gigatoken README

The project's README from github.com/marcelroed/gigatoken. As one markdown file: README.md.

Gigatoken

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

Tokenize your text data at GB/s!

GPT-2 Speedup

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

What is Gigatoken?

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.

Installation

pip install gigatoken

Usage

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

Compatibility Mode (Easiest)

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.

Gigatoken API (Fastest)

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.

Benchmarks

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.

The slowest rows are the SentencePiece-based tokenizers, which are not well optimized in Gigatoken.

Each row is one distinct tokenizer (identical vocab/merges/pretokenizer), measured on a representative repo. If you don't see your tokenizer here, it's likely based on some existing one. For instance:

  • Llama 3 / 3.1 / 3.2 — Llama 3 / 3.1 / 3.2, DeepSeek-R1-Distill-Llama, Hermes 3, Saiga, and other Llama-3 finetunes
  • Llama 3.3 — Llama 3.3, Llama-3.1-Nemotron-Nano-VL, SmolLM3, Kanana 1.5, jina-embeddings-v5, Ultravox
  • Qwen 2 / 2.5 — Qwen 2 and 2.5 (incl. Coder and VL), Qwen3-Coder, Qwen3-VL, DeepSeek-R1 Qwen distills, MiMo V2.5, MiniCPM-o 2.6, InternVL3
  • Qwen 3 — Qwen 3 (incl. Embedding and Reranker), Qwen2.5-Omni, Qwen3-VL-Embedding, MiMo V2.5 Pro, jina-reranker-m0, pplx-embed, MOSS-TTS, Zeta
  • DeepSeek V3 / R1 / V4 — DeepSeek V3 / V3.1 / V3.2, R1, V4 Flash and Pro, DeepSeek-VL2
  • GLM 4 — GLM 4.1V, 4.5, and 4.7
  • GLM 5 — GLM 5 / 5.2 and GLM-4.7-Flash
  • Nemotron 3 — Nemotron 3 Nano, Super, and Ultra
  • Kimi K2 — Kimi K2 / K2.5 / K2.6 / K2.7, Kimi-Linear, Kimi-VL, Moonlight
  • Phi-4-mini — Phi-4-mini and Phi-4-multimodal
  • TinyLlama / Phi-3 (Llama 2) — TinyLlama, Phi-3-mini, Phi-3.5-mini and Phi-3.5-vision (the Llama 2 vocab)
  • Gemma 3 — Gemma 3 (270M–27B) and EmbeddingGemma
  • Gemma 4 — Gemma 4 (dense, MoE, and E-series) and DiffusionGemma

FAQ

Q: Did you just way over-optimize for a specific CPU and tokenizer? How is it so fast?

No, I way over-optimized for every combination of these! The results are very consistent across CPUs (modern x86 and ARM), and across specific tokenizers.

The major improvements are in optimizing heavily an implementation that usually is outsourced to a Regex engine (pretokenization) using SIMD, minimizing branching and other tricks, as well as heavily optimizing caching of pretoken mappings (if a word has been seen before, look up its encoded tokens efficiently). Caching is a very hard problem in this domain since the cache grows very quickly, and pretoken distributions are very long-tailed.

Some gains are also achieved from minimizing interactions with Python, and avoiding communication between threads.

Q: How can I quickly check if my tokenizer is supported?

You can try it out without installing anything! The following command will validate and time tokenization for a given HuggingFace model repo: 

# Download your data
wget https://huggingface.co/datasets/stanford-cs336/owt-sample/resolve/main/owt_train.txt.gz  # Just an example!
gunzip owt_train.txt.gz

uvx --with tokenizers gigatoken bench 'openai-community/gpt2' owt_train.txt \
    --validate --doc-separator "<|endoftext|>"

      cpu: Apple M4 Max, 16 cores
gigatoken:    1.432 s |   11920.51 MB at  8327.05 MB/s |  2701.65 Mtok at 1887.23 Mtok/s
       hf:   16.250 s |     100.00 MB at     6.15 MB/s |    22.76 Mtok at    1.40 Mtok/s
gigatoken is 1353.13x faster than hf
validation OK: 20401 documents match

      cpu: AMD EPYC 9565 72-Core Processor, 144 cores, 2 sockets
gigatoken:    0.486 s |   11920.51 MB at 24532.45 MB/s |  2701.65 Mtok at 5564.94 Mtok/s
       hf:    4.033 s |     100.00 MB at    24.80 MB/s |    22.76 Mtok at    5.63 Mtok/s
gigatoken is 989.21x faster than hf
validation OK: 20401 documents match

At the rates we see on the EPYC CPU, you could tokenize the entirety of Common Crawl (often considered to be the entire internet, 130 trillion tokens) in just under 6.5 hours!

