llama-go: Run LLMs locally with Go

Go bindings for llama.cpp, enabling you to run large language models locally with GPU acceleration. Production-ready library with thread-safe concurrent inference and comprehensive test coverage. Integrate LLM inference directly into Go applications with a clean, idiomatic API.

This is an active fork of go-skynet/go-llama.cpp, which hasn't been maintained since October 2023. The goal is keeping Go developers up-to-date with llama.cpp whilst offering a lighter, more performant alternative to Python-based ML stacks like PyTorch and/or vLLM.

Documentation:

• Getting started: Installation guide | API guide | Build options
• Migration: v1 to v2 migration guide for upgrading from the old API
• API reference: pkg.go.dev (complete godoc with examples)
• Examples: Working code examples for chat, streaming, embeddings, speculative decoding
• Upstream: llama.cpp for model formats and engine details

Quick start

# Clone with submodules
git clone --recurse-submodules https://github.com/liminalpurple/llama-go
cd llama-go

# Build the library (default: static linkage, single-binary friendly)
make libbinding.a

# Download a test model
wget https://huggingface.co/Qwen/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf

# Run an example
export LIBRARY_PATH=$PWD C_INCLUDE_PATH=$PWD
go run ./examples/simple -m Qwen3-0.6B-Q8_0.gguf -p "Hello world" -n 50

Basic usage

package main

import (
    "context"
    "fmt"
    llama "github.com/liminalpurple/llama-go"
)

func main() {
    // Load model weights (ModelOption: WithGPULayers, WithMLock, etc.)
    model, err := llama.LoadModel(
        "/path/to/model.gguf",
        llama.WithGPULayers(-1), // Offload all layers to GPU
    )
    if err != nil {
        panic(err)
    }
    defer model.Close()

    // Create execution context (ContextOption: WithContext, WithBatch, etc.)
    ctx, err := model.NewContext(
        llama.WithContext(2048),
        llama.WithF16Memory(),
    )
    if err != nil {
        panic(err)
    }
    defer ctx.Close()

    // Chat completion (uses model's chat template)
    messages := []llama.ChatMessage{
        {Role: "system", Content: "You are a helpful assistant."},
        {Role: "user", Content: "What is the capital of France?"},
    }
    response, err := ctx.Chat(context.Background(), messages, llama.ChatOptions{
        MaxTokens: llama.Int(100),
    })
    if err != nil {
        panic(err)
    }
    fmt.Println(response.Content)

    // Or raw text generation
    text, err := ctx.Generate("Hello world", llama.WithMaxTokens(50))
    if err != nil {
        panic(err)
    }
    fmt.Println(text)
}

When building, set these environment variables:

export LIBRARY_PATH=$PWD C_INCLUDE_PATH=$PWD

The default static linkage links every llama.cpp library directly into your Go binary — no LD_LIBRARY_PATH setup, no shared libraries to ship alongside the executable. If you'd rather use shared libraries, build with BUILD_LINKAGE=shared make libbinding.a and pass -tags shared_lib to go build; the shared mode bakes -Wl,-rpath,$ORIGIN into the binary so the .so files only need to sit next to the executable.

Key capabilities

Text generation and chat: Generate text with LLMs using native chat completion (with automatic chat template formatting) or raw text generation. Give vision and audio models images and audio clips through their multimodal projectors. Ask decision models typed questions for calibrated answers. Extract embeddings for semantic search, clustering, and similarity tasks.

GPU acceleration: Supports NVIDIA (CUDA), AMD (ROCm), Apple Silicon (Metal), Intel (SYCL), and cross-platform acceleration (Vulkan, OpenCL). Eight backend options cover virtually all modern GPU hardware, plus distributed inference via RPC.

Production ready: Comprehensive test suite with almost 400 test cases and CI validation including CUDA builds. Active development tracking llama.cpp releases - maintained for production use, not a demo project.

Advanced features: Model/Context separation enables efficient VRAM usage - load model weights once, create multiple contexts with different configurations. Cache common prompt prefixes to avoid recomputing system prompts across thousands of generations. Serve multiple concurrent requests with a single model loaded in VRAM (no weight duplication). Stream tokens via callbacks or buffered channels (decouples GPU inference from slow processing). Speculative decoding for 2-3× generation speedup.

Architecture

The library bridges Go and C++ using CGO, keeping the heavy computation in llama.cpp's optimised C++ code whilst providing a clean Go API. This minimises CGO overhead whilst maximising performance.

Model/Context separation: The API separates model weights (Model) from execution state (Context). Load model weights once, create multiple contexts with different configurations. Each context maintains its own KV cache and state for independent inference operations.

Key components:

• wrapper.cpp/wrapper.h - CGO interface to llama.cpp
• model.go - Model loading and weight management (thread-safe)
• context.go - Execution contexts for inference (one per goroutine)
• Clean Go API with comprehensive godoc comments
• llama.cpp/ - Git submodule tracking upstream releases

The design uses functional options for configuration (ModelOption vs ContextOption), explicit context creation for thread safety, automatic KV cache prefix reuse for performance, resource management with finalizers, and streaming callbacks via cgo.Handle for safe Go-C interaction.

Licence

MIT