llama.cpp for Mac OS X Tiger & Leopard (PowerPC G4/G5)

WORLD FIRST: LLM Inference on Classic Mac OS X PowerPC!

This is a working port of llama.cpp for Mac OS X Tiger (10.4) and Leopard (10.5) running on PowerPC G4 and G5 processors.

Why This Matters

• 20-year-old hardware running modern LLM inference
• AltiVec/Velocity Engine SIMD acceleration
• No GPU required - pure CPU inference
• Vintage computing meets AI - your 2005 Power Mac can run Llama!

Tested Hardware

Machine	CPU	RAM	OS	Status
Power Mac G5	Dual 2.0GHz PPC970	8GB	Leopard 10.5	✅ Working
PowerBook G4	1.67GHz 7447A	2GB	Tiger 10.4	✅ Working
Power Mac G4	Dual 1.25GHz 7455	2GB	Tiger 10.4	✅ Working
iMac G5	1.8GHz PPC970	2GB	Tiger 10.4	✅ Working

Performance

Model	Machine	Tokens/sec
TinyLlama 1.1B Q4	G5 Dual 2.0GHz	~3-5 t/s
TinyLlama 1.1B Q4	G4 1.67GHz	~1-2 t/s
Phi-2 Q4	G5 Dual 2.0GHz	~2-3 t/s

It's not fast, but it works!

Building on Tiger/Leopard

Prerequisites

• Xcode 2.5 (Tiger) or Xcode 3.1 (Leopard)
• GCC 4.0 or 4.2
• CMake 3.14 or newer. This is required, not optional. llama.cpp_source/CMakeLists.txt starts with cmake_minimum_required(VERSION 3.14), and the old plain-make build was removed upstream (the Makefile in the tree is now just a stub that prints an error). There is no working no-CMake path. Apple's Tiger/Leopard Xcode does not ship CMake, so see "Getting CMake 3.14+ on PowerPC Tiger/Leopard" below to build one on your own Mac.
• At least 1GB RAM (2GB+ recommended)

Getting CMake 3.14+ on PowerPC Tiger/Leopard

Status update (2026-07): the prebuilt CMake 3.16.9 kit below has been reported broken on real hardware on both OS versions: dyld: Symbol not found: _fcntl against libSystem.B.dylib on Tiger 10.4, and pointer/memory allocation errors on Leopard 10.5. Until that binary is rebuilt and re-verified, build CMake from source directly on the Mac you intend to use it on (see below). That path only depends on your own machine's compiler and libraries, so it does not carry the same cross-OS risk as a single binary shared between Tiger and Leopard.

You do not need MacPorts for this (its PowerPC support has stalled), and you do not need to hand-write a CMakeLists patch. CMake bootstraps with just a C++ compiler, no CMake required to build CMake. With GCC 10 from ppc-tiger-tools installed:

# CMake 3.16.9 is the version verified to bootstrap against llama.cpp's
# cmake_minimum_required(VERSION 3.14) floor. Build it on THIS machine,
# for THIS OS version, do not copy the resulting binary to a different
# Tiger/Leopard machine or OS version.
curl -LO https://github.com/Kitware/CMake/releases/download/v3.16.9/cmake-3.16.9.tar.gz
tar xzf cmake-3.16.9.tar.gz && cd cmake-3.16.9
export DYLD_LIBRARY_PATH=/usr/local/gcc-10/lib
CC=/usr/local/gcc-10/bin/gcc CXX=/usr/local/gcc-10/bin/g++ ./bootstrap --parallel=2
make -j2 && sudo env DYLD_LIBRARY_PATH=/usr/local/gcc-10/lib make install

A full build takes a while on real PowerPC hardware (expect it to run well past an hour on a G4), so start it and come back rather than assuming it hung.

One known gotcha when building CMake from source with GCC 10: Source/cmMachO.h needs #include <memory> added at the top (GCC 10 is stricter about missing standard-library includes than the old Apple GCC was). If ./bootstrap or make fails complaining about std::unique_ptr or similar in cmMachO.h, add that include and re-run make.

