaces")) | Medium (quota/priority) ([Hugging Face](https://huggingface.co/pricing "Pricing")) | Medium | Optional demos / backup |
| Inference Providers | Variable (per-request) ([Hugging Face](https://huggingface.co/docs/inference-providers/en/pricing "Pricing and Billing")) | High if students pay | Medium | Lightweight helpers |
| Inference Endpoints | High (known hourly + autoscaling) ([Hugging Face](https://huggingface.co/docs/inference-endpoints/en/pricing "Pricing")) | Low | High | Office + workshops |
| Runpod/Modal | Medium (provider rates) ([Runpod](https://www.runpod.io/pricing "Pricing")) | Low | Medium–High (more ops) | When you outgrow HF |
| Colab | None (you) ([colab.research.google.com](https://colab.research.google.com/signup "Colab Paid Services Pricing")) | Medium (availability) | Medium | Coding notebooks |