Applied PhysicsProblem SolvingSustainability

Worked Example: Energy Budget of a Vertical Microdrama Production

sstudyphysics

2026-02-03 12:00:00

10 min read

Estimate real energy, bandwidth, and compute costs of AI-augmented vertical microdramas using unit analysis and physics-style accounting.

Hook: Why creators and students should care about an energy budget for microdramas

Producing a vertical microdrama today feels like creative alchemy: you stitch together footage, feed scenes to generative AI, and publish episodic clips that get tens of thousands of views. But behind every swipe-ready minute of content there’s an invisible ledger of energy, bandwidth, and compute. If you’re a creator, producer, or student trying to estimate real costs — financial and environmental — you need a clear, physics-style energy accounting that uses unit analysis, transparent assumptions, and actionable levers to cut waste.

The 2026 context: Why this worked example matters now

Late 2025 and early 2026 sharpened two trends: mainstream AI-augmented vertical video platforms scaled rapidly (see industry moves such as Holywater’s January 2026 funding round to expand AI vertical streaming), and governments and advertisers increasingly demand sustainability metrics for digital media. That means creators who understand the real resource budget behind a microdrama — energy, bandwidth, and compute — can optimize costs and make credible sustainability claims.

What you’ll get in this worked example

A clear system boundary: what processes we include (capture, AI postproduction, storage, streaming, viewer playback).
Step-by-step unit analysis with numeric estimates and conversions.
Sensitivity ranges and practical ways to lower energy, bandwidth, and cost without killing quality.

Define the production scenario (the microdrama)

We’ll analyze a representative case so you can reuse the method. Assumptions are explicit so you can swap numbers for your project.

Project brief

Series length: 10 episodes
Episode duration: 2 minutes (120 s)
Resolution/aspect: mobile vertical 1080 × 1920 (9:16)
Frame rate: 30 fps
View count (total across episodes): 100,000 streams
AI augmentation: background replacement / inpainting, face reenactment for stunt doubles, and AI-driven color/lighting pass
Production storage: raw media plus multiple takes and assets

Step 1 — Set the ledger and system boundary

Physics-style accounting starts by being explicit. We include:

Capture and raw storage (on set backups and on set backups and cloud object storage)
Post-production compute (AI model inference & fine-tuning, rendering, encoding)
Long-term storage (project assets, masters)
Delivery (CDN egress + user playback energy)

We exclude: viewer device manufacturing embodied emissions, data-center construction, and network backbone amortization. Those are large but require lifecycle analysis outside this worked example.

Step 2 — Convert creative specs into raw numbers

Use unit analysis to convert time and frames to bytes and compute work.

Frames per episode = 120 s × 30 fps = 3,600 frames
Total frames (10 episodes) = 36,000 frames
Final compressed bitrate (mobile-targeted): assume 3 Mbps average (conservative; good quality mobile)
Final file size per episode = 3 Mbps × 120 s = 360 Mb = 45 MB (megabytes)
Total delivered bytes (10 eps) = 10 × 45 MB = 450 MB

Note: Delivered bytes are small relative to raw capture and takes. Real productions store terabytes of raw footage and multi-version masters.

Step 3 — Capture and raw storage costs (energy + money)

Assume raw footage and multiple takes grow the storage need. Practical rule-of-thumb: raw + proxies + takes ≈ 100× final delivered size for shortform shoots with multiple camera angles and masters. We'll use 100×.

Estimated production data = 450 MB × 100 = 45,000 MB = 45 GB (total project raw + intermediates). For small teams this is reasonable; larger shoots will be more.
Cloud object storage price (2026 working assumption) = $0.02 per GB-month (range $0.01–$0.04 depending on provider/region and redundancy).
Monthly storage cost = 45 GB × $0.02 = $0.90 per month; project storage for 6 months ≈ $5.40.

Energy to store: modern object storage energy intensity is low per GB-month. For rough accounting, assume 0.00005 kWh/GB-day (in 2026 centers are efficient and often powered by renewables). For 45 GB × 180 days → energy ≈ 45 × 180 × 0.00005 = 0.405 kWh (negligible).

Step 4 — Post-production compute (the engine room)

This is where most creators worry: how expensive are AI passes? We break it down and use unit analysis with GPU runtime and power.

