Multi-Tier Pricing System (MTP)
The Multi-Tier Pricing System (MTP) is StarMiner’s dynamic pricing framework for GPU computation. It replaces static billing models and unpredictable auction mechanisms with a programmable pricing engine that adjusts based on real-time network conditions, task parameters, and node performance.
MTP ensures that computing power is priced fairly, distributed efficiently, and remains accessible to a broad range of users from budget-conscious researchers to enterprise-grade AI labs.
Why MTP Exists
In decentralized compute networks, one of the biggest challenges is pricing diversity:
Not all tasks require the same performance
Not all providers offer equal infrastructure
Not all users can tolerate the same latency or reliability
MTP solves this by establishing multiple pricing tiers and applying real-time adjustments to match compute supply with demand, based on context, not just cost.
Core Pricing Variables
MTP calculates pricing using a blend of on-chain and off-chain inputs:
Hardware Class
Higher-performing GPUs (e.g., A100, H100) command premium pricing tiers.
Nodes with verified TEEs or sustainable energy credentials may also be priced higher.
Task Urgency
Tasks marked as time-sensitive (e.g., real-time inference, priority training) are charged higher AGPU rates for prioritized routing.
Network Load
When the network is congested or GPU availability is low, MTP increases pricing to reflect scarcity.
During off-peak periods, AGPU requirements are reduced to incentivize demand.
Job Type Complexity
Tasks involving multi-GPU parallelism, long runtimes, or memory-intensive models are factored into the tier curve.
Geographic Routing Preferences
Users may choose lower-cost regions for less urgent jobs, or pay more for localized execution (e.g., jurisdictional compliance, data sovereignty).
Tier Structure Overview
StarMiner maintains three main pricing tiers:
Economy
Low-latency tolerance, flexible timing
Batch inference, render queues
Low
Standard
Balanced SLA and latency expectations
Model training, real-time prediction
Medium
Premium
Fastest routing, guaranteed resources
Enterprise workloads, high-priority AI
High
Each tier operates with predictive AGPU price bands that shift based on demand similar to cloud spot pricing, but transparently encoded on-chain.
Price Discovery and Execution
When a task is submitted, it is automatically categorized and assigned to a pricing tier.
The MTP engine determines the current price band based on:
Active network demand
Supply in that tier
Node availability and historical performance
The task is accepted only if the user has sufficient AGPU balance to meet tier-specific pricing.
If tier conditions change mid-job, pricing remains locked for the duration of execution, avoiding surprises or volatility mid-compute.
Benefits of the MTP System
Fairness: Ensures that users only pay for what they actually use based on urgency and hardware.
Market Efficiency: Routes resources toward the highest-value use cases without central interference.
Predictability: Users know in advance what AGPU cost bands to expect for their job class.
Incentive Alignment: Encourages nodes to upgrade hardware or improve uptime to qualify for higher-paying jobs.
DAO Control and Future Customization
MTP parameters (e.g., tier thresholds, region multipliers, SLA scoring weights) are governed by AMAX AI token holders. This allows the protocol to:
Adjust incentive curves
Introduce domain-specific tiers (e.g., biomedical compute, defense)
Integrate sustainable compute discounts or compliance-based pricing structures
Through governance, the MTP system can evolve into a flexible economic engine, tailored to meet the changing needs of AI and decentralized compute infrastructure.
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