Year: 2026
Type: article
Abstract: This article examines the practical aspects of local deployment of Large Language Models (LLM) on personal computing systems. It provides an overview of the local inference architecture, the llama.cpp software suite, and the GGUF model storage format, which ensures the efficient use of modern language models on user hardware. The principles of model quantization and their impact on RAM and video memory requirements are considered, and recommendations for choosing the quantization level depending on the computer hardware configuration are provided. The article describes the software and hardware requirements, the specifics of using NVIDIA graphics accelerators with CUDA technology, and a methodology for preparing an environment for local model execution. Practical recommendations are presented for choosing llama.cpp builds, loading models, configuring launch parameters, organizing a local inference server, and using the OpenAI-compatible API for integrating language models into application software systems. The advantages of the local approach related to data confidentiality, autonomous operation, and flexibility of model configuration are discussed, as well as its limitations due to the computing resources of personal computers. The results obtained can be used by developers, researchers, educators, and specialists interested in deploying personal artificial intelligence systems without the use of cloud infrastructure.
Language: Russian
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220135190