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inferd 0.4.1

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@johntdavies johntdavies released this 05 Nov 16:12
v0.4.1
4ad756a

inferd 0.4.1: first-run fixes on top of 0.4.0. The engine is unchanged: same kernels and same performance.

Downloads

  • macOS 27: inferd-0.4.1-macos27-arm64.tar.gz
  • macOS 26: inferd-0.4.1-macos26-arm64.tar.gz, built natively on macOS 26.6.2 with Xcode 26.5. 0.4.0's macOS 26 file
    (same engine and toolchain) passed the full release gate on two testers' Macs on macOS 26.6: a 128 GB Mac and a MacBook
    Pro M4 Max, 36 GB. Never run a macOS 26 build on macOS 27 (run.sh refuses it).

Then ./install.sh --model 27b and ./run.sh start --model 27b (see the README's Getting started).

What's fixed (from external testers' reports)

  • Building from source with only the Command Line Tools selected: build.sh now finds Xcode by itself
    (/Applications/Xcode*.app with a Metal compiler), says so, and tells you how to make it permanent
    (sudo xcode-select -s …). Without Xcode it says exactly what to install.
  • install.sh on a venv made by uv, which has no pip: it now uses uv pip, else ensurepip, else a clear message.
  • The SSD tier persists across restarts by design, so agents can come back from it. The new ./run.sh clean [port]
    removes a port's tier; it refuses while that server is running.
  • GET /version now reports the release; it said 0.3.0-dev.
  • examples/run_all.sh defaults its machine name to the Mac's name.
  • examples/agents-at-once writes its results under $TMPDIR, not into the checkout.
  • Docs:
    • the macOS 26 status is corrected, now with two Macs that passed the gate;
    • on macOS 26 the gate may show a Python crash report, because test_image_decode aborts a helper process on purpose;
      it is not a failure;
    • the Neural Engine split is on by default for M4 / M4 Pro only, and off on Max chips;
    • the coding-smoke score ranges are stated per thinking effort.

New: check it yourself

examples/agents-at-once runs N agents at once on their own contexts and checks every agent's answer. It reports tok/s,
and with any watt meter, tokens per joule. On a Mac Studio with Flash-Next:

agents answers right total tok/s wall power tokens per joule
64 128 / 128 1,326 298 W 4.4
100 200 / 200 1,457 300 W 4.9
128 256 / 256 1,487 293 W 5.1

In every run, all the agents were generating at the same moment.

Performance numbers are unchanged from 0.4.0. See the README and docs/RESULTS.md.

Assets 6
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