llama.go

llama.go is a pure Go implementation of LLaMA large language model inference — essentially llama.cpp rewritten in Golang, with no C dependencies. The open-source project has around 1,400 stars on GitHub and appeals to Go developers who want local LLM inference inside the Go ecosystem.

Overview

When LLaMA leaked and llama.cpp proved that large models could run on consumer CPUs, most ports stayed in C or wrapped the C library. llama.go instead implements the transformer inference engine natively in Go, loading GGML-format model files for LLaMA, Alpaca, Vicuna, and GPT4All-style models and generating text with them. The practical benefit is deployment simplicity: a single static Go binary, cross-compiled the way Go programs are, embeddable in Go services and CLI tools without cgo or native library wrangling. The project implements quantization support and the sampling controls you would expect, and it serves as a readable reference for how transformer inference works when written in a garbage-collected, high-level language. Its most recent updates were in 2024, so it reflects the early LLaMA model generation rather than the newest architectures, but for Go shops experimenting with local inference of that era’s models it remains a distinctive option.

Key Features

• LLaMA inference implemented in pure Go — no cgo or C libraries
• Loads GGML model files for LLaMA, Alpaca, Vicuna, and GPT4All models
• Single-binary deployment and easy embedding into Go programs
• Quantized model support for running on modest hardware
• Readable reference implementation of transformer inference in Go
• Cross-platform anywhere Go cross-compiles

💳 Pricing

Free. llama.go is open source and free to use; models run on your own hardware.

Who Is It For

Go developers who want to run or embed LLaMA-generation models locally without leaving the Go toolchain or depending on C libraries.