v0.1.0 — first public build

A native macOS menu bar app that works out what a local model will cost in
unified memory before you download it, then proves the prediction.

Install

1. Download Silicon Optimizer.dmg, open it, drag the app to Applications.

2. This build is signed locally rather than notarized, so macOS will block it on
   first launch. Clear the quarantine flag once:

   xattr -d com.apple.quarantine "/Applications/Silicon Optimizer.app"

   Or build from source, which avoids the prompt entirely.

3. Install a runtime — the app finds it automatically:

   brew install llama.cpp

What it does

Predicts weights, expert pool, KV cache and compute buffers from the model's
real architecture rather than estimating from file size, then tells you whether
your Mac can run it and how fast. If it will not fit, you get a ranked list of
what to change with the exact saving of each.

A bundled MCP server lets Claude or ChatGPT drive the model you already have
loaded, instead of starting a second copy.

Measured on an M3 Max, llama.cpp b10280

	Predicted	Measured
Qwen3-Coder 30B-A3B, Q4_K_M, 16K	20.30 GB	20.24 GB
Generation	89 tok/s	89 tok/s
Expert streaming, 24 of 128 experts	5.20 GB	5.32 GB

Expert streaming runs the same 30B model in 5.3 GB instead of 19 GB, with
identical output — only residency changes.

Known limits

• Calibrated on one machine. The built-in benchmark corrects for yours.
• Not notarized, hence the quarantine step above.
• Expert streaming needs a llama.cpp built with the expert-paging patch; the
  app detects the flags and hides the option when they are absent.
• MLX runs but has no catalog entries yet — point it at a model directory.
• Split-file models resolve correctly but the multi-part download is untested.

Full detail: https://optimize.zamasu.dev