Running Responsible AI Compliance Checks at Scale on Cloud TPU: A Practical Tutorial for vLLM Batch Inference

This tutorial shows how to use Cloud TPU v5e and vLLM batch inference to turn RAI compliance checks (supporting three rules: PII detection, jailbreak identification, and bias checking) from a sequential bottleneck into a scalable parallel pipeline. It is suitable for scenarios such as large-scale model output compliance audits and real-time dialogue system security filtering.

With the widespread application of LLMs, RAI compliance checks are crucial, but traditional sequential execution processes are limited in speed and cannot meet large-scale production needs (e.g., tens of millions of outputs per day from a dialogue system with millions of daily active users). The ByteanAtomResearch team's open-source tutorial provides a TPU+vLLM batch inference solution to address this issue.

Architecture: Input → Prompt Construction → vLLM TPU Batch Inference → JSON Results → Report Generation; supports both offline batch and online API paths.

Tech Stack: Cloud TPU v5e-4 (significant cost advantage), vllm-tpu package (requires installation via uv pip), Gemma4 model (native JSON output + guided decoding to eliminate parsing errors), rai-checklist-cli integration.

Three Rules: 1. PII detection (phone numbers, emails, etc.); 2. Jailbreak identification; 3. Bias detection (gender/race stereotypes, etc.).

Key Details: XLA compilation cache (20-30 minutes for the first run, seconds to start subsequent runs); batch prompt construction (50 records ×3 rules →150 prompts processed in batch); structured output ensures correct formatting.

Result Example: v5e-4 cold start throughput:8-12 items per second; utilization increases as batch size grows.

Report Format: metadata (timestamp, model, etc.), summary (statistics per rule: number of violations, passes, parsing errors), results (detailed verdict for each record).

Alternatives: Google Colab free TPU (quota limited) or Kaggle Notebooks (30 hours of free TPU v3-8 per week).

Usage: The project is divided into 4 modules (setup, offline_batch, online_server, integration_demo); common commands: make verify (environment verification), make batch (offline batch), make serve (online service), etc.

Value:1. Production-grade reproducible solution;2. TPU batch inference is more cost-efficient than GPU;3. Structured output eliminates parsing uncertainty;4. Dual modes cover offline/online scenarios.

Scenarios: Large-scale compliance audits, real-time dialogue security filtering, AI-generated content detection, enterprise AI governance processes.

This tutorial systematically demonstrates how TPU+vLLM batch inference transforms RAI compliance checks into a scalable parallel pipeline, reducing time and cost.

Project link: https://github.com/ByteanAtomResearch/compliance-at-scale-tpu