StayLameBro/backburner
Your iPhone helps your Mac run a 27B model: faster prompt reading and more context over a USB-C cable
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
Plug your iPhone into your MacBook with a 10 Gb/s USB-C cable and it helps run Qwen3.8-27B locally: on its GPU, pipelined. Your agent waits less every time it reads a file or a tool result: 29-44% faster prefill at 16k-48k. and computes attention over it: its GPU during prefill, its GPU and Neural Engine while writing. The server sizes the total from the phone's free memory at startup (196k-229k tokens at 8-bit on an iPhone 17 Pro Max). Tested end to end to 128k at 8-bit and 140k at 4-bit. The engine is a llama.cpp fork (llama.cpp/, StayLameBro/backburner-llama.cpp) with its own Mac kernels (SME2, Metal fusions, DFlash2 speculative decoding). Those speed things up on the Mac alone too; the numbers below keep the two apart. Tested on a MacBook Pro M4 Pro (24 GB) with iPhone 17 Pro Max (A19 Pro) and iPhone 16 Pro Max (A18 Pro) phones.…
Read the full README ↗ · Preview checked 2026-10-03T13:16:37.309Z
Inside the original README — Document outline
- Backburner
- Results
- Reading (prefill): Mac alone vs Mac + iPhone, same build
- Writing (decode)
- Context you can hold
- How it works
- Who does what
- The pieces
- Limits
- Setup
- Reproducing the numbers
- Post your results
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What this repository does
Your iPhone helps your Mac run a 27B model: faster prompt reading and more context over a USB-C cable
Repository facts
- Owner
- StayLameBro
- Primary language
- Objective-C++
- Stars
- 158
- Forks
- 13
- Open issues + pull requests
- 5
- License
- MIT
- Archived
- No
- Default branch
- main
- Created
- 2026-10-01T20:59:56.000Z
- Last push
- 2026-10-03T02:44:10.000Z
Topics and intended use
Owner-supplied topics: apple-silicon, ios, iphone, llama-cpp, llm-inference, local-llm, macos, metal, qwen, sme2, speculative-decoding
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Source and freshness
Source: GitHub. Metadata observed 2026-10-03T12:05:37.640Z. Daily imports are snapshots, not real-time monitoring.
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