Industries
GPU computing for every industry.
AI labs, render studios, research teams and enterprises use OpenGPU for affordable GPU capacity on demand.
01 / 11
AI & Machine Learning
Fine-tune, evaluate and serve models without reserving a cluster. Elastic capacity for training runs, OpenAI-compatible inference for production, one network underneath both. Used across healthcare, finance, logistics, manufacturing, automotive and retail.
The challenge
- Capacity is scarce Top-tier GPUs come with waitlists and multi-year commitments.
- Utilization is uneven Training bursts and quiet weeks still get billed as reserved capacity.
- Too many inference vendors Every model provider means another SDK, key and invoice.
With OpenGPU
- Burst on demand Route fine-tuning and eval jobs to available GPUs in minutes.
- Pay per verified task No idle reservations. Proof of Execution before settlement.
- One inference API Relay serves 80+ models through a single OpenAI-compatible endpoint.
Recommended stack
- Inference
- Relay
- Training & fine-tuning
- Client App
- Typical GPUs
- H100, A100, RTX 4090
- Billing
- Per token · per task
- LLM chatbots
- High-QPS inference APIs
- LoRA & QLoRA
- Full fine-tuning
- RLHF / DPO
- Model evaluation
- RAG pipelines
- Embeddings
- Vision models
- AI agents
02 / 11
Generative Media
Image, video, voice and 3D generation at production volume. Call 80+ hosted models through the Relay API, create finished videos with no code in Relay Studio, or run your own pipelines on network GPUs.
The challenge
- Spiky demand A viral launch can 50× generation volume overnight.
- Expensive per-asset cost Video and high-res image models are GPU-hungry.
- Fast-moving model landscape New best-in-class models ship every few weeks.
With OpenGPU
- Scale with your users Routing absorbs spikes across hundreds of providers.
- Lower cost per asset Open models on network GPUs, priced in real time.
- Swap models in one line Image, video and audio models behind one Relay key.
- Relay Studio for creative teams Prompt to finished video, with shot planning, voice, music and editing built in.
Recommended stack
- Hosted models
- Relay API
- No-code creation
- Relay Studio
- Custom pipelines
- Client App
- Typical GPUs
- RTX 4090, H100
- Billing
- Per generation · per task
- Text-to-image
- Image-to-video
- Text-to-speech & voice cloning
- Music generation
- 3D asset generation
- ComfyUI workflows
- Talking-head video
- Video ads & reels
03 / 11
Rendering & VFX
Burst to hundreds of GPUs for a delivery deadline, then scale to zero. Frame-parallel rendering is a natural fit for a distributed network.
The challenge
- Deadline-driven peaks Render farms sit idle for weeks, then can't keep up.
- Capital-heavy hardware On-prem GPUs age fast and depreciate faster.
- Cloud render is costly Hyperscaler GPU hours erode project margins.
With OpenGPU
- Frame-parallel by design Each frame or tile is a task, routed to its own GPU.
- Scale to zero No farm to maintain between projects.
- Verified output Every completed frame is accounted for before settlement.
Recommended stack
- Entry point
- Client App
- Typical GPUs
- RTX 4090, A100
- Engines
- Blender Cycles, Redshift, Octane
- Billing
- Per frame · per task
- Animation rendering
- Architectural visualisation
- Product rendering
- Simulation caches
- Photogrammetry
- Mesh baking & texturing
- FX sims & compositing
- Gaussian splatting
- NeRF
04 / 11
Video Processing
Transcoding, AI upscaling, captioning and content analysis. Split long videos into segments and process them in parallel across the network.
The challenge
- Huge backlogs Archives and UGC libraries grow faster than processing capacity.
- AI enhancement is slow Upscaling and frame interpolation are compute-intensive.
- Unpredictable volume Live events and launches create sudden peaks.
With OpenGPU
- Segment-parallel processing Long-form content split into chunks across many GPUs.
- AI models built in Speech-to-text and vision models through Relay.
- Elastic throughput Clear backlogs in hours, not weeks.
