Andriy Burkov’s Post

Absolutely agree with Andriy here! When fine-tuning LLMs with 100k+ token examples in private cloud becomes a standard capability, businesses will move beyond the generic—and that’s when real, enterprise-grade benefits will materialize. Custom models trained on rich, domain-specific data will unlock targeted accuracy, security, and nuanced workflows that no out-of-the-box solution can deliver. The moment this process is commoditized, scalable, and secure... this is the tipping point for large language models to deliver transformative business impact that’s truly tangible. #AIValue #LLM #FutureOfAI

Recently, I explored running an open-source large language model locally using Ollama on my MacBook — specifically gpt-oss:20b. What surprised me was how seamless it felt: a quick pull, a simple run, and I was chatting with a 20B parameter model right from my terminal. The magic here isn’t just the novelty — it’s the privacy and control. Everything runs on-device, with no cloud dependency, meaning your prompts and data stay entirely yours. Open-source AI feels a lot like the early Linux days: community-driven, fast-evolving, and closing the gap with big cloud models step by step. It’s not about replacing GPT-4 today, but about a future where AI is democratized and runs locally for everyone. #AI #OpenSource #LLM #Ollama #GenerativeAI #GPTOss

Did we just witness the next leap in enterprise AI? Oracle has officially integrated OpenAI’s GPT-5 into its cloud and database services, marking a significant pivot point for data centers and every business leveraging enterprise-grade AI. �� By embedding GPT-5 directly into core applications, Oracle isn’t just supporting AI, it’s making advanced language models the backbone of mission-critical operations. Think: hyper-personalized customer experiences and real-time insights at unprecedented scale. This level of AI integration fundamentally changes #energy, #cooling, and hardware requirements. How will data centers evolve to handle exponentially more powerful AI queries while maintaining efficiency? We're entering an era where the integration of powerful AI isn’t just a feature — it’s becoming the foundation of digital transformation. https://lnkd.in/gDFQiNrm

In one year, OpenRouter went from 133B → 3.4 Trillion tokens/week. And that’s just one [small] slice of the AI compute economy, big players OpenAI, Google, MSFT, AWS... process far more. Sonnet 4 tops the chart, even without all the tokens burned by Claude Code users. The scale of AI inference is impressive as it is today, without the gigawatt scale data centers that will come online soon.

Dealing with slow-downs in any context can be a headache. In a large-scale machine learning models a single underperforming node—or a straggler—can force thousands of other nodes to wait, dragging down your entire training time. This "fail-slow" problem is hard to find, often taking engineers days to manually debug. Our new feature straggler detection available through Cluster Director on Google Cloud changes the game. It builds a real-time communication map of your nodes, then traces the slowdown back to its source in minutes. This pinpoints the exact straggler, saving you precious hours and accelerating your ML workflows. To see it in action 🎬 https://lnkd.in/gJRPBGZs Learn more here 👇 https://lnkd.in/g5m4jFQx

Anthropic just raised the bar on context windows, giving enterprises the ability to send up to 1 million tokens (about 750,000 words or 75,000 lines of code) to Claude Sonnet 4 in a single prompt. For business leaders, this means AI can now process entire product catalogs, massive codebases, or long policy documents in one go without breaking them into chunks. That unlocks faster insights, smoother automation, and more reliable outputs for teams that live in complex data. With long context now available through Amazon Bedrock and Google Cloud’s Vertex AI, Anthropic is doubling down on enterprise use cases at a time when competition with OpenAI’s GPT-5 is heating up.

Cirrascale Cloud Services is proud to be Ai2’s cloud computing partner, providing managed services for the new hardware infrastructure that will power this effort. Ai2 has received $152M in combined support from the U.S. National Science Foundation (NSF) and NVIDIA to build a national, fully open AI ecosystem through the Open Multimodal AI Infrastructure (OMAI) project. Read the announcement: https://lnkd.in/gbsqS6zq

AI apps are moving from single prompts to complex, multi-agent systems. How do you design for scale? On Sept 16, 12:00–13:00 CET (6:00–7:00 AM ET), Theo van Kraay will present at the European Microsoft Fabric Community Conference: Designing for Scale in Fabric: How Azure Cosmos DB Powers the Full Lifecycle of Multi-Agent AI Apps Learn how #AzureCosmosDB supports agent memory, retrieval-augmented generation (RAG), semantic reranking, and multitenancy — all inside Microsoft Fabric. ➡️ https://msft.it/6040sdsdg #FabConEurope #Azure #AzureCosmosDB

Your AI app works locally. Great. But can it handle millions of queries across the globe without choking? LangChain’s integration with Azure Cosmos DB for NoSQL gives you vector search with the same scale, security, and speed you use for production workloads. That’s how you ship AI for real. 📖 https://msft.it/6046sP2Bw #Azure #CosmosDB #LangChain #AI #MachineLearning #VectorSearch