Target Verticals
USE CASES
MIRASET connects AI inference demand with shared GPU resources. Explore application prototypes, worker participation, and enterprise evaluations, with signed usage records and planned PoCC incentives.
VERTICALS
- 01
AI Startups
Problem
Growing inference demand makes dedicated GPU infrastructure costly to maintain.
Solution
Explore shared GPU inference without buying a dedicated fleet. Final prices and access terms are not confirmed.
Example
A startup tests a text-generation feature on shared workers before committing to its own hardware.
Key Features
- Model selection based on available workers
- Usage records for cost analysis
- Signed receipts for worker attribution
SharedGPU access - 02
Indie Developers
Problem
Cannot afford $10K+ GPU setups or expensive hourly cloud rentals for hobby projects.
Solution
Build AI features using shared workers instead of owning a high-end GPU. Access depends on model and capacity availability.
Example
A solo developer prototypes a chatbot using a model available on the network.
Key Features
- No dedicated GPU purchase
- Model and hardware compatibility checks
- Inference through the application flow
PrototypeAI features - 03
Decentralized Apps
Problem
Decentralized applications need access to AI inference without running a GPU service themselves.
Solution
Request inference through MIRASET's application flow and keep signed usage records. There is no smart-contract integration.
Example
An app requests a moderation suggestion through its application layer and reviews the response before acting.
Key Features
- Application-level inference requests
- Signed receipts for usage accounting
- No contract-triggered jobs
AIapplication layer - 04
Enterprise AI
Problem
Scaling AI without capital expenditure while meeting compliance and audit requirements.
Solution
Evaluate shared inference capacity and usage records. Production terms, data handling, and compliance must be assessed separately.
Example
A team benchmarks a non-sensitive text workload before deciding whether the service meets its requirements.
Key Features
- Worker capability and model checks
- Signed usage records for review
- Separate privacy and service-readiness evaluation
Evaluatebefore deployment
CHOOSE YOUR COMPUTE MODEL
| Feature | Own hardware | Cloud rental | Miraset |
|---|---|---|---|
| GPU ownership | Buy and maintain | Provider-owned | Worker-provided GPUs |
| Cost basis | Hardware + electricity | Provider-specific rates | Final terms pending |
| Supply incentives | Your own capacity | Provider-managed | Planned PoCC capacity + compute rewards |
| Usage records | Your own monitoring | Provider reports | Signed worker receipts |
| Job assignment | Self-managed | Provider-managed | Permissioned MVP coordinator |
| Availability | Your hardware limits | Provider terms | Subject to release readiness |