GigaToken: ~1000x faster Language model tokenization

~1000x faster than HuggingFace's tokenizers, drop-in replacement.

Note that both HF tokenizers and tiktoken are already running multithreaded Rust!

Gigatoken is the fastest tokenizer for language modeling. It supports a wide range of CPU hardware, and nearly all commonly used tokenizers. See the Benchmarks section for detailed throughput numbers across tokenizers and CPUs.

pip install gigatoken Usage Gigatoken can be used with its own API, or in compatibility mode with HuggingFace Tokenizers or Tiktoken.

import gigatoken as gt # Minimum change from existing HuggingFace tokenizers usage (compatibility mode) hf_tokenizer = ... tokenizer = gt . Tokenizer ( hf_tokenizer ). as_hf () # tokenizer can be used in the same contexts as hf_tokenizer tokens = tokenizer . encode_batch ([ "This is a test string" , "And here is another" ]) # OR with tiktoken tiktokenizer = ... tokenizer = gt . Tokenizer ( tiktokenizer ). as_tiktoken () # Now works like existing tiktoken tokenizers tokens = tokenizer . encode_batch ([ "This is a test string" , "And here is another" ]) A substantial amount of effort has been put into making sure the outputs match exactly with what you would get with HuggingFace Tokenizers in this setting, but this is at a non-negligible cost to performance. You can still expect way faster performance across the board, but not quite the 1000x you will get with the Gigatoken API.

import gigatoken as gt tokenizer = gt . Tokenizer ( "Qwen/Qwen3-8B" ) # Accepts HF model names file_source = gt . TextFileSource ([ "owt_train.txt" ], separator = b"<|endoftext|>" ) tokens = tokenizer . encode_files ( file_source ) Using the Gigatoken API lets the Rust implementation read data directly, and skips as much overhead as possible while allowing for maximum parallelism. Keep in mind that passing Python data structures through this API still incurs the overhead of reading from Python.

