Yu Tian

❌ Microsoft is set to cut yet more roles in its home state of Washington, with software engineers once again among those hardest hit. 🔎 Filings show 305 workers in total will be affected. While this impacts less than 1% of the company's workforce, the latest cuts come just weeks after it announced plans to lay off nearly 6,000 staff globally. 📝 ITPro's Ross Kelly has more on the layoffs here. https://lnkd.in/e2qHE9Fn

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⚡️ Frontier open models run faster on #NVIDIA. New benchmarks show the NVIDIA #Blackwell platform maximizes #inference ROI by optimizing user interactivity and throughput per GPU. ⚙️ OpenAI gpt-oss-120b reaches over 60,000 tokens per second (TPS) per #GPU. ⚙️ AI at Meta Llama-3.3-70b tops 15,000 TPS per GPU. ⚙️ #DeepSeek #AI DeepSeek-R1 achieves over 10,000 TPS per GPU. Our continuous software improvements and extreme hardware-software co-design enable real-world AI inference to balance cost, efficiency, throughput, and responsiveness. 📈 Explore the interactive performance charts: https://bit.ly/3LbWgeU #GPT #DeepLearning #Education #Research

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Nvidia Challenges AI Workloads With New GPU: Optimized for large context data such as video, audio and text, this specialized GPU complements existing technology, bolstering throughput for AI inference.

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Building a Production-Ready RAG System for Persian Language Processing I’m excited to share insights from developing an advanced Retrieval-Augmented Generation system specifically engineered for Persian/Farsi document processing, leveraging cutting-edge ML infrastructure and architectural patterns. 🔬 Technical Architecture Highlights: Advanced RAG Implementation: ∙ Self-Corrective Agentic RAG with adaptive query rewriting and iterative refinement ∙ Hybrid search architecture combining dense embeddings with SPLADE sparse representations ∙ Multi-agent orchestration enabling context-aware document interaction and general conversation modes Production Infrastructure: ∙ NVIDIA Triton Inference Server deployment on H100 GPUs with vLLM backend ∙ Milvus vector database for high-performance similarity search ∙ PostgreSQL for structured data with MinIO object storage for scalable file management ∙ Model ensemble: Qwen2.5-72B-Instruct-AWQ, DeepSeek-OCR, and sentence-transformers ML Pipeline Innovations: ∙ Automated synthetic QA pair generation using LangGraph workflows ∙ Cross-encoder reranking (ms-marco-MiniLM-L6-v2) for precision optimization ∙ Dynamic prompt management system with A/B testing capabilities and versioning ∙ Fine-tuning pipeline with quality-scored synthetic datasets Engineering Principles: ∙ Clean Architecture with dependency injection and separation of concerns ∙ Docker containerization for reproducible deployments ∙ Comprehensive observability with metrics tracking and performance monitoring This project demonstrates how to architect enterprise-grade RAG systems that combine theoretical ML advancements with practical production considerations, particularly for low-resource languages. Open to discussing technical implementation details, architectural decisions, or challenges in deploying LLMs at scale. #MLEngineering #RAG #NLP #LLMs #VectorSearch #ProductionML #AI #DeepLearning #Persian #TritonInference #CleanArchitecture

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🚀 New Course: Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training Built in partnership with AMD and taught by Sharon Zhou, PhD, you'll learn how to use post-training to transform pretrained LLMs into the reliable systems behind developer copilots, support agents, and AI assistants. Across 5 modules, you'll explore: - Where post-training fits in the LLM lifecycle - Techniques such as fine-tuning, RLHF, reward modeling, PPO, GRPO, and LoRA - How to design evals, detect reward hacking, and red team for robustness - How to prepare datasets and create synthetic data - Production pipelines for deployment, go/no-go decisions, and feedback loops Enroll now: https://hubs.la/Q03QxM0Y0

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Reinforcement Pre-Training (Microsoft, June 2025) Paper: https://lnkd.in/eY4PCpUi Abstract: "In this work, we introduce Reinforcement Pre-Training (RPT) as a new scaling paradigm for large language models and reinforcement learning (RL). Specifically, we reframe next-token prediction as a reasoning task trained using RL, where it receives verifiable rewards for correctly predicting the next token for a given context. RPT offers a scalable method to leverage vast amounts of text data for general-purpose RL, rather than relying on domain-specific annotated answers. By incentivizing the capability of next-token reasoning, RPT significantly improves the language modeling accuracy of predicting the next tokens. Moreover, RPT provides a strong pre-trained foundation for further reinforcement fine-tuning. The scaling curves show that increased training compute consistently improves the next-token prediction accuracy. The results position RPT as an effective and promising scaling paradigm to advance language model pre-training."

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Frontier models that use reasoning to "think" during inference are generating 5X more AI tokens per year. ✨ "Inference is now a thinking process. And in order to teach AI how to think, reinforcement learning and very significant computation was introduced into post-training," said NVIDIA CEO Jensen Huang in a recent keynote. Reinforcement learning is increasing computation demands across all AI scaling laws: pre-training, post-training, and test-time scaling. Learn more about reinforcement learning and AI scaling ➡️

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In this post, we demonstrate how to integrate Amazon SageMaker HyperPod with Anyscale platform to address critical infrastructure challenges in building and deploying large-scale AI models. The combined solution provides robust infrastructure for...

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At Microsoft’s AI for Good Lab, we’re working with partners at the University of Washington, the Fred Hutchinson Cancer Center, and other institutions to explore whether #AI can improve breast cancer screening. Read about our latest study published in Radiology: https://msft.it/6046sDSKA

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Meta Neural Band | Our #neuralinterface demonstrates gesture recognition across diverse populations without recalibration—a paradigm shift from previous EMG systems requiring extensive setup. Read more on the sEMG research behind the Neural Band released at Connect yesterday: https://lnkd.in/emAe-Dwc https://lnkd.in/ekbh9M6e

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🚀 NVIDIA GPUs are the powerhouse behind AI breakthroughs from ChatGPT to autonomous vehicles driving faster, smarter, and scalable AI solutions worldwide. 🌐 #RecruitRidersTechnologies #AI #TechNews #Innovation #FutureIsNow #BrandChampions #WeAreRecruitRiders #SocialStars #EmployeeInfluencers

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"There is one technical advance that I think could completely yet again change the landscape for autonomous driving." Vincent Vanhoucke on Unsupervised Learning nearly one year ago. And here we are.. 🚀🚀🚀 Join the company that is pushing SOTA technology forward AND pulling it into production in the real world: https://lnkd.in/gAYKSJky ___ Unsupervised Learning podcast: https://lnkd.in/gNSNw9Kf Waymo's World Model: https://lnkd.in/grnubY_a

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