Fedora 43 源码编译安装 llama.cpp 教程（支持 CUDA 显卡加速）
本文首发于 启鑫的黑板报，转载请注明出处。
在 Linux 环境下彻底释放显卡的高效推理潜能，源码编译 llama.cpp 带有 CUDA 加速的版本是最佳选择。本文将手把手带你在最新的 Fedora 43 系统（基于 CUDA 13.2 环境）中，从配置环境变量开始，一步步完成源码构建，打造完美契合你显卡算力的私有化大模型部署神器。
1. 配置 CUDA 环境变量
在编译之前，我们需要将 CUDA 的编译器 nvcc 和相关动态库路径写入系统的环境变量中，确保 CMake 能够准确识别。
# 1. 将 CUDA 路径写入全局 shell 配置中
cat >> ~/.bashrc << 'EOF'
# NVIDIA CUDA Toolkit
export PATH=/usr/local/cuda/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH
EOF
# 2. 使环境变量立即生效
source ~/.bashrc
# 3. 验证环境变量是否生效
nvcc --version
which nvcc
提示：如果 nvcc --version 正确打印出了 CUDA 的版本号，说明路径配置成功，可以进行下一步。
2. 获取源码与依赖安装
确保系统里安装了基本的构建工具（cmake 和 gcc-c++）。
# 安装基础编译依赖
sudo dnf install -y cmake gcc-c++ git
# 进入你的开发目录并克隆官方仓库
mkdir -p ~/Dev && cd ~/Dev
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
3. 利用 CMake 执行 CUDA 加速编译
为了让 llama.cpp 在运行时获得最佳性能，我们通过 CMake 指定启用 CUDA，并且将算力精准锁定在 86（对应 RTX 30 系列）。
# 1. 安全起见，先清理可能存在的旧构建缓存
rm -rf build
# 2. 配置 CMake 编译选项
cmake -B build \
-DGGML_CUDA=ON \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CUDA_ARCHITECTURES=86 \
-DGGML_CUDA_F16=ON \
-DGGML_NATIVE=OFF
# 3. 启动多核并行编译
cmake --build build -j$(nproc)
编译参数解析
- -DGGML_CUDA=ON：核心开关，开启 NVIDIA GPU 的 CUDA 计算后端。
- -DCMAKE_BUILD_TYPE=Release：开启高级编译器优化，大幅提升运行期推理速度。
- -DCMAKE_CUDA_ARCHITECTURES=86：极其关键！ 指定 GPU 架构。RTX 30 系列（如 3060 Ti、3070、3090）填写 86；如果你使用的是 RTX 40 系列，请改为 89。
- -DGGML_NATIVE=OFF：关闭纯本地 CPU 强绑定优化，提升编译出的二进制文件在不同 CPU 架构间的兼容性。
4. 验证编译结果
编译完成后，可执行文件会被输出到 build/bin/ 目录下。我们需要验证程序能正常跑起来，且 GPU 加速已成功启用。
4.1 验证基础版本信息
./build/bin/llama-cli --version
如果输出显示了相应的版本号与 Git Commit 标识，说明程序本身编译无误。
4.2 核心验证：检查 CUDA 参数是否就绪
./build/bin/llama-cli -h | grep -E "ngl|gpu|cuda|flash"
运行后如果在输出中看到了如下关键控制参数，说明你的显卡已经可以整装待发了：
-ngl N, --n-gpu-layers N number of layers to store in VRAM
--sm N, --split-mode N how to split the model across multiple GPUs
--flash-attn enable Flash Attention
特别是 -ngl（--n-gpu-layers），这是我们在运行大模型时，用来指定将多少层大模型放进显存（VRAM）中计算的核心参数。
5. 部署大模型服务（llama-server）
编译成功后，llama.cpp 提供了一个非常现代且支持 OpenAI 兼容接口的 Web 服务端程序，叫做 llama-server。我们可以直接用它把 GGUF 模型挂载为 API 服务。
# 启动 API 服务端
./build/bin/llama-server \
-m /path/to/your-model.gguf \
--host 0.0.0.0 \
--port 8080 \
-ngl 99 \
--ctx-size 4096
核心启动参数调优说明：
- --host 0.0.0.0 和 --port 8080：绑定所有网络接口并监听 8080 端口，不仅本地能调，局域网内的其他设备（如 HomeLab 中的 OpenWebUI）也能直接跨机器访问。
- -ngl 99：告诉程序把模型的全部层（写 99 足够覆盖绝大多数 7B/14B 模型）都完全卸载到的显存中。如果模型太大导致显存溢出（OOM），可以适当调低该数值（如 24 或 32）。
- --ctx-size 4096：分配 4096 长度的上下文窗口。
启动后如何玩转？
- 原生 Web 界面：直接在浏览器打开 http://localhost:8080，即可使用 llama.cpp 内置的轻量化聊天面板。
- 对接第三方客户端：该服务完美兼容 OpenAI API 标准，接口地址为 http://localhost:8080/v1，可无缝接入 OpenWebUI、Cherry Studio 或 NextChat 等主流前端。
本文首发于 启鑫的黑板报，转载请注明出处。
编译过程
# 配置 CMake 编译选项
liqixin@fedora:~/Dev/llama.cpp$ cmake -B build -DGGML_CUDA=ON \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CUDA_ARCHITECTURES=86 \
-DGGML_CUDA_F16=ON \
-DGGML_NATIVE=OFF
CMAKE_BUILD_TYPE=Release
-- Warning: ccache not found - consider installing it for faster compilation or disable this warning with GGML_CCACHE=OFF
-- CMAKE_SYSTEM_PROCESSOR: x86_64
-- GGML_SYSTEM_ARCH: x86
-- Including CPU backend
-- x86 detected
-- Adding CPU backend variant ggml-cpu: -msse4.2;-mf16c;-mfma;-mbmi2;-mavx;-mavx2 GGML_SSE42;GGML_F16C;GGML_FMA;GGML_BMI2;GGML_AVX;GGML_AVX2
-- CUDA Toolkit found
-- The CUDA compiler identification is NVIDIA 13.2.78 with host compiler GNU 15.2.1
-- Detecting CUDA compiler ABI info
-- Detecting CUDA compiler ABI info - done
-- Check for working CUDA compiler: /usr/local/cuda/bin/nvcc - skipped
-- Detecting CUDA compile features
