Blog

Even larger workloads are welcome! We raised quotas last month, so you can now access up to 32 NVIDIA H200 GPUs on demand in both the US and Europe. Speaking of Europe, we’ll be revealing many of our plans for the region at NVIDIA GTC Paris next week.

Model distillation is a powerful technique in machine learning where a compact “student” model learns to replicate the behavior of a larger, more complex “teacher” model within the given task. In this tutorial, we demonstrate how to perform distillation by using Nebius AI Studio, to create a grammar-correcting model.

A detailed analysis of the incident on May 5th, 2025 that led to an outage of the S3 service in the eu-north1 region.

In the past weeks, we have made several significant observability-related improvements to the Nebius AI Cloud. Among them: advanced monitoring metrics in our web console and API, out-of-the-box Grafana dashboards, as well as monitoring and logging features, allowing customers to upload their custom metrics and logs to our cloud.

Our AI R&D team presents SWE-rebench, a new benchmark for evaluating agentic LLMs on a continuously updated and decontaminated set of real-world software engineering tasks mined from real GitHub repositories. Our goal with this benchmark is to make evaluation of software engineering LLMs more transparent, reproducible and focused on core model capabilities.

Explore how to get Qwen3 running on Nebius AI Cloud with SkyPilot and SGLang. This setup enables you to deploy both the massive 235B MoE model and the efficient 32B variant seamlessly, leveraging high throughput, cost-effective scaling and robust multilingual support.

Let’s walk through how to get Llama 4 running on Nebius AI Cloud (recently integrated with SkyPilot) by using SGLang as the serving framework. This combo provides high throughput, efficient memory usage and none of the typical deployment headaches.

This month, we revealed some of the partnerships and collaborations we’ve been engaged in, such as with Meta and DDN. We also showcased work done by our customers like vLLM and Brave, as well as participants of our Research credits program — including Stanford, USC and others.

As LLM-powered agents become more complex, integrating them with tools, APIs, and private data sources remains a major challenge. Model Context Protocol (MCP) offers a clean, open standard for connecting language models to real-world systems through a modular, plug-and-play interface. In this article, we explore how MCP works.

In this short research blog post, we discuss how RL-finetuned reasoning language models can be used as a better alternative to regression-based critics when performing parallel trajectory search in test time, and how to train such models to prioritize precision over recall.

Large language models (LLMs) rely on fast data processing and distributed computing, making the efficiency of data processing tools a critical factor. Apache Spark streamlines text data preparation, enables parallel processing of massive datasets and simplifies the development of scalable ML workflows. This article explores Spark’s architecture, its advantages for data preparation and solutions to common limitations when working with LLM-scale models.