Articles

Ryax Technologies is joining Nutanix. Since our founding, Ryax has built an AI-driven compute orchestration and management platform designed to help enterprises make the most of their available GPU and CPU resources, through intelligent resource optimization and AI-aware smart scheduling. As agentic AI adoption accelerates, the challenge customers face has become clearer: getting seamless, reliable […]

Introduction We recently migrated a scheduled cloud pricing pipeline from GitLab CI to a Ryax workflow. The goal stayed the same: refresh cloud provider catalogs, build a consolidated CSV snapshot, derive pricing artifacts for on‑prem cost estimation, and publish stable outputs for downstream systems (including our scheduler). This post focuses on the data science /

Introduction This second part of the case study builds on the infrastructure and cost-optimization strategies described in Part 1 (read here), and focuses on the retrieval-augmented generation (RAG) pipeline that powers automated KPI extraction from financial reports. Technical implementation Core components Our KPI extraction pipeline is built on three fundamental components, each implemented as modular

Introduction As AI and large language models move into production, GPU-accelerated Kubernetes clusters have become the foundation of modern AI platforms. To support model training, inference, and end-to-end workflows, organizations continue to invest heavily in GPU, CPU, and memory resources. At the same time, a growing reality is becoming impossible to ignore: a significant portion

Background In today’s financial sector, the manual extraction of Key Performance Indicators (KPIs) from annual reports represents a significant operational bottleneck. Financial institutions process thousands of reports annually, each containing critical metrics buried within complex document structures. This process traditionally requires substantial human effort, is prone to errors, and struggles to scale with increasing document

Introduction The demand for computing power has never been higher. AI workloads continue to grow, data pipelines become more complex, and both users in production and innovation rely on increasingly large HPC platforms capable of handling more numerous and larger-scale calculations. Yet despite their impressive capabilities, computing centers such as cloud providers, on-prem infrastructures and

Introduction AI workloads are pushing organizations toward multi-cloud strategies, driven by rapid technological change and the search for affordable pricing. Challenges like GPU shortages, unpredictable cloud costs, data-governancy, and security rules make relying on a single cloud risky and potentially expensive. However, managing multiple clouds manually is painful, to say the least. It creates fragmentation,

Abstract In an era of rapid digital transformation, enterprises face growing demands for efficient, scalable, and cost-effective data analytics and AI processing solutions. Ryax offers a next-generation compute and workflow orchestration platform designed for the execution of compute intensive apps upon diverse infrastructure environments, including Cloud, Edge, on-premises, and HPC. This whitepaper introduces Ryax key

Open source LLM Llama 2 available for inference Llama2 inference in a few clicks Thanks to new LLM Llama2 availability for inference into Ryax platform, deploy and use Llama 2 without any code, in a few clicks ! The LLM is deployed on a serverless architecture – that can be on premise or on any

Fine-tune open source LLM on your data Having your custom and private LLM can be a key asset in the competition. Trained on your data, with your specific inputs, your LLM is more accurate than ever !  Ryax helps you at each step of your LLM journey fine-tuning of any open source LLM (here w’re using