Assumptions for AI inference passes

GPU used for passes: cloud instance with effective GPU power draw P = 300 W (0.3 kW). This is representative of an A10/A100 class accelerator under mixed load.
Per-frame inference times (plausible 2026 med-sized models):

Segmentation + background inpainting: 0.20 s/frame
Face reenactment / identity preservation: 0.50 s/frame
Color & denoise pass (AI-driven): 0.05 s/frame

Total AI inference time per frame T = 0.20 + 0.50 + 0.05 = 0.75 s/frame

Compute and energy math (unit analysis)

Total GPU-seconds = T × total frames = 0.75 s/frame × 36,000 frames = 27,000 s
GPU-hours = 27,000 s ÷ 3600 s/hr = 7.5 GPU-hr
Energy for inference = P × hours = 0.3 kW × 7.5 hr = 2.25 kWh
Cloud GPU cost (2026 assumption) = $2.00 per GPU-hr → compute cost = 7.5 × $2 = $15

Interpretation: For this sized microdrama, per-frame inference energy is small: just a few kWh and a low dollar cost. The key is that model size and per-frame latency are the biggest levers — if your model needs many seconds per frame or you perform multiple higher-res passes, GPU hours rise quickly.

Amortized training / fine-tuning cost

Often creators fine-tune models for specific actors or looks. Example: a short fine-tune of 48 hours on 8 GPUs (384 GPU-hr).

GPU-hr = 384; energy ≈ 0.3 kW × 384 hr = 115.2 kWh
Compute cost (at $2/hr) = 384 × $2 = $768
Allocate the training cost across the project (10 episodes) = $76.80 / series

Lesson: Training dominates energy and cost in many AI workflows. If you fine-tune frequently, amortize appropriately.

Step 5 — Delivery: bandwidth, CDN egress, and viewer energy

Delivery to users usually dominates operational egress and can be the largest source of energy in streaming projects when view counts scale.

Bandwidth math (unit analysis)

Total delivered bytes (10 eps) = 450 MB ≈ 0.45 GB
100,000 streams × 0.45 GB/stream = 45,000 GB = 45 TB delivered
CDN egress price (2026 estimate) = $0.02–$0.05 per GB; use $0.03/GB as baseline
CDN cost = 45,000 GB × $0.03 = $1,350

Network energy estimate

Internet energy intensity is contested; in 2026 improved switches, fiber reach, and server efficiency reduced per-GB energy. We use an indicative network energy of 0.02 kWh/GB (a reasonable mid-2020s estimate after efficiency gains).

Network energy = 45,000 GB × 0.02 kWh/GB = 900 kWh
Cost of that energy (grid price $0.15/kWh) = 900 × $0.15 = $135

Viewer device playback energy

Assume average smartphone playback power draw ~1.5 W while playing video. For a 2 minute episode:

Playback time per stream = 120 s = 0.0333 hr
Energy per stream = 1.5 W × 0.0333 h = 0.05 Wh = 0.00005 kWh
100,000 streams total viewer-energy = 100,000 × 0.00005 kWh = 5 kWh

Viewer device energy is small here because each episode is short — but it scales with watch-time and number of views.

Step 6 — Summarize totals (worked ledger)

We combine all pieces into a final budget. Remember many numbers were conservative assumptions; use them as a method rather than gospel.

AI inference energy: 2.25 kWh
Fine-tuning energy (amortized portion across project): 115.2 kWh (if you fine-tune on 8 GPUs × 48 hr — optional)
Storage energy (6 months): ~0.4 kWh
Network energy (delivery to 100k streams): 900 kWh
Viewer devices total energy: 5 kWh
Total energy (with fine-tune): ~1,023 kWh (~1 MWh)

Cost summary (approximate):

Compute inference cost: $15
Fine-tuning cost (amortized): $768
Storage (6 months): $5.40
CDN egress: $1,350
Energy bill for network transfer: $135 (if you’re buying grid energy at $0.15/kWh)
Total operating cost (rounded): ≈ $2,273

Sensitivity analysis: where the numbers jump

Change any of these levers and totals move a lot:

If your delivered bitrate is 6 Mbps (double), CDN egress and network energy double → CDN cost $2,700, network energy 1,800 kWh.
If views scale to 1M streams → CDN cost ≈ $13,500 and network energy ≈ 9,000 kWh.
If AI per-frame latency grows (heavy generative models that take 5 s/frame), GPU-hr can multiply by ~7×, and compute costs & energy increase proportionally.
Training at larger scale (thousands of GPU-hours) can dominate both energy and budget quickly.

