Q3 results in short (non official version)
1. we sold all we had
2. we build much more
Roman Chernin
418 posts
- Today we signed the almost 20 billions 5y contract with MSFT @nebiusai now in the highest league. nebius.com/newsroom/nebiu…
- As I get it from X: the last (and only) thing left for @nebiusai to make investors 100% happy is merch.
- We are proud to announce our first deployment of @nvidia Blackwell Ultra in the UK. Accelerated by NVIDIA Blackwell Ultra GPUs and NVIDIA Quantum-X800 InfiniBand networking, the deployment delivers unprecedented performance for generative AI and future foundation model
- This is a big step and it gives much more than just revenue. It gives the fuel to build product we all came to @nebiusai for. And deliver more value to our beloved customers.
- Today we’re launching Nebius 3.0 — “Aether”, our new version of cloud platform named after the Greek god of light. v1.0: first true multi-tenant AI cloud v2.0: unmatched performance for training & inference v3.0 : enterprise-grade trust — SOC 2 Type II, granular IAM, full infra
- Guess how long after signing the pivotal deal it took our CEO to ask: “Why hasn’t this (relatively small) new customer received an answer yet?” Less than 1 hour. That’s the obsession we need to keep as we build a true multi-customer cloud. The MSFT deal is the fuel for growth.
- We’re launching Nebius Token Factory (@nebiustf), the next evolution of Nebius AI Studio. The platform built for production-scale inference. 🔥 Now the best way to run scaled inference workloads: - Vertivaly integrated with Cloud (flexible access to capacity, all the best
- Cloud is the post-sales business. The hard work starts the minute the contract is signed. But with this team in @nebiusai I’m confident. No words to express the gratitude 🧡
- What podcasts to participate in September-October to speak about Nebius full stack approach and building the truely software driven growth?
- We’re launching Nebius Token Factory (@nebiustf), the next evolution of Nebius AI Studio. The platform built for production-scale inference. 🔥 Now the best way to run scaled inference workloads “Running inference at scale with healthy economics requires efficient on-demand
