Scale Requirements for LLM Training Crawls

Training GPT-4-scale model requires ~10T tokens. At 250 tokens/KB (average), that's 40TB of text data. Crawling 100TB of raw HTML to extract 40TB of text (60% overhead for boilerplate, filtering loss).

Success rate is everything. If your proxy setup succeeds 85% of the time, you need 117TB of crawl to get 100TB useful. If 95% success, only 105TB needed. The 12TB difference costs ~$100K+ in storage, computation, proxy bandwidth.

// ProxifyPRO for training data crawls

AI training teams can't afford 30% failure rate. ProxifyPRO mobile CGNAT rotation achieves 95%+ success on diverse targets because carrier-level rotation is indistinguishable from real user behavior. Legacy per-request rotation overhead makes this harder.

Crawl Architecture at Scale

Distributed Workers

Use 100+ concurrent workers (Kubernetes pods, AWS Batch). Each worker: fetch URL → parse HTML → extract text → validate → send to queue. Typical throughput: 500-1000 URLs/worker/hour with mobile proxies.

Quality Filters

Language detection: Discard non-English (or target language). Use langdetect, confidence > 0.95.

Toxicity scoring: Jigsaw Perspective API flags toxic content. Threshold 0.5-0.7 to filter offensive text without over-filtering casual language.

Boilerplate removal: Navigation menus, ads, footers. Use trafilatura library (fast, ML-based boilerplate detection).

Checkpointing

Store state every 100 URLs: last URL processed, next URL in queue. On crash, resume from checkpoint. Prevents re-crawling the same domain.

Deduplication at Web Scale

Detecting exact duplicates of 40TB text is computationally expensive. Solutions:

MinHash (Probabilistic)

Create a "signature" of each document (128-256 bits). Two documents are similar if their signatures match. False positive rate < 0.1% with 256-bit hashes. Cost: O(1) space per document.

Algorithm: hash document into k random projections, store minimum hash from each projection. Compare signatures instead of full text.

Bloom Filters (Space-Efficient)

Store URLs in a bloom filter (constant memory, no false negatives for existence). New URL? Check bloom filter (~O(1)). If not present, add. Prevents re-crawling the same URL.

Trade-off: small false positive rate (e.g., 0.01%) but saves 90% RAM vs hash set.

Cost Optimization for AI-Scale Crawls

Typical budget: 1TB crawl with mobile proxies = $8-15K proxy cost + $5K compute + $2K storage = $15-22K total.

Optimization levers:

  • Batch by domain: crawl all pages from example.com before rotating IP (reduce proxy overhead)
  • Reuse sessions: maintain cookie jar per IP, crawl multiple pages per connection (HTTP keep-alive)
  • Selective mobile: use mobile proxies only on high-detection targets (Instagram, Twitter). Use residential elsewhere (cheaper)
  • Spot instances: AWS Spot for crawl workers (70% discount vs on-demand)

Optimized: 1TB crawl might cost $8-12K instead of $15-22K.