Recommended stack
- Batch pipelines
- Client App
- Transcription & vision
- Relay
- Typical GPUs
- RTX 4090
- Billing
- Per minute processed · per task
- GPU transcoding
- AI upscaling
- Frame interpolation
- Captioning & dubbing
- Content moderation
- Scene detection
05 / 11
Gaming
AI-driven characters, generated assets and faster build pipelines. Give studios GPU capacity that scales with player counts and production schedules.
The challenge
- AI features at player scale LLM-powered NPCs multiply inference costs per player.
- Asset production bottlenecks Texture, model and lighting work slows releases.
- Launch-day spikes Capacity must scale with a hit, then scale back.
With OpenGPU
- Affordable in-game AI Open-weight models on network GPUs keep per-player cost down.
- Generated assets on demand Texture, 3D and voice generation in your pipeline.
- Faster builds Offload lightmap baking and shader compilation.
Recommended stack
- NPC dialogue & voice
- Relay
- Baking & asset jobs
- Client App
- Typical GPUs
- RTX 4090
- Billing
- Per token · per task
- AI NPCs
- Procedural content
- Texture generation
- Lightmap baking
- Voice lines
- Playtesting agents
06 / 11
Scientific Computing
Simulation and research compute without the grant-sized bill. Run parameter sweeps, Monte Carlo methods and ML-for-science across the network.
The challenge
- HPC queues Shared university clusters mean days of waiting.
- Grant budgets Commercial cloud consumes funding meant for research.
- Reproducibility Results need a trustworthy record of how they ran.
With OpenGPU
- No queue Jobs route to available GPUs immediately.
- Research-friendly pricing Pay only for the compute your experiments use.
- Auditable runs Every task is recorded with Proof of Execution.
Recommended stack
- Entry point
- Client App · SDK
- Typical GPUs
- A100, H100, RTX 4090
- Frameworks
- PyTorch, JAX, CUDA, OpenMM
- Billing
- Per task · academic programs
- Molecular dynamics
- Climate & weather models
- Parameter sweeps
- Monte Carlo
- Physics-informed ML
- Astronomy pipelines
07 / 11
Healthcare & Life Sciences
AI-assisted diagnostics, drug discovery, protein structure prediction, genomics and medical imaging. Run large screening campaigns in parallel, with regional deployment options for sensitive data.
The challenge
- Massive screening jobs Thousands of structure predictions per campaign.
- Data sensitivity Patient and proprietary data need strict handling.
- Compute cost slows discovery Every experiment competes for budget.
With OpenGPU
- Embarrassingly parallel Each sequence or compound is its own routed task.
- Data controls Regional deployment, region pinning and zero data retention.
- More experiments per dollar Run wider screens on the same budget.
Recommended stack
- Entry point
- Client App · Enterprise
- Typical GPUs
- A100, H100
- Data residency
- Regional deployment · region pinning
- Billing
- Per task · invoiced
- Protein folding
- Virtual screening
- Genomic variant calling
- Medical image segmentation
- Biomedical LLMs
08 / 11
Financial Services
Risk simulation, strategy backtesting and document intelligence. GPU-accelerated batch compute and private LLM inference for financial teams.
The challenge
- Overnight batch windows Risk runs must finish before markets open.
- Document overload Filings, contracts and reports pile up faster than analysts can read.
- Compliance requirements Data handling and audit trails are non-negotiable.
With OpenGPU
- Wide parallel batch Fan out simulations across hundreds of GPUs.
- Document AI via Relay Extraction, summarisation and structured outputs.
- Full audit trail Every task recorded and verifiable after the fact.
Recommended stack
- Simulation & backtests
- Client App
- Document intelligence
- Relay · regional deployment
- Typical GPUs
- A100, H100
- Billing
- Invoiced · dedicated pools
- Monte Carlo risk
- Strategy backtesting
- Fraud detection
- Risk scoring
- KYC document processing
- Earnings call analysis
09 / 11
Robotics, Automotive & Industry
Autonomous driving, driver assistance, predictive maintenance, computer-vision quality control and route optimization. Generate synthetic data and run simulation at scale for perception and policy models.
