The rise of open-source AI is reshaping how machine learning and large language models are built, scaled, and deployed. In this guide, we explore the top open-source AI platforms of 2026 including Kubeflow, MLflow, Ray, Hugging Face, and ONNX that are driving the next generation of innovation.

Transfer learning vs fine-tuning explained. Find out which approach fits your data, compute, and accuracy needs for better ML outcomes.

GreenMind—an open-source reasoning large language model (LLM) developed by GreenNode—has now become the first Vietnamese LLM integrated into NVIDIA NIM

Use case from a major robotics and automation company to train and fine-tune large-scale Large Language Models (LLMs), scale their operations seamlessly, and unlock a new generation of AI-powered service robots

Artificial intelligence (AI) is becoming pervasive in the enterprise. To support these AI applications, businesses look toward optimizing AI servers and performance networks. Many enterprises prioritize GPU acceleration while overlooking a critical component: Storage infrastructure. According to Gartner, most enterprises will not have to build new storage infrastructure for generative artificial intelligence (GenAI) because they will fine-tune existing large language models (LLMs) instead of training new models. However, large-scale GenAI deployments will still require distinct storage performance and data management capabilities for the data ingestion, training, inference, and archiving stages of the GenAI workflow, rather than relying on traditional cloud storage.

This blog provides a proof of concept (PoC) for building and training Large Language Models (LLMs) using open-source tools

We're thrilled to announce a series of essential updates designed to make your LLM (Large Language Model) training experiences smoother, faster, and more cost-effective. CPU Instances, SSH Access, Network Enhancements and More!

In this article, GreenNode will share our experience with multinode setups—specifically using NVIDIA’s H100 GPUs and InfiniBand technology—to address specific use cases for our clients.

Building an LLM from scratch is not just about coding and data—it’s about navigating uncharted waters with limited resources and an unforgiving clock ticking in the background.