After install, confirm the version before moving on:

export PATH=/usr/local/bin:$PATH
cmake --version
# cmake version 3.16.9

Prebuilt CMake kit (currently unreliable, use with caution)

There is also a prebuilt CMake 3.16.9 tarball for Tiger/Leopard PPC in the sibling repo. It was built and tested on real G4/G5 hardware at the time, but has since been reported to fail on other real Tiger and Leopard machines (see the status note above), most likely because it is one binary being asked to run against two different OS versions' libSystem, which have different symbol layouts. Try it only if building from source is not an option for you, and expect to fall back to the from-source path above if it does not work:

curl -LO https://github.com/Scottcjn/tiger-macports/raw/main/binaries/kit-cmake-3.16.9-tiger-ppc.tar.gz
cd /usr/local
sudo tar xzf ~/kit-cmake-3.16.9-tiger-ppc.tar.gz
export PATH=/usr/local/bin:$PATH
cmake --version

If it complains about a missing libstdc++/libgcc_s, install the GCC 10 runtime from ppc-tiger-tools and add export DYLD_LIBRARY_PATH=/usr/local/gcc-10/lib before running it. If instead you hit dyld: Symbol not found on Tiger or pointer/allocation crashes on Leopard, that is the known issue above, not something wrong on your end. Please build from source instead and, if you can, file the exact error output as a comment on issue #25 so the prebuilt kit itself can get fixed.

Note: the CMake 2.8.12 release in ppc-tiger-tools is too old for llama.cpp (it is below the 3.14 minimum). Do not use it for this build.

Build Commands

# Clone or copy the source
cd llama.cpp

# Create build directory
mkdir build && cd build

# Configure for PowerPC with AltiVec
cmake .. \
    -DCMAKE_BUILD_TYPE=Release \
    -DCMAKE_OSX_ARCHITECTURES=ppc \
    -DCMAKE_C_FLAGS="-mcpu=G4 -maltivec -O3" \
    -DCMAKE_CXX_FLAGS="-mcpu=G4 -maltivec -O3"

# For G5, use:
# -DCMAKE_C_FLAGS="-mcpu=G5 -maltivec -O3"

# Build
make -j2

Note on the old Makefile build (does not work anymore)

Older llama.cpp guides told you to run make CFLAGS=... with no CMake. That path is gone. The Makefile in llama.cpp_source is now a deprecation stub, so running make there just prints:

Build system changed:
The Makefile build has been replaced by CMake.

That error is expected. It is not a bug in this port, it is upstream removing the old build system. CMake 3.14+ is the only supported way to build now, so use the CMake commands above with the CMake you installed in the previous section.

Running Inference

# Basic inference
./main -m ~/models/tinyllama-1.1b-q4_0.gguf -p "Hello from PowerPC!" -n 32

# With thread count (use number of CPU cores)
./main -m ~/models/tinyllama-1.1b-q4_0.gguf -p "Hello" -n 32 -t 2

Model Recommendations

For vintage Macs with limited RAM:

RAM	Recommended Models
1-2GB	TinyLlama 1.1B Q4_0, Phi-1.5 Q4_0
2-4GB	Phi-2 Q4_0, Gemma-2B Q4_0
4-8GB	Llama-7B Q4_0 (G5 only)

Use Q4_0 quantization for best performance on limited RAM.

Known Issues

• Slow startup: Model loading takes 30-60 seconds on HDD
• Memory pressure: Close other apps before running
• No Metal/GPU: Pure CPU inference only
• Thread scaling: More than 2 threads may not help on G4

Part of Sophiacord

This build is part of the Sophiacord distributed AI system, which runs across:

• IBM POWER8 (main inference)
• PowerPC G4/G5 Macs (vintage nodes)
• x86 servers with GPUs
• ARM nodes

Learn more: https://github.com/Scottcjn

Attribution

A year of development, real hardware, electricity bills, and a dedicated lab went into this.

If you use this project, please give credit:

llama.cpp Tiger/Leopard Port by Scott (Scottcjn)
https://github.com/Scottcjn/llama-cpp-tigerleopard

If this helped you, please:

• ⭐ Star this repo - It helps others find it
• 📝 Credit in your project - Keep the attribution
• 🔗 Link back - Share the love

Credits

• ggerganov/llama.cpp - Original project
• Scott (Scottcjn) - Creator, architect, hardware lab, PowerPC/Tiger/Leopard port
• Claude (Opus 4.1/4.5) - Implementation assistance

Designed by Scott, coded with Claude

License

MIT License (same as llama.cpp) - Free to use, but please keep the copyright notice and attribution.

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"Your 2005 Power Mac is now an AI. The future is vintage."

Running LLMs on 20-year-old hardware. Vintage computing meets modern AI.

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