Problem Description
Design a multi-tenant platform responsible for allocating GPU compute resources, tracking usage, and managing user credits. Users pre-purchase credits and are charged based on their GPU consumption (e.g., per GPU-second). The system must accurately track consumption in near real-time, prevent users from overspending their purchased credits, facilitate credit top-ups, and enforce fair usage policies and rate limits across the entire GPU fleet.
Context
The platform provides access to GPU compute across many nodes. Key characteristics include:
- Multiple GPU types are available (e.g., A100, H100), each with potentially different pricing models.
- User jobs can utilize one or more GPUs.
- The system should support job migration or rescheduling across different GPUs or nodes.
- The system must be resilient to node failures, agent failures, and network partitions, ensuring continued operation with a bounded exposure to inconsistencies or overspending.
Requirements
- Usage Tracking: Accurately track per-user GPU consumption across all nodes and over time.
- Real-time Deduction: Deduct credits in near real-time (seconds-level granularity) while preventing double-spending across distributed nodes.
- Credit Management: Support user credit top-ups (payments) and provide immediate visibility into updated balances.
- Rate Limiting: Enforce various rate limits, such as maximum concurrent GPUs per user, maximum spend rate per second, and daily spending caps.
- Fair Usage: Implement mechanisms to ensure fair usage of resources, preventing any single user from monopolizing or starving others, especially when resources are scarce.
- Fault Tolerance: Design the system to handle outages of nodes, agents, or the underlying ledger, guaranteeing that any potential overspend is bounded.
- Auditable Ledger: Maintain an auditable ledger with idempotent and immutable records. This ledger must support reconciliation between provisional and final charges.
- API Endpoints: Provide APIs for querying user balance, reserving/authorizing credits, consuming credits, performing top-ups, and generating usage reports.
- Scalability: The system must be scalable to support thousands of GPU nodes and tens of thousands of concurrent jobs.
Deliverables
The candidate is expected to provide:
- A high-level architecture outlining key components and their responsibilities.
- A detailed data model for accounts, balances, credit holds/leases, usage events, and pricing.
- A mechanism for real-time credit deduction across multiple nodes, specifically addressing double-spend prevention.
- An approach for implementing rate limiting and fair scheduling.
- A comprehensive strategy for handling failures and reconciling inconsistencies.
- A small numeric example to illustrate the charging and limit enforcement logic.
AI interview prep
Core dilemma
Balancing strong consistency for financial credit deductions and preventing double-spending in a highly distributed, real-time system with the need for high throughput, low latency GPU allocation, and robust fault tolerance.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Problem Description
Design a multi-tenant platform responsible for allocating GPU compute resources, tracking usage, and managing user credits. Users pre-purchase credits and are charged based on their GPU consumption (e.g., per GPU-second). The system must accurately track consumption in near real-time, prevent users from overspending their purchased credits, facilitate credit top-ups, and enforce fair usage policies and rate limits across the entire GPU fleet.
Context
The platform provides access to GPU compute across many nodes. Key characteristics include:
- Multiple GPU types are available (e.g., A100, H100), each with potentially different pricing models.
- User jobs can utilize one or more GPUs.
- The system should support job migration or rescheduling across different GPUs or nodes.
- The system must be resilient to node failures, agent failures, and network partitions, ensuring continued operation with a bounded exposure to inconsistencies or overspending.
Requirements
- Usage Tracking: Accurately track per-user GPU consumption across all nodes and over time.
- Real-time Deduction: Deduct credits in near real-time (seconds-level granularity) while preventing double-spending across distributed nodes.
- Credit Management: Support user credit top-ups (payments) and provide immediate visibility into updated balances.
- Rate Limiting: Enforce various rate limits, such as maximum concurrent GPUs per user, maximum spend rate per second, and daily spending caps.
- Fair Usage: Implement mechanisms to ensure fair usage of resources, preventing any single user from monopolizing or starving others, especially when resources are scarce.
- Fault Tolerance: Design the system to handle outages of nodes, agents, or the underlying ledger, guaranteeing that any potential overspend is bounded.
- Auditable Ledger: Maintain an auditable ledger with idempotent and immutable records. This ledger must support reconciliation between provisional and final charges.
- API Endpoints: Provide APIs for querying user balance, reserving/authorizing credits, consuming credits, performing top-ups, and generating usage reports.
- Scalability: The system must be scalable to support thousands of GPU nodes and tens of thousands of concurrent jobs.
Deliverables
The candidate is expected to provide:
- A high-level architecture outlining key components and their responsibilities.
- A detailed data model for accounts, balances, credit holds/leases, usage events, and pricing.
- A mechanism for real-time credit deduction across multiple nodes, specifically addressing double-spend prevention.
- An approach for implementing rate limiting and fair scheduling.
- A comprehensive strategy for handling failures and reconciling inconsistencies.
- A small numeric example to illustrate the charging and limit enforcement logic.