gigatoken-rb

12.4 GB/s / 2.8 billion tokens per second in Ruby.

Zero-copy Ruby bindings for marcelroed/gigatoken, the fastest open-source BPE tokenizer around.

Mac Studio M4 Max, OpenWebText, GPT-2 tokenizer; every library produces the same tokenization, gigatoken just does it faster. The Python row includes a fix for marcelroed/gigatoken#38 — a hidden memcpy in shrink_to_fit() we found while chasing the last of the Ruby–Python gap and sent upstream (one-line fix; without it the wheel lands around 7.4 GB/s).

340x faster than the fastest existing Ruby gem (tiktoken_ruby) and 1,050x faster than the tokenizers gem. Full methodology & exact counts: docs/rb/benchmarks.md.

gem install gigatoken

Precompiled native gems ship for Apple Silicon macOS (arm64-darwin) and x86_64/aarch64 Linux — on those platforms RubyGems grabs the binary automatically, no Rust toolchain, no compile wait. In a Bundler project it's one command:

bundle add gigatoken

(or drop gem "gigatoken" into the Gemfile yourself).

Anywhere else (or with --platform ruby to opt out of the binary), the extension builds from source. That needs a Rust toolchain: rust-toolchain.toml pins the nightly, and rustup fetches it automatically on first build.

require "gigatoken"

tok = Gigatoken::Tokenizer.load("openai-community/gpt2")

tok.encode("Hello, world!")             # => [15496, 11, 995, 0]
tok.decode([15496, 11, 995, 0])         # => "Hello, world!"
tok.encode_batch(["Hello!", "Another"]) # => [[15496, 0], [6610]]

tok.vocab_size                          # => 50257
tok.special_tokens                      # => {"<|endoftext|>" => 50256}

load takes a tokenizer.json path, a directory holding one, a HuggingFace Hub repo id, or a .tiktoken mergeable-ranks file, and dispatches on shape. Hub downloads run over socketry's async-http — no Python anywhere. Know what you have? Skip the dispatch:

Gigatoken::Tokenizer.from_file("tokenizer.json")
Gigatoken::Tokenizer.from_hub("openai-community/gpt2", revision: "main")
Gigatoken::Tokenizer.from_tiktoken("vocab.tiktoken")
Gigatoken::Tokenizer.from_json(File.binread("tokenizer.json"))

SentencePiece-BPE models (Llama, Gemma, Mistral — any tokenizer.json with byte_fallback: true) load through the same entry points and pick the right backend automatically. One difference: the SentencePiece core decodes text, so it validates input and raises Gigatoken::Error on invalid UTF-8 instead of guessing.

encode_files reads and tokenizes files entirely on the native side — document contents never materialize as Ruby objects. .gz and .zst decompress transparently.

tok.encode_files("owt_train.txt", separator: "<|endoftext|>")

jsonl   = Gigatoken::Native::JsonlFileSource.new(["docs.jsonl"], field: "text")
parquet = Gigatoken::Native::ParquetFileSource.new(["docs.parquet"], column: "text")
tok.encode_files(jsonl)

Pass packed: true to encode_batch or encode_files and results land in a single IO::Buffer of u32 token ids instead of a ragged Array of Arrays — no per-token Ruby allocation, the fastest way out of the engine:

packed = tok.encode_files("owt_train.txt", packed: true, separator: "<|endoftext|>")

packed.buffer       # => one IO::Buffer, every document's ids back to back
packed.lens         # => [12, 8, 41, ...] tokens per document
packed.token_count  # => total tokens
packed[3]           # => document 3's ids as an Array, on demand

encode_batch and encode_files release the GVL for the whole encode; the parallelism runs on the engine's rayon pool, not Ruby threads. Under Async, give the fiber scheduler a worker pool (ASYNC_SCHEDULER_WORKER_POOL=true) and the calling fiber yields to the reactor too. Design notes: docs/rb/async.md.

gigatoken bench openai-community/gpt2 owt_train.txt --doc-separator "<|endoftext|>"
gigatoken validate openai-community/gpt2 owt_train.txt --doc-separator "<|endoftext|>"

bench reports MB/s and Mtok/s (--packed for the fused packed path, --no-parallel for the serial core). validate confirms native split-and-encode agrees with a Ruby-side split through encode_batch.

bundle install
bundle exec rake compile    # native extension (Rust nightly, via rust-toolchain.toml)
bundle exec rspec
bundle exec standardrb

The Ruby layer is fiber-first throughout — no Thread, no Mutex; all parallelism lives in the core's rayon pool. CI runs ubuntu + macos × Ruby 3.3/3.4/4.0, and release.yml cross-builds the precompiled native gems (arm64-darwin, x86_64-linux, aarch64-linux).

This fork exists because I need fast tokenization in Ruby. The Rust core is changed as little as possible from upstream. Most of the python shell has been removed from this fork, but you can still find it upstream.

SentencePiece works but — matching upstream — is less optimized than the BPE path.

Not ported/no current plans:

• the HF/tiktoken Python compat shims
• padded-batch matrices
• and BPE training

The engine is Marcel Rød's gigatoken. If it shows up in your research, cite that:

@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},
}

The Rust engine is upstream's — see upstream's AI-use disclosure for how that was built (majority hand-crafted, AI-assisted toward the end).

The Ruby port in this fork is 100% AI generated using Fable 5 and Sonnet 5 via space-architect in ~24 hours.