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AI-centric cloud for ML practitioners
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Explaining Soperator, Nebius’ open-source Kubernetes operator for Slurm
Explaining Soperator, Nebius’ open-source Kubernetes operator for Slurm
We’ve built an open-source Kubernetes operator that runs and manages Slurm clusters as K8s resources. From this article, you’ll find out…
Mikhail Mokrushin
Sep 24
How transformers, RNNs and SSMs are more alike than you think
How transformers, RNNs and SSMs are more alike than you think
By uncovering surprising links between seemingly unrelated LLM architectures, a way might be paved for effective idea exchange and boosting…
Stanislav Fedotov
Sep 6
Cloud architects team: lessons learned
Cloud architects team: lessons learned
It’s a little quieter now — as is often the case in August — in terms of big AI conferences, announcements, and product launches. Everyone…
Levon Sarkisyan
Aug 28
Choosing storage for deep learning: a comprehensive guide
Choosing storage for deep learning: a comprehensive guide
The rapid evolution of deep learning models has brought about unprecedented growth in both their size and complexity. This trend, while…
Igor Ofitserov
Aug 15
Mixtures of Experts and scaling laws
Mixtures of Experts and scaling laws
Mixture of Experts (MoE) has become popular as an efficiency-boosting architectural component for LLMs. In this blog post, we’ll explore…
Stanislav Fedotov
Aug 13
How large models can abstract rules: a research by LIMS
How large models can abstract rules: a research by LIMS
How well can LLMs abstract rules and how to test such ability? Here’s a research by the London Institute for Mathematical Sciences (LIMS).
Leonid Kliuev
Aug 12
Nebius AI at the frontiers of physics and maths
Nebius AI at the frontiers of physics and maths
Donated computing time is helping researchers at the London Institute model the universe and formulate new conjectures.
Ananyo Bhattacharya
Aug 5
In-house LLM R&D: Nebius AI’s secret ingredient for truly AI‑centric cloud
In-house LLM R&D: Nebius AI’s secret ingredient for truly AI‑centric cloud
Severe GPU scarcity and struggles with MLOps are forcing ML engineers to more and more divert focus from model development. Such a shift…
Anastasia Zemskova
Jul 30
Data preparation for LLMs: techniques, tools and our established pipeline
Data preparation for LLMs: techniques, tools and our established pipeline
Why are datasets for LLMs so challenging? As with any machine learning task, data is half the battle (the other half being model efficiency…
Yury Anapolskiy
Jun 27
Fundamentals of LoRA and low-rank fine-tuning
Fundamentals of LoRA and low-rank fine-tuning
In the next installment of our series of deep technical articles on AI research, let’s switch our attention to the famous LoRA, a low-rank…
Stanislav Fedotov
Jun 17
Slurm vs Kubernetes: Which to choose for your ML workloads
Slurm vs Kubernetes: Which to choose for your ML workloads
Scaling your machine learning workloads will eventually require resource orchestration. Among the multiple solutions available, the most…
Panu Koskela
Jun 10
Demo: applying retrieval-augmented generation with open tools
Demo: applying retrieval-augmented generation with open tools
Retrieval-augmented generation (RAG) is a technique that enhances language models by combining generative AI with a retrieval component…
Roman Bunin
Apr 18
Transformer alternatives in 2024
Transformer alternatives in 2024
With this article, we are starting a new category on our blog, the one dedicated to AI research. Expect these posts to be very technical…
Stanislav Fedotov
Apr 4
Tips and tricks for performing large model checkpointing
Tips and tricks for performing large model checkpointing
There are various aspects to optimize when training large models. It often lasts weeks and involves managing billions of rows of data, with…
Simon Karasik
Mar 12
Joining AI research community: overview for industry experts
Joining AI research community: overview for industry experts
The global network of ML engineers is divided into two parts: industrial and academic. The flow of information, methods of interaction, and…
Leonid Kliuev
Feb 21
Which AI conferences to attend in 2024?
Which AI conferences to attend in 2024?
The beginning of the year is a good time to make plans, right? If you’re an ML engineer, researcher, or technical manager, now is the…
Leonid Kliuev
Jan 24
NVIDIA H100 and other GPUs — which are relevant for your ML workload?
NVIDIA H100 and other GPUs — which are relevant for your ML workload?
My name is Igor, I’m the Technical Product Manager for IaaS at Nebius AI. Today, I’m going to break down the differences between NVIDIA’s…
Igor Ofitserov
Nov 21, 2023
On Kubernetes significance for ML/AI engineers
On Kubernetes significance for ML/AI engineers
In the ever-evolving landscape of machine learning, the infrastructure that supports it plays a pivotal role, standing out as the key…
Levon Sarkisyan
Nov 20, 2023
Designing hardware for hosting AI-tailored GPUs
Designing hardware for hosting AI-tailored GPUs
The creation of ML models powering intelligent products and services hinges heavily on powerful graphics cards. The ongoing deep learning…
iznam
Nov 20, 2023
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