Llama.Go

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llama.go is an Open Source & Models tool that reimplements llama.cpp in pure Golang. It provides a GGML-compatible implementation for LLM debugging and inference, allowing ML hackers to work with large GPT models.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is llama.go?

llama.go is a pure Golang reimplementation of the popular llama.cpp framework, designed for machine learning enthusiasts and developers. It aims to provide a GGML-compatible environment for debugging and inferring large GPT models directly in Golang, offering an alternative to lower-level languages like C++. The project focuses on performance and elegance, enabling users to work with models like LLaMA-7B, 13B, 30B, and 65B. Key features include multi-threading, cross-platform compatibility (Mac, Linux, Windows), and optimizations for ARM NEON and x64 AVX2. It also supports modern GGUF V3 model format, INT8 quantization, and offers an embedded REST API for production use, allowing parallel inference with configurable pods and threads.

Best used for

Ideal for developers who need to run and debug large language models directly in Golang, integrate LLM inference into their applications via a REST API, and optimize performance across various CPU architectures. Especially valuable for those seeking a pure Go implementation for GGML-compatible models.

Common actions

run LLM models

debug LLM inference

integrate LLM API

optimize LLM performance

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Capabilities

Key features

GGML-compatible implementation
Pure Golang
LLM inference
Multi-threading support
Cross-platform compatibility
Embedded REST API
AVX2/NEON optimizations

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What kind of LLaMA models does llama.go support?

llama.go supports LLaMA-7B, 13B, 30B, and 65B models, including LLaMA V2 7B/13B/34B/70B architectures. It also supports the modern GGUF V3 model format and INT8 quantization for larger models.

What are the hardware requirements for running llama.go?

For LLaMA-7B models, you'll need at least 32GB of RAM, and for LLaMA-13B, 64GB. It leverages CPU optimizations like ARM NEON for Apple Silicon and x64 AVX2 for Intel/AMD, with future plans for AVX512 and GPU support.

Can llama.go be used in production environments?

Yes, llama.go includes an embedded HTTP server exposing a REST API. This allows for parallel inference with configurable 'pods' and 'threads' parameters, making it suitable for integrating LLM capabilities into real-world applications.

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Also listed in

This tool also appears in

Research & Education › Scientific Computing

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