The challenge
- Real data is expensive Collecting and labelling edge cases takes months.
- Simulation is GPU-heavy Photorealistic environments need serious compute.
- Iteration speed Every model change means regenerating datasets.
With OpenGPU
- Parallel scene generation Thousands of variations rendered at once.
- Generative augmentation Image and video models via Relay to fill data gaps.
- Fast regeneration Rebuild datasets in hours when requirements change.
Recommended stack
- Simulation & rendering
- Client App
- Generative augmentation
- Relay
- Typical GPUs
- RTX 4090, A100
- Billing
- Per task
- Synthetic image datasets
- Domain randomisation
- Policy training
- Sensor simulation
- Auto-labelling
- Predictive maintenance
- Visual quality control
- Route optimization
10 / 11
Education & Developers
GPU labs for courses, bootcamps and hackathons. Give every student and builder real GPUs without a cloud account per seat.
The challenge
- No GPUs on student laptops Hands-on AI courses need real hardware.
- Per-seat cloud is complex Accounts, quotas and runaway bills for every learner.
- Hackathon spikes Hundreds of teams need GPUs for one weekend.
With OpenGPU
- Shared credits, capped budgets One organization account, per-user limits.
- Ready-made templates Notebooks and model templates to start in minutes.
- Learn by doing Docs, templates and guides to get started fast.
- University courses
- Bootcamps
- Hackathons
- Student research
- Developer workshops
11 / 11
Retail & E-commerce
Personalized recommendations, semantic search, product content and smarter supply chains. AI that scales with seasonal peaks and costs nothing when traffic is quiet.
The challenge
- Seasonal spikes Black Friday traffic is many times a normal week.
- Catalogs never stop growing Every new product needs copy, images and embeddings.
- Thin margins AI features have to pay for themselves.
With OpenGPU
- Elastic by default Capacity follows traffic up and back down.
- Content at catalog scale Descriptions, product images and video ads via Relay and Relay Studio.
- Lower cost per request Open models on network GPUs keep unit costs down.
- Recommendations
- Semantic search
- Product descriptions
- Product imagery
- Video ads
- Demand forecasting
- Support agents
At a glance
Which product fits your workload?
Most teams start with one and add the other as their workload grows.
| Workload | Best entry point | Typical GPUs | Billing unit |
|---|---|---|---|
| Model inference (LLM, image, audio) | Relay | Routed automatically | Per token / generation |
| Fine-tuning & training | Client App | H100, A100 | Per task |
| Rendering & VFX | Client App | RTX 4090, A100 | Per frame / task |
| Video processing | Client App + Relay | RTX 4090 | Per minute / task |
| Scientific batch & simulation | Client App · SDK | A100, H100 | Per task |
| Dev boxes, notebooks, long-running services | Relay Instances | Pick per instance | Per hour |
| Regulated / enterprise | Enterprise · regional deployment | Dedicated pools | Invoiced |
FAQ
Common questions.
How do I pay?
Relay works with card payments and monthly invoices, like any cloud. Enterprise customers can also arrange custom billing.
How is my data protected on third-party GPUs?
Tasks run in isolated containers on providers selected by the protocol. You can pin workloads to regions, and sensitive inference can run in a regional deployment with zero data retention.
How do I know a task actually ran?
Every task goes through Proof of Execution before settlement, and you can follow every task in the Management App.
Can I reserve dedicated capacity?
Yes. Enterprise customers can reserve provider pools with SLAs. Contact sales to plan capacity for your workload.
My industry isn't listed. Can OpenGPU still help?
If your workload runs on GPUs and can be containerized or called via an API, it can run on OpenGPU. Talk to our team and we'll help you benchmark it.
Let's talk
Bring us your workload. We'll benchmark it.
Our solutions team will run your workload on OpenGPU side by side with your current setup.