llama.cpp

Easily integrate llama.cpp with Python apps using llama-cpp-python. Load GGUF models and run inference with C++ performance directly in Python.

Learn how llama.cpp enables hybrid CPU+GPU inference for large models. Discover techniques for layer splitting, memory management, and pipeline parallelism for enhanced performance.

Learn to use GBNF grammar files in llama.cpp for constrained generation. Ensure syntactically valid outputs with the --grammar-file flag or JSON API.

Learn to build llama.cpp with CMake for various backends. Enable hardware acceleration using GGML flags and compile with cmake --build for optimized performance.

Debug slow token generation in llama.cpp by enabling performance monitoring and identifying bottlenecks in prompt evaluation decoding sampling or KV-cache management.

Learn to implement reranking models for search with llama.cpp. Discover how to score document relevance using classification-head models via HTTP endpoints.

Learn how to use memory mapping in llama.cpp to run models larger than VRAM. Stream weights from disk, loading only active pages on demand for efficient processing.

Accelerate GPU inference in llama.cpp with the Vulkan backend. Compile with GGML_VULKAN=ON, install the Vulkan SDK, and use `--device Vulkan0` for faster AI processing.

Learn to handle multimodal models with image input in llama.cpp. Load vision-enabled GGUF models, use projector files, and send base64 images via API or CLI for powerful AI.

Benchmark LLM inference performance with llama-bench in llama.cpp. Measure prompt speed, generation throughput, and latency for GGUF models. Optimize your AI models today.

Learn to implement custom chat templates in llama.cpp using Jinja strings. This guide shows how to initialize and apply templates for conversation mode, enhancing your AI interactions.

Explore Llama.cpp quantization types to compress LLMs. Understand accuracy vs performance tradeoffs for faster inference and reduced memory footprint.