-- Detecting CUDA compile features - done
-- Using CMAKE_CUDA_ARCHITECTURES=86 CMAKE_CUDA_ARCHITECTURES_NATIVE=86-real
-- Could NOT find NCCL (missing: NCCL_LIBRARY NCCL_INCLUDE_DIR)
-- Warning: NCCL not found, performance for multiple CUDA GPUs will be suboptimal
-- CUDA host compiler is GNU 15.2.1
-- Including CUDA backend
-- ggml version: 0.12.0
-- ggml commit: 4f0e43da6
-- Found OpenSSL: /usr/lib64/libcrypto.so (found version "3.5.4")
-- Performing Test OPENSSL_VERSION_SUPPORTED
-- Performing Test OPENSSL_VERSION_SUPPORTED - Success
-- OpenSSL found: 3.5.4
-- UI: derived HF_UI_VERSION=b9282
-- UI: embedded with source: provisioned
-- Generating embedded license file for target: llama-common
-- Configuring done (2.2s)
-- Generating done (0.1s)
-- Build files have been written to: /home/liqixin/Dev/llama.cpp/build
# 执行编译（Build）
liqixin@fedora:~/Dev/llama.cpp$ cmake --build build -j$(nproc)
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[ 90%] Linking CXX executable ../../bin/llama-speculative-simple
[ 90%] Built target llama-speculative-simple
[ 90%] Building CXX object tools/parser/CMakeFiles/llama-debug-template-parser.dir/debug-template-parser.cpp.o
[ 90%] Linking CXX executable ../../bin/llama-convert-llama2c-to-ggml
[ 90%] Built target llama-convert-llama2c-to-ggml
[ 90%] Linking CXX shared library ../../bin/libllama-batched-bench-impl.so
[ 90%] Building CXX object tools/parser/CMakeFiles/llama-template-analysis.dir/template-analysis.cpp.o
[ 90%] Linking CXX executable ../../bin/llama-gguf-split
[ 90%] Built target llama-batched-bench-impl
[ 90%] Building CXX object tools/tts/CMakeFiles/llama-tts.dir/tts.cpp.o
[ 90%] Built target llama-gguf-split
[ 90%] Linking CXX executable ../../bin/llama-tokenize
[ 90%] Building CXX object tools/mtmd/CMakeFiles/llama-mtmd-cli.dir/mtmd-cli.cpp.o
[ 90%] Built target llama-tokenize
[ 90%] Building CXX object tools/mtmd/CMakeFiles/llama-mtmd-debug.dir/debug/mtmd-debug.cpp.o
[ 90%] Linking CXX executable ../../bin/llama-debug
[ 90%] Built target llama-debug
[ 90%] Building CXX object tools/cvector-generator/CMakeFiles/llama-cvector-generator.dir/cvector-generator.cpp.o
[ 90%] Linking CXX executable ../../bin/llama-speculative
[ 90%] Built target llama-speculative
[ 91%] Building CXX object tools/export-lora/CMakeFiles/llama-export-lora.dir/export-lora.cpp.o
[ 92%] Linking CXX executable ../../bin/llama-mtmd-debug
[ 92%] Built target llama-mtmd-debug
[ 93%] Linking CXX shared library ../../bin/libllama-quantize-impl.so
[ 94%] Building CXX object tools/fit-params/CMakeFiles/llama-fit-params-impl.dir/fit-params.cpp.o
[ 94%] Built target llama-quantize-impl
[ 94%] Building CXX object tools/results/CMakeFiles/llama-results.dir/results.cpp.o
[ 94%] Linking CXX static library libgguf-model-data.a
[ 94%] Built target gguf-model-data
[ 94%] Building CXX object examples/diffusion/CMakeFiles/llama-diffusion-cli.dir/diffusion-cli.cpp.o
[ 94%] Linking CXX executable ../../bin/llama-export-lora
[ 95%] Linking CXX executable ../../bin/llama-cvector-generator
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[ 95%] Linking CXX executable ../../bin/llama-mtmd-cli
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[ 95%] Built target llama-export-lora
[ 95%] Building CXX object tools/quantize/CMakeFiles/llama-quantize.dir/main.cpp.o
[ 95%] Built target llama-cvector-generator
[ 95%] Building CXX object tests/CMakeFiles/test-gguf-model-data.dir/test-gguf-model-data.cpp.o
[ 95%] Linking CXX executable ../../bin/llama-quantize
