llama.cpp -- C/C++ LLM Inference Framework Guide

Official Documentation

Source	URL
GitHub Repository	https://github.com/ggml-org/llama.cpp
C API Header (llama.h)	https://github.com/ggml-org/llama.cpp/blob/master/include/llama.h
C++ RAII Wrappers	https://github.com/ggml-org/llama.cpp/blob/master/include/llama-cpp.h
Build Instructions	https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md
Server Documentation	https://github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md
Quantization Tool	https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md
GGUF Specification	https://github.com/ggml-org/ggml/blob/master/docs/gguf.md
Function Calling Docs	https://github.com/ggml-org/llama.cpp/blob/master/docs/function-calling.md
Multimodal Docs	https://github.com/ggml-org/llama.cpp/blob/master/docs/multimodal.md
Examples Directory	https://github.com/ggml-org/llama.cpp/tree/master/examples
HuggingFace GGUF Hub	https://huggingface.co/docs/hub/gguf-llamacpp
Llama-Unreal Plugin	https://github.com/getnamo/Llama-Unreal

What is llama.cpp?

llama.cpp is a pure C/C++ LLM inference engine with minimal dependencies, designed for high-performance local inference across CPUs and GPUs. Key properties:

• MIT licensed, extremely active development (~daily releases, currently b8766+)
• Widest hardware support: NVIDIA (CUDA), AMD (ROCm/Vulkan), Apple (Metal), Intel (SYCL/Vulkan), Qualcomm (OpenCL), ARM, WebGPU
• GGUF model format: single-file, mmap-compatible, 40+ quantization types from 1.5-bit to 16-bit
• Built-in HTTP server: OpenAI-compatible API, Anthropic Messages API, streaming, function calling, multimodal
• Language bindings: Python (llama-cpp-python), Go, Rust, C#, Node.js, Java, Swift, and more

Quick Start

Run a model via server (fastest path)

# Install
brew install llama.cpp    # macOS/Linux
winget install llama.cpp  # Windows

# Start server with a HuggingFace model
llama-server -hf bartowski/Llama-3.3-70B-Instruct-GGUF:Q4_K_M -ngl 99

# Query via curl
curl http://localhost:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"messages":[{"role":"user","content":"Hello"}],"temperature":0.8}'

Embed in a C++ project (library usage)

#include "llama.h"

// 1. Init backends and load model
ggml_backend_load_all();
auto params = llama_model_default_params();
params.n_gpu_layers = 99;
llama_model * model = llama_model_load_from_file("model.gguf", params);

// 2. Create context
auto ctx_params = llama_context_default_params();
ctx_params.n_ctx = 4096;
llama_context * ctx = llama_init_from_model(model, ctx_params);

// 3. Tokenize, decode, sample (see C API reference for full pattern)

Build from source with GPU

git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON      # or GGML_VULKAN=ON, GGML_METAL=ON
cmake --build build --config Release -j

Core Architecture

llama.cpp/
  include/
    llama.h          # C API (primary interface)
    llama-cpp.h      # C++ RAII wrappers (unique_ptr aliases)
    ggml.h           # Tensor computation library
  common/
    common.h         # High-level convenience layer
  tools/
    server/          # HTTP server (llama-server)
    quantize/        # Model quantization tool
  examples/
    simple/          # Minimal inference example
    simple-chat/     # Multi-turn chat example

Inference pipeline:

Model (GGUF file)
  -> llama_model_load_from_file()  [load + mmap weights]
  -> llama_init_from_model()       [create context with KV cache]
  -> llama_tokenize()              [text -> tokens]
  -> llama_decode()                [run transformer, fill KV cache]
  -> llama_get_logits()            [get output probabilities]
  -> llama_sampler_sample()        [select next token]
  -> llama_token_to_piece()        [token -> text]
  -> repeat decode/sample loop until EOS

Quantization Quick Reference

Type	Bits	Quality	Recommended For
Q4_K_M	~4.5	Good	Default choice -- best quality/size balance
Q5_K_M	~5.5	Very good	When ~20% more space is acceptable
Q8_0	8	Near-lossless	Validation, quality-critical tasks
IQ4_XS	~4.25	Best at 4-bit	With imatrix, slightly smaller than Q4_K_M
Q3_K_M	~3.5	Acceptable	RAM-constrained scenarios
IQ2_XS	~2.3	Reduced	Extreme compression (needs imatrix)
F16	16	Reference	Full precision baseline

# Quantize a model
llama-quantize model-f16.gguf model-q4km.gguf Q4_K_M

# Convert from HuggingFace
python convert_hf_to_gguf.py /path/to/hf_model --outfile model.gguf

GPU Backends

Backend	Flag	Hardware
CUDA	GGML_CUDA=ON	NVIDIA GPUs
Metal	auto on macOS	Apple Silicon
Vulkan	GGML_VULKAN=ON	Cross-platform (NVIDIA/AMD/Intel)
HIP	GGML_HIP=ON	AMD GPUs (ROCm)
SYCL	GGML_SYCL=ON	Intel GPUs

GPU offloading (-ngl 99) is the single most impactful performance setting.

Server API

The built-in server provides OpenAI-compatible endpoints:

Endpoint	Purpose
POST /v1/chat/completions	Chat (streaming supported)
POST /v1/completions	Text completion
POST /v1/embeddings	Embeddings
POST /v1/messages	Anthropic Messages API
POST /completion	Native API with full parameter control
GET /health	Health check
GET /metrics	Prometheus metrics

Features: function calling (--jinja), grammar constraints (--grammar/--json-schema), multimodal (vision+audio), parallel decoding, speculative decoding, router mode, LoRA hot-swap, built-in web UI.

CMake Integration

# Method 1: Subdirectory (embedding)
add_subdirectory(vendor/llama.cpp)
target_link_libraries(myapp PRIVATE llama ggml)

# Method 2: Installed package
find_package(llama REQUIRED)
target_link_libraries(myapp PRIVATE llama)

Detailed Reference Documents

• C/C++ API reference: See references/c-api-reference.md for complete llama.h function signatures, types, enums, structs, sampling chain API, and full working examples
• Build system & integration: See references/build-and-integration.md for all CMake options, GPU backend builds, library linking methods, Docker, and package managers
• Server REST API: See references/server-api.md for all HTTP endpoints, CLI flags, environment variables, curl examples, function calling, grammar constraints, and Python client usage
• GGUF & quantization: See references/quantization-guide.md for GGUF format spec, all 40+ quantization types, imatrix generation, model conversion, hardware requirements, and supported architectures
• Performance & GPU backends: See references/performance-and-backends.md for backend comparison, CUDA/Vulkan/Metal optimization, memory management, speculative decoding, and hardware recommendations
• Unreal Engine integration: See references/unreal-engine-integration.md for Llama-Unreal plugin, custom C++ integration with Build.cs, HTTP server approach, performance in-game, and common UE pitfalls