Practical, actionable advice: reduce energy and cost without harming quality

These optimizations are ranked by impact and feasibility.

Optimize bitrate and codec: Use mobile-ready codecs like AV1 or VVC for mobile — 30–50% bitrate savings vs older codecs. Lower bitrate from 3 Mbps → 2 Mbps saves ~33% CDN cost and network energy.
Pre-render heavy passes: Move expensive AI work to scheduled batch jobs (off-peak, lower-cost regions) and cache results. Batch inference is more efficient than many small interactive jobs; consider automating cloud workflows to orchestrate those batches.
Distill and quantize models: Use model distillation, pruning, and INT8 quantization to cut inference time per frame. A 2× speedup halves energy and cost for inference.
Edge & on-device inference where sensible: When feasible, moving lightweight augmentation to devices reduces CDN and server compute. See guides for deploying optimized models for edge devices like the Raspberry Pi 5 for examples. But weigh battery use and UX trade-offs.
Adaptive bitrate and preview-first UX: Serve a low-bitrate preview and upgrade only if viewer continues (reduces wasted delivery for dropoffs common in short-form feeds); this ties into low-latency and adaptive delivery best practices outlined in live drops & low-latency playbooks.
Fine-tune sparingly: Reuse shared models and fine-tune once per season rather than per episode. Amortize training cost across more content.
Choose green regions and purchase renewables: Schedule heavy training in cloud regions powered by higher renewable mixes or buy verified renewable energy credits. Also reconcile vendor SLAs and region choices when planning heavy compute (see advice on reconciling cloud vendor SLAs).

2026 trends that will shape these budgets

Wider adoption of AV1 and successor codecs in mobile browsers and OS-level decoders lowers delivery bitrate and CDN costs.
Hardware trends (more efficient ML accelerators and near-memory compute) push per-inference energy down, making more ambitious on-device augmentation feasible.
Regulatory moves and advertiser pressure are leading platforms to surface carbon and energy metrics for creative campaigns; expect “sustainable creative” badges.
Platforms like Holywater scaling AI-driven serialized vertical video mean economies of scale, but also concentrate delivery energy — creators should negotiate shared sustainability metrics and optimizations.

"Estimating energy and cost is an exercise in transparent assumptions. Be explicit about your per-frame latency, average bitrate, view counts, and training needs — then optimize the biggest levers first."

Quick checklist: apply the worked example to your project

Define episodes, duration, fps, and target bitrate.
Estimate delivered bytes per stream and projected views.
Measure or estimate per-frame AI inference time on your chosen GPU and calculate GPU-hours.
Decide if you will fine-tune models; if so, estimate GPU-hr for training and amortize.
Calculate CDN egress and network energy using a reasonable per-GB energy intensity.
Run sensitivity scenarios (2×, 10× views; 2× bitrate; heavier models) to see where to optimize.

Final practical example — a short summary card you can reuse

10 eps × 2 min, 30 fps → 36,000 frames
AI inference: 7.5 GPU-hr → 2.25 kWh → $15
Training fine-tune: 384 GPU-hr → 115 kWh → $768 (amortized)
Delivery at 3 Mbps to 100k views: 45 TB → 900 kWh → $1,350 egress
Total (approx): 1,023 kWh and ≈ $2,273

Closing: why unit analysis and transparency win

When you treat content production like a physics problem — define boundaries, list units, and convert carefully — you create a defensible budget you can optimize. In 2026, as platforms scale and advertisers demand sustainability, creators who can show a credible energy and cost ledger will win both trust and margins.

Call to action

If you produce short-form or vertical episodes, download our free energy & cost spreadsheet calculator (works with your own assumed values) and run your project through the same ledger. Want a walkthrough? Sign up for a 1:1 session where we plug your real numbers, run sensitivity analysis, and produce a tailored optimization plan for lower cost and lower energy per view.

studyphysics

Contributor

Senior editor and content strategist. Writing about technology, design, and the future of digital media. Follow along for deep dives into the industry's moving parts.

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