What You’ll Build
In about two hours, you will have a content moderation pipeline that screens user-generated text, images, and video thumbnails against your custom policy rules, classifying content as approved, flagged for review, or auto-removed. The system processes 50,000+ text posts and 10,000+ images per hour on a single dedicated GPU server with sub-200ms latency for real-time moderation of live content streams.
Platforms that rely on user-generated content face regulatory pressure and brand risk from harmful material. Manual moderation does not scale, and commercial moderation APIs charge per item while applying generic policies that may not match your community standards. Self-hosted moderation on open-source models lets you define exactly what constitutes a violation on your platform while keeping all user content under your data governance.
Architecture Overview
The moderation system has parallel pipelines for text and visual content, both served from the same GPU. Text moderation uses an LLM via vLLM with your custom policy document embedded in the system prompt. Image moderation uses a vision-language model that classifies images against visual policy categories. Both pipelines return structured verdicts with confidence scores and policy violation citations.
LangChain routes content to the appropriate pipeline based on media type and handles multi-modal posts containing both text and images. A policy engine lets you define category-specific rules: what counts as harassment on a gaming forum differs from a professional network. The system supports tiered actions from warning labels to auto-removal, with confidence thresholds determining when human moderators review borderline cases. Understanding image generation models helps configure visual content classifiers that detect AI-generated content alongside policy violations.
GPU Requirements
| Platform Scale | Recommended GPU | VRAM | Throughput |
|---|---|---|---|
| Small community | RTX 5090 | 24 GB | ~20K text + 5K images/hr |
| Mid-size platform | RTX 6000 Pro | 40 GB | ~50K text + 12K images/hr |
| Large-scale platform | RTX 6000 Pro 96 GB | 80 GB | ~100K text + 25K images/hr |
Text moderation batches extremely well, with the LLM classifying 20+ posts per inference call. Image classification is more compute-intensive per item. Running both text and image models on the same GPU requires careful VRAM allocation. See our self-hosted LLM guide for multi-model deployment strategies.
Step-by-Step Build
Deploy vLLM with a text classification model and a vision-language model on your GPU server. Define your content policy as a structured document that the LLM references during classification. Build the moderation API that accepts content submissions and returns verdicts.
# Text moderation prompt
MODERATE_TEXT = """You are a content moderator. Apply these policies:
{platform_policies}
Classify this user post:
Author context: {user_trust_level}
Content: {post_text}
Return JSON:
{verdict: "approved|flagged|removed",
violations: [{policy: "specific policy violated",
severity: "critical|major|minor",
evidence: "the problematic text span"}],
confidence: 0.0-1.0,
action: "none|warning|hide|remove|ban_recommend"}
Be precise. Do not flag content that is merely edgy but policy-compliant."""The moderator dashboard shows flagged content queued for human review, sorted by severity and confidence. Moderator decisions on borderline cases feed back as calibration examples in the prompt. Build analytics tracking false positive rates, policy violation trends, and moderator agreement rates. Follow our vLLM production guide for low-latency configuration and add a chatbot interface for moderators to query moderation patterns.
Performance and Accuracy
On an RTX 6000 Pro, text moderation processes 50,000 posts per hour with batched inference at 150ms median latency per post. Image classification runs at 12,000 images per hour at 300ms per image. Policy compliance accuracy reaches 93% for clear violations and 82% for nuanced cases like satire and contextual humour. False positive rates stay below 3% after prompt calibration with platform-specific examples.
The system adapts to evolving policies without retraining. Updating the policy document in the system prompt immediately changes moderation behaviour. Seasonal policy adjustments for events, elections, or crisis situations deploy in seconds rather than requiring model retraining cycles.
Deploy Your Moderation System
Self-hosted content moderation gives you full control over policies, data, and moderation decisions at scale. No per-item API fees, no generic policies, no user content leaving your infrastructure. Launch on GigaGPU dedicated GPU hosting and protect your platform today. Browse more automation patterns in our use case library.
