Show HN: Llmconfig – configfile and CLI for local LLM
llmconfig is a tool that simplifies managing local large language models using a single YAML configuration file and CLI interface. It supports multiple backends like llama.cpp, stable-diffusion.cpp, and whisper.cpp, offering OpenAI-compatible APIs and automatic hardware detection. The tool can be installed via script, Go, or built from source, with comprehensive documentation available.
- ▪llmconfig allows users to manage local LLM inference through a unified CLI and YAML config file.
- ▪It supports hardware-aware profiles for NVIDIA, Apple Silicon, AMD, Intel GPU, and CPU, selecting them automatically at runtime.
- ▪The tool provides OpenAI-compatible APIs and can run models as drop-in replacements, with an optional gateway to unify access on a single port.
- ▪Backend binaries are downloaded automatically, eliminating the need for a local build chain.
- ▪llmconfig includes 18 built-in templates and supports image generation and speech recognition through stable-diffusion.cpp and whisper.cpp.
Opening excerpt (first ~120 words) tap to expand
llmconfig Local Large Model Config — manage local inference with llama.cpp, stable-diffusion.cpp, and whisper.cpp from a single YAML file and a single CLI. llmconfig up gemma # or just: llmc up gemma ✓ gemma is ready at http://127.0.0.1:8080 Ships with a shorter llmc alias — every command works with either binary name. Why llmconfig One YAML, three backends. Define a model once; llmconfig handles downloading, starting, stopping, restarting, and monitoring. Hardware-aware. Profiles for NVIDIA, Apple Silicon, AMD, Intel GPU, and CPU are auto-selected at runtime. OpenAI-compatible. Models run as drop-in replacements for the OpenAI API. The optional gateway command exposes every running model on a single port. No build chain.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.