Encoding throughput on owt_train.txt (11.9 GB) — AMD EPYC 9565 72-Core Processor x 2 sockets (144 cores) Tokenizer gigatoken HF tokenizers tiktoken vs HF vs tiktoken GPT-2 24.53 GB/s 24.8 MB/s 36.0 MB/s 989× 681× Phi-4 24.00 GB/s 29.9 MB/s — 801× — GPT-OSS 23.96 GB/s 49.7 MB/s 42.8 MB/s 482× 560× OLMo 2 / 3 23.06 GB/s 27.7 MB/s — 833× — Nemotron 3 22.79 GB/s 49.4 MB/s — 462× — Qwen 3 22.16 GB/s 34.2 MB/s — 648× — Llama 3 / 3.1 / 3.2 22.15 GB/s 48.5 MB/s — 457× — GLM 5 20.97 GB/s 74.8 MB/s — 280× — Llama 3.3 20.82 GB/s 48.3 MB/s — 431× — Llama 4 20.77 GB/s 72.7 MB/s — 286× — GLM 4 20.61 GB/s 72.3 MB/s — 285× — Phi-4-mini 20.05 GB/s 27.6 MB/s — 726× — DeepSeek V3 / R1 / V4 19.69 GB/s 26.2 MB/s — 750× — Qwen 2 / 2.5 19.12 GB/s 27.7 MB/s — 691× — Kimi K2 18.85 GB/s — — — — Qwen 3.5 / 3.6 15.49 GB/s 27.7 MB/s — 558× — Gemma 4 4.82 GB/s 334.1 MB/s — 14× — ModernBERT 4.18 GB/s 26.9 MB/s — 155× — Mistral 7B v0.3 3.57 GB/s 354.7 MB/s — 10× — TinyLlama / Phi-3 (Llama 2) 3.48 GB/s 323.6 MB/s — 11× — CodeLlama 3.47 GB/s 347.4 MB/s — 10.0× — Gemma 3 3.43 GB/s 357.2 MB/s — 9.6× — Gemma 1 2.51 GB/s 342.2 MB/s — 7.3× — Encoding throughput on owt_train.txt (11.9 GB) — Apple M4 Max (16 cores) Tokenizer gigatoken HF tokenizers tiktoken vs HF vs tiktoken GPT-2 8.79 GB/s 6.9 MB/s 62.8 MB/s 1,268× 140× Nemotron 3 7.82 GB/s 10.9 MB/s — 715× — Phi-4 7.76 GB/s 7.7 MB/s — 1,012× — Llama 3 / 3.1 / 3.2 7.60 GB/s 11.2 MB/s — 676× — OLMo 2 / 3 7.56 GB/s 5.8 MB/s — 1,299× — Llama 3.3 7.50 GB/s 15.7 MB/s — 479× — Phi-4-mini 6.97 GB/s 7.2 MB/s — 964× — Kimi K2 6.88 GB/s — — — — Llama 4 6.81 GB/s 11.6 MB/s — 590× — Qwen 2 / 2.5 6.37 GB/s 5.8 MB/s — 1,105× — Qwen 3 6.36 GB/s 6.9 MB/s — 918× — Qwen 3.5 / 3.6 6.31 GB/s 6.3 MB/s — 994× — GPT-OSS 6.20 GB/s 20.2 MB/s 87.2 MB/s 306× 71× GLM 4 6.17 GB/s 15.8 MB/s — 392× — DeepSeek V3 / R1 / V4 5.68 GB/s 7.2 MB/s — 788× — GLM 5 5.55 GB/s 12.2 MB/s — 456× — ModernBERT 2.64 GB/s 5.8 MB/s — 452× — Mistral 7B v0.3 1.99 GB/s 95.1 MB/s — 21× — Gemma 4 1.82 GB/s 85.2 MB/s — 21× — CodeLlama 1.73 GB/s 80.2 MB/s — 22× — TinyLlama / Phi-3 (Llama 2) 1.69 GB/s 80.1 MB/s — 21× — Gemma 1 1.42 GB/s 85.7 MB/s — 17× — Gemma 3 1.38 GB/s 82.2 MB/s — 17× — Encoding throughput on owt_train.txt (11.9 GB) — AMD Ryzen 7 9800X3D 8-Core Processor (16 cores) Tokenizer gigatoken HF tokenizers tiktoken vs HF vs tiktoken GPT-2 6.27 GB/s 59.0 MB/s 92.1 MB/s 106× 68× Phi-4 6.09 GB/s 55.4 MB/s — 110× — OLMo 2 / 3 6.06 GB/s 55.4 MB/s — 109× — Phi-4-mini 5.80 GB/s 54.6 MB/s — 106× — GPT-OSS 5.68 GB/s 79.6 MB/s 112.7 MB/s 71× 50× Qwen 3 5.34 GB/s 54.4 MB/s — 98× — Qwen 2 / 2.5 5.30 GB/s 51.7 MB/s — 103× — Llama 3.3 5.26 GB/s 79.9 MB/s — 66× — Llama 3 / 3.1 / 3.2 5.24 GB/s 79.5 MB/s — 66× — Kimi K2 5.23 GB/s — — — — Qwen 3.5 / 3.6 5.22 GB/s 51.6 MB/s — 101× — Nemotron 3 5.20 GB/s 79.0 MB/s — 66× — GLM 5 5.05 GB/s 79.5 MB/s — 63× — GLM 4 5.04 GB/s 79.5 MB/s — 63× — Llama 4 5.03 GB/s 78.2 MB/s — 64× — DeepSeek V3 / R1 / V4 4.21 GB/s 51.6 MB/s — 82× — ModernBERT 2.84 GB/s 52.1 MB/s — 54× — Mistral 7B v0.3 1.47 GB/s 91.6 MB/s — 16× — Gemma 4 1.45 GB/s 78.8 MB/s — 18× — CodeLlama 1.38 GB/s 85.2 MB/s — 16× — TinyLlama / Phi-3 (Llama 2) 1.37 GB/s 84.9 MB/s — 16× — Gemma 1 1.14 GB/s 84.9 MB/s — 13× — Gemma 3 1.12 GB/s 83.0 MB/s — 13× — Benchmark details OWT (openwebtext) was chosen because it's roughly representative of the text you get after extraction from CommonCrawl documents. Gigatoken encodes the whole file un-split, and is thus doing more work than the other tokenizers to find the split boundaries and automatically parallelize. HuggingFace tokenizers ( encode_batch_fast ) gets the first 100 MB and tiktoken ( encode_ordinary_batch ) the first 1 GB, both presplit on <|endoftext|> . This is fair because neither of the compared tokenizers do caching, meaning the speed is roughly uniform throughout processing. Tiktoken rows are currently only filled in for tokenizers with official support.