Token Count → Storage Estimate
Each token in modern LLM training pipelines consumes approximately 4 bytes of raw text storage (~4 tokens per word in English). Use this tool to estimate corpus storage requirements and inference KV-cache sizing.
Bidirectional Token Storage Estimator
Convert between token counts and estimated storage in both directions. Assumes ~4 bytes per token (UTF-8 average for English text corpora).
Famous Corpus Size Reference
Real-world training dataset sizes for context.
| Dataset / Model | Tokens | Est. Raw Storage |
|---|---|---|
| Common Crawl (Filtered) | ~3T | ~12 TB |
| The Pile (v1) | ~800B | ~3.2 TB |
| FineWeb | ~15T | ~60 TB |
| GPT-4 Training Corpus | ~13T | ~52 TB |
| Claude Training Corpus | ~10T+ | ~40 TB+ |
| Wikipedia (English, Full) | ~4.4B | ~17.6 GB |
Token Economics: From Text to Storage to GPU Memory
The 4 bytes-per-token estimate is derived from UTF-8 encoding averages on English-dominated text corpora. Non-English languages (CJK scripts in particular) produce fewer bytes per token due to more compact semantic encoding per Unicode codepoint. Multilingual datasets should budget 3–5 bytes per token depending on language distribution.
Raw text storage is only the first layer. Training infrastructure multiplies this footprint: tokenized integer sequences double the storage (4 bytes per token ID), shuffled and packed sequences add another 1–1.5×, and the final dataloader memory-map typically requires 2–3× the raw corpus size. A 10 TB raw corpus often translates to 25–30 TB of training-ready storage — before replication, snapshots, or backup. Factor this into your HPC cluster procurement or cloud NAS provisioning from the start.