# Panduan Teknikal: Compile llama.cpp di Debian 12/13 dan Cross Compile ARM64 > Source: > Published: 2026-07-14 07:52:24+00:00 **1. Pengenalan** llama.cpp ialah runtime inference LLM berasaskan C/C++ yang popular kerana ringan, pantas, dan sesuai untuk menjalankan model GGUF secara local. Ia boleh digunakan pada: Server x86_64 Workstation Linux Mini PC Raspberry Pi Orange Pi SBC ARM64 Container Linux Dalam deployment sebenar, terdapat dua pendekatan utama: Native build Compile terus pada mesin yang akan menjalankan llama.cpp. Cross compile Compile pada mesin lebih laju (contohnya PC x86_64), tetapi menghasilkan binary untuk platform lain (contohnya ARM64 Orange Pi). **Bahagian 1 — Persediaan Debian 12/13** 1.1 Install dependency asas ``` sudo apt update sudo apt install -y \ git \ build-essential \ cmake \ ninja-build \ pkg-config ``` Komponen utama: Package Fungsi git Ambil source code build-essential GCC, G++, make cmake Build configuration ninja-build Build engine lebih pantas pkg-config Cari library dependency **Bahagian 2 — Clone llama.cpp** ``` git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp ``` Semak versi: ``` git log -1 --oneline ``` **Bahagian 3 — Compile Native (Mesin Sama)** Contoh: Debian 12/13 x86_64 Debian ARM64 Orange Pi Raspberry Pi **3.1 Configure CMake** ``` Build menggunakan Ninja: cmake -B build \ -G Ninja \ -DCMAKE_BUILD_TYPE=Release 3.2 Compile ninja -C build -j$(nproc) ``` atau: ``` cmake --build build ``` plaintext **3.3 Hasil build** Semak: ``` ls build/bin ``` plaintext Contoh: ``` llama-cli llama-server llama-bench llama-perplexity ``` shell **Bahagian 4 — Enable OpenBLAS (Pilihan)** OpenBLAS boleh membantu operasi matrix CPU. Install: ``` sudo apt install libopenblas-dev ``` cmake Build: ``` cmake -B build \ -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DGGML_BLAS=ON \ -DGGML_BLAS_VENDOR=OpenBLAS ``` shell Kemudian: ``` ninja -C build ``` Nota Penting: CMake Cache Jika pernah configure dengan: ``` -DGGML_BLAS=ON ``` kemudian buang option tersebut, CMake masih menyimpan konfigurasi lama. Contoh masalah: BLAS not found missing: BLAS_LIBRARIES Penyelesaian: ``` rm -rf build ``` Kemudian configure semula. Sentiasa ingat: CMakeCache.txt menyimpan konfigurasi lama. **Bahagian 5 — Cross Compile x86_64 → ARM64** Contoh: PC Debian 12 x86_64 | | v Orange Pi ARM64 Kelebihan: Compile lebih cepat Tidak membebankan SBC Sesuai untuk production image **5.1 Install ARM64 cross compiler** ``` sudo apt install -y \ gcc-12-aarch64-linux--gnu\ g++-12-aarch64-linux-gnu sudo apt install -y \ gcc-13-aarch64-linux--gnu\ g++-13-aarch64-linux-gnu ``` Semak: ``` aarch64-linux-gnu-gcc --version ``` **5.2 Configure cross build** Bersihkan dahulu: ``` rm -rf build-arm ``` Kemudian: ``` cmake -B build-arm \ -G Ninja \ -DCMAKE_BUILD_TYPE=Release \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=aarch64 \ -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \ -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ 5.3 Compile ninja -C build-arm -j$(nproc) ``` Hasil: ``` ls build-arm/bin ``` **Bahagian 6 — Semak Architecture Binary** Gunakan: ``` file build-arm/bin/llama-server ``` Contoh output berjaya: ``` ELF 64-bit LSB pie executable, ARM aarch64, dynamically linked ``` Maksud: Output Maksud ELF 64-bit Binary 64-bit ARM aarch64 Untuk ARM64 dynamically linked Perlukan shared library PIE executable Linux security hardening Bahagian 7 — Semak Dependency .so Jangan guna ldd untuk cross binary Jika compile ARM64 tetapi check pada PC x86: ``` ldd llama-server ``` boleh gagal: ``` not a dynamic executable ``` Sebab: PC: x86_64 loader Binary: ARM64 loader Gunakan readelf aarch64-linux-gnu-readelf \ -d build-arm/bin/llama-server | grep NEEDED Contoh: ``` Shared library: [libllama.so] Shared library: [libggml.so] Shared library: [libstdc++.so.6] ``` Cari semua .so ``` find build-arm -name "*.so" ``` Contoh: ``` libllama.so libggml.so libggml-base.so libggml-cpu.so ``` Semak architecture: ``` file build-arm/bin/*.so ``` Output: ``` ARM aarch64 Bahagian 8 — Dynamic vs Static Binary ``` Semak: ``` file llama-server ``` Contoh dynamic: ``` dynamically linked ``` Perlu: lib*.so Contoh static: statically linked Tidak perlu .so. **Bahagian 9 — Installation ke Linux** Pilihan standard Binary: ``` /usr/local/bin ``` Library: ``` /usr/local/lib ``` Contoh: ``` sudo cp llama-server /usr/local/bin/ sudo cp llama-cli /usr/local/bin/ sudo cp *.so /usr/local/lib/ sudo ldconfig ``` Pilihan appliance / embedded Untuk SBC: ``` /opt/llama.cpp/ llama-server llama-cli libllama.so libggml.so ``` Kemudian: ``` export LD_LIBRARY_PATH=/opt/llama.cpp ``` Sesuai untuk: Orange Pi kiosk AI edge inference node **Bahagian 10 — Deploy ke Orange Pi** Copy: ``` scp build-arm/bin/llama-server \ orangepi:/usr/local/bin/ scp build-arm/bin/llama-cli \ orangepi:/usr/local/bin/ ``` Jika perlu: ``` scp build-arm/bin/*.so \ orangepi:/usr/local/lib/ ``` Pada Orange Pi: ``` sudo ldconfig ``` Semak: ``` uname -m ``` Expected: ``` aarch64 ``` **Bahagian 11 — Cadangan Production Architecture** Untuk sistem AI agent: +----------------+ | Go Agent | | Tool Router | +-------+--------+ | | HTTP API | v +----------------+ | llama-server | | llama.cpp | +----------------+ | | GGUF Model Kelebihan: Go agent tidak perlu embed model Model boleh tukar tanpa rebuild llama.cpp boleh upgrade sendiri Mudah scale ke banyak node Kesimpulan Workflow yang stabil: Native cmake -B build -G Ninja -DCMAKE_BUILD_TYPE=Release ninja -C build Cross Compile ARM64 sudo apt install gcc-aarch64-linux-gnu g++-aarch64-linux-gnu rm -rf build-arm cmake -B build-arm \ -G Ninja \ -DCMAKE_SYSTEM_NAME=Linux \ -DCMAKE_SYSTEM_PROCESSOR=aarch64 \ -DCMAKE_C_COMPILER=aarch64-linux-gnu-gcc \ -DCMAKE_CXX_COMPILER=aarch64-linux-gnu-g++ ninja -C build-arm Verification file llama-server aarch64-linux-gnu-readelf -d llama-server | grep NEEDED find . -name "*.so" Dengan proses ini, satu mesin Debian 12/13 boleh menjadi build server untuk menghasilkan node AI ARM64 seperti Orange Pi, Raspberry Pi, atau edge inference appliance.