tilefoundry 0.0.4rc1

Hand the compiler to the agent.

Not an agent that becomes the compiler,
and not an agent plugged in as one of its passes.
The compiler stays a tool — the agent is simply the one holding it.

One prompt in  ·  612 tok/s out  ·  Compiler in the loop

Documentation · Quick Start · Examples

Latest News

• 10/2026 🚀: TileFoundry 0.0.3 is on PyPI — a new instruction-level schedule, a restructured analysis, and prefill examples on H200 and Apple AMX.
• 10/2026 ⚡: Qwen3.8-27B prefill — a 64-layer gated-delta-net / attention hybrid, faster than SGLang at 1024 / 4096 / 8192 tokens, and 1.88x–1.03x faster at 128–1024 tokens, across the roofline ridge.
• 10/2026 🍎: Qwen prefill on Apple AMX — Qwen3-1.7B and Qwen3.5-4B text prefill on an M2 Pro, with the large projections as FP16 AMX outer products.
• 09/2026 📝: AI Compilers in the Agentic Era — where is the right boundary between coding agents and AI compilers?
• 09/2026 🔧: TileFoundry 0.0.2 is on PyPI — bug fixes and refactoring across the analysis, parser, and IR layers.

Quick Start

1 · Install

pip install "tilefoundry[all]"    # needs Python 3.12 or newer; [nvidia] or [apple] installs one vendor
tilefoundry                # check the install: the commands an agent will ask

This run also needs one NVIDIA GPU, pip install tilelang, the published Qwen/Qwen3-1.7B checkpoint on disk (3.8 GB), and a coding agent started in an empty directory.

2 · Hand it the prompt

There is no API to learn first. Give your coding agent this, with a checkpoint directory of your own:

Get real tokens out of Qwen3-1.7B on TileFoundry, and make it fast.
Weights and config: <checkpoint directory>
Backend: tilelang.

Everything about TileFoundry is to be asked of the `tilefoundry` command -- do not
ask a person, do not go looking elsewhere. The model itself is yours to research.

Done when this runs from outside, prints the continuation, and reports a
tokens-per-second number measured over the whole generation:

    python run.py \
        --prompt "Write a detailed explanation of how a GPU executes a matrix multiplication." \
        --max-new-tokens 2048

Measure over a long generation -- 2048 new tokens, more than 2000 characters of
text. A 32-token sample is too short for the number to mean anything.

That is the whole input — nothing under it is written by hand.

3 · Come back in two hours

Claude Opus 5 at xhigh reasoning effort worked 2.1 hours and 177 tool calls without a single interaction, and left a run.py behind — it prints the continuation, and the number it measured: 612 tok/s on one H200.

python run.py --ckpt <checkpoint directory> \
    --prompt "Write a detailed explanation of how a GPU executes a matrix multiplication." \
    --max-new-tokens 2048

The first run compiles the kernels — once, a few minutes.

Where to go next

That run is kept whole in examples/qwen3_1_7b-tilelang/, with three more beside it. The specifications are meant to be argued with: open an issue, or start from docs/develop.md.

License

This project is licensed under the MIT License.