Llama.go – port of llama.cpp to pure Go
It's April 12 - The First Man in Space* Day - and I'm releasing #ML framework I've been building for the last month :) It's written in #Go and allows #LLaMA #GPT inference having just regular PC - so no monster GPU cluster is needed to start experiment with: https://github.com/gotzmann/llama.go The V1 is using FP32 math only, but will work with AVX2 data types and INT8 quantisation soon. * The first man in space was Yuri Gagarin from USSR
Llama.go – port of llama.cpp to pure Go launched on April 12, 2023 with 25 votes, #1,523 of 4,853 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 3 years on.
In plain words
Llama.go is a Go implementation of llama.cpp that enables LLaMA and GPT model inference on standard computers without requiring specialized GPU hardware. Written in pure Go, the framework allows developers and researchers to experiment with large language models on regular PCs. Version 1 uses FP32 math, with planned support for AVX2 data types and INT8 quantization coming soon.
written from the facts on this page · September 2026
From the sources
llama.go is like llama.cpp in pure Golang! Contribute to gotzmann/llama.go development by creating an account on GitHub.
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Llama-dl – high-speed download of LLaMA, Facebook's 65B GPT model2023 · github.com · ▲343High-speed download of LLaMA, Facebook's 65B parameter GPT model - shawwn/llama-dl
Llama 3.1 70B on a single RTX 3090 via NVMe-to-GPU bypassing the CPUFeb 2026 · github.com · ▲395Hi everyone, I'm kinda involved in some retrogaming and with some experiments I ran into the following question: "It would be possible to run transformer models bypassing the cpu/ram, connecting the gpu to the nvme?" This is the result of that question itself and some weekend vibecoding (it has the linked library repository in the readme as well), it seems to work, even on consumer gpus, it should work better on professional ones tho

Tune LLaMa3.1 on Google Cloud TPUs2024 · github.com · ▲189Hey HN, we wanted to share our repo where we fine-tuned Llama 3.1 on Google TPUs. We’re building AI infra to fine-tune and serve LLMs on non-NVIDIA GPUs (TPUs, Trainium, AMD GPUs). The problem: Right now, 90% of LLM workloads run on NVIDIA GPUs, but there are equally powerful and more cost-effective alternatives out there. For example, training and serving Llama 3.1 on Google TPUs is about 30% cheaper than NVIDIA GPUs. But developer tooling for non-NVIDIA chipsets is lacking. We felt this pain ourselves. We initially tried using PyTorch XLA to train Llama 3.1 on TPUs, but it was rough: xla…
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