[ 95%] Built target llama-mtmd-cli
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[ 95%] Building CXX object tests/CMakeFiles/test-quant-type-selection.dir/test-quant-type-selection.cpp.o
[ 95%] Building CXX object tests/CMakeFiles/export-graph-ops.dir/export-graph-ops.cpp.o
[ 95%] Linking CXX shared library ../../bin/libllama-fit-params-impl.so
[ 95%] Built target llama-quantize
[ 95%] Building CXX object tests/CMakeFiles/test-quant-type-selection.dir/get-model.cpp.o
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[ 95%] Built target llama-fit-params-impl
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[ 95%] Linking CXX executable ../../bin/llama-fit-params
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[ 95%] Built target test-chat-auto-parser
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[ 97%] Built target llama-results
[ 97%] Built target test-gguf-model-data
[ 97%] Built target llama-completion
[ 97%] Linking CXX shared library ../../bin/libllama-perplexity-impl.so
[ 97%] Linking CXX executable ../../bin/llama-diffusion-cli
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[ 98%] Built target test-quant-type-selection
[ 98%] Built target llama-perplexity
[ 98%] Linking CXX executable ../bin/test-jinja
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[ 98%] Linking CXX static library libserver-context.a
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[ 98%] Building CXX object tools/server/CMakeFiles/llama-server-impl.dir/server-models.cpp.o
[ 98%] Building CXX object tools/server/CMakeFiles/llama-server-impl.dir/server.cpp.o
[ 98%] Building CXX object tools/server/CMakeFiles/llama-server-impl.dir/server-http.cpp.o
[ 98%] Building CXX object tools/cli/CMakeFiles/llama-cli-impl.dir/cli.cpp.o
[ 98%] Building CXX object tests/CMakeFiles/test-chat.dir/get-model.cpp.o
[ 98%] Building CXX object tests/CMakeFiles/test-chat.dir/test-chat.cpp.o
[ 99%] Linking CXX executable ../../bin/llama-tts
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[ 99%] Linking CXX shared library ../../bin/libllama-cli-impl.so
[ 99%] Built target llama-cli-impl
[ 99%] Building CXX object tools/cli/CMakeFiles/llama-cli.dir/main.cpp.o
[100%] Linking CXX executable ../../bin/llama-cli
[100%] Built target llama-cli
[100%] Linking CXX shared library ../../bin/libllama-server-impl.so
[100%] Built target llama-server-impl
[100%] Building CXX object tools/server/CMakeFiles/llama-server.dir/main.cpp.o
[100%] Building CXX object app/CMakeFiles/llama-app.dir/llama.cpp.o
[100%] Linking CXX executable ../../bin/llama-server
[100%] Built target llama-server
[100%] Linking CXX executable ../bin/llama
[100%] Built target llama-app
[100%] Linking CXX executable ../bin/test-chat
[100%] Built target test-chat
# 查看版本
liqixin@fedora:~/Dev/llama.cpp$ ./build/bin/llama-cli --version
version: 9282 (4f0e43da6)
built with GNU 15.2.1 for Linux x86_64
# 查看是否支持 GPU
liqixin@fedora:~/Dev/llama.cpp$ ./build/bin/llama-cli -h | grep -E "ngl|gpu|cuda|flash"
-fa, --flash-attn [on|off|auto] set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
-ngl, --gpu-layers, --n-gpu-layers N max. number of layers to store in VRAM, either an exact number,
-mg, --main-gpu INDEX the GPU to use for the model (with split-mode = none), or for
single value is broadcast across all devices, default: 1024
--spec-draft-ngl, -ngld, --gpu-layers-draft, --n-gpu-layers-draft N
-st, --single-turn run conversation for a single turn only, then exit when done
liqixin@fedora:~/Dev/llama.cpp$
本文首发于 启鑫的黑板报，转载请注明出处。