This example uses the train sample from this dataset, and the CLI by default subsets to the first 100MB of the file for validation and comparison with HF. You can see help for these flags with uvx gigatoken bench --help. You might need to run your commands twice on macOS to get a good reading, since the first run will always perform a security scan, which will slow down the Rust code.

Q: I've found a mismatch/slow use-case, is this expected?

Most likely not! Despite reasonably wide testing I don't have every use-case on hand, so please report anything you find in a GitHub Issue so I can address it as soon as possible.

Citation

If you use Gigatoken in your research, please cite it as:

@software{roed2026gigatoken,
  author = {Marcel R{\o}d},
  title = {{G}igatoken: SIMD and Cache Hierarchies for 1000x Faster Byte-Pair Encoding Tokenization on Modern CPUs},
  url = {https://github.com/marcelroed/gigatoken},
  year = {2026},
}

Known Issues

AI Use Disclosure A majority of this code base was crafted by hand without any use of AI (which can be seen from the project's Git history). In the final stages of the project, AI was used to assist:
  • Implementing the user-facing API
  • Widening of compatibility, for instance generalizing and porting the pretokenizer implementations to support more tokenizers, less interesting features like padding/truncation/unicode normalization
  • Porting SIMD strategies between AVX512/AVX2/NEON
  • Final profiling stages and the last ~4x worth of performance from eliminating branching and improving the pretoken cache hierarchy
  • Refactoring and code reuse

gigatoken (marcelroed/gigatoken)

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4.1K ·GitHub
Gigatoken
What is this?Gigatoken is an extremely fast tokenizer for language models, built as a near drop-in replacement for HuggingFace Tokenizers and tiktoken, encoding text at multi-GB/s speeds.
Is this a standalone CLI or a library I plug into my code?It's both: a Rust-based library with its own Python API for maximum speed, a compatibility mode that mimics HuggingFace Tokenizers or tiktoken for easy adoption, and a CLI (gigatoken bench) for benchmarking a tokenizer without writing code.
How do I try it on my own tokenizer?Run it with 'uvx gigatoken bench' pointed at a HuggingFace model repo to validate and time tokenization without installing anything first; see 'uvx gigatoken bench --help' for all flags.
Do I need Python to use it?Yes, it's distributed and run through Python tooling (uvx/pip) with a Rust core underneath, though passing data through the Python API carries some overhead compared to the native Rust path.
Are there tokenizers or platforms it doesn't fully support yet?WordPiece isn't supported yet, SentencePiece-based tokenizers (mostly Google/BERT-style models) aren't well optimized, file sinks are missing from the Gigatoken API, and Windows hasn't been tested much, with WSL recommended instead. CJK-heavy text is also notably slower to tokenize.
Why is gigatoken so much faster than HuggingFace tokenizers?It heavily SIMD-optimizes the pretokenization step that other tokenizers outsource to a regex engine, minimizes branching, and uses an aggressive multi-level cache for pretoken-to-token mappings, while also cutting overhead from Python interop and thread communication.
Which model families' tokenizers does it cover out of the box?It covers nearly all commonly used tokenizers, including GPT-2, Llama 3/3.1/3.2/3.3/4, Qwen 2/2.5/3, DeepSeek V3/R1/V4, GLM 4/5, Gemma 1/3/4, Phi-4, Nemotron 3, Kimi K2, and more, with benchmarks published for each.
Was AI used to build this?Most of the codebase was written by hand; AI was only used in later stages to help with the user-facing API, widening tokenizer compatibility, porting SIMD strategies across AVX512/AVX2/NEON, and final profiling and refactoring.
How fast could it tokenize something like the entire internet?At the throughput measured on the AMD EPYC benchmark, Gigatoken could tokenize the entirety of Common Crawl (about 130 trillion tokens) in just under 6.5 hours.
What are similar projects?tiktoken, CalcKernel, jeeves, spaCy, sentencepiece, jieba, hotpath-rs, simdjson
Which topics is it in?tokenizer, simd, tokenization, data-processing, cpu, pypi, text-processing, high-performance, fine-tuning, large-language-models, nlp, huggingface
Creator
marcelroed
Size
2.9 MB
Stars
4.1K
Updated
CommitAug 06
Age
11mo
Commits · 90 Days