Blog

While reinforcement learning drives agents to state-of-the-art performance, rejection fine-tuning serves as a powerful baseline. Stemming from our extensive experiments with different models and scaffoldings, we are sharing a dataset of 67k high-quality OpenHands trajectories from Qwen3 Coder 480B for research purposes. We also include two RFT checkpoints — Qwen3 Instruct 30B and 235B, achieving ~50% and ~60% on SWE-bench Verified respectively.

Bioscience seeks to unravel the structure of protein complexes that drive nearly every cellular process. Open-source tools and data banks are essential for accelerating advances in this field. With support from Nebius AI Cloud and a Nebius Academy fellowship, researchers at the University of California, San Francisco are adapting widely used protein modeling software to modern GPU cloud platforms, expanding access and impact for the scientific community.

This guide teaches the essentials of serving large language models with vLLM. It builds from foundational neural network concepts, like transformers and attention, to introduce practical inference workflows, explore useful vLLM features and offer hands-on, operational guidance for efficient deployments.

Europe’s first operational NVIDIA GB300 NVL72 systems are now live in Nebius AI Cloud. The Blackwell Ultra-based platform is fully deployed in our Finland data center, delivering supercomputer-class performance for large-scale training and high-throughput inference. This rollout expands the compute foundation available to teams building next-generation models across Europe.

Today, we’re expanding Nebius AI Cloud’s elasticity even further with the introduction of Capacity Blocks and Capacity Dashboard — two new capabilities that bring additional clarity to GPU capacity management and give users full confidence in the compute they rely on.

In this blog, we will take a look at how teams can deploy the four NVIDIA NIMs available now in our platform, how they can be consumed via their API and how they can fit into larger scientific workflows. It also covers the Virtual Screening Blueprint, which turns folding, molecule generation and docking into a ready workflow that becomes available in JupyterHub shortly after deployment.

This guide reveals the engineering patterns behind modern conversational platforms, and how Nebius Token Factory shapes open models, serving pipelines and GPU economics to keep quality stable at scale. The blueprint presented here is a proof of concept (PoC) grounded in work we conducted with a major conversational AI platform operating hundreds of thousands of concurrent chats. The details are anonymized, but the architectural lessons are real.

Post-training by Token Factory is now live, the missing layer between MVP and real production AI. Base models are great for demos, but production needs models trained on your data that are faster, cheaper, and aligned to your workflows. Today, Token Factory makes that possible for every team, without building ML infrastructure.

November at Nebius brought yet another multi-billion hyperscaler deal and further expansion of our data center footprint, new leading benchmark results and deeper platform ecosystem integrations.

Saturn Cloud is an AI development platform that is deployed from your Nebius console. It enables AI/ML teams to build and deploy models with their favorite tools, without reliance on engineering support. You can use it for free and watch your AI/ML team onboard and start shipping right away.

Launching biomolecular models at scale in a structured and safe environment is key for drug discovery teams to reach reliable, experimentally relevant insights faster. This article provides a practical, reproducible blueprint for running Boltz-2 inference in Nebius, from single-GPU experiments to scalable multi-node screening pipelines.

We’re proud to share the results of our participation in the MLPerf® Training v5.1 benchmark, where Nebius showcased strong performance across several configurations of the latest NVIDIA Blackwell and Blackwell Ultra systems. This round continues our commitment to transparency and collaboration with the MLCommons® community, as we work to ensure the highest quality standards for training and fine-tuning next-generation GenAI models.