Vol. I · No. 163TUE, SEP 29, 2026
Archive

The Archive

Search the full wire by company, model, lab, or keyword. Every story we have ever aggregated.

Introducing OpenAI for India

OpenAI expands into India with local infrastructure, enterprise partnerships, and workforce development initiatives.

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Unlock Massive Token Throughput with GPU Fractioning in NVIDIA Run:ai

As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges... As AI workloads scale, achieving high throughput, efficient resource usage, and predictable latency becomes essential. NVIDIA Run:ai addresses these challenges through intelligent scheduling and dynamic GPU fractioning. GPU fractioning is wholly delivered by NVIDIA Run:ai in any environment—cloud, NCP, and on-premises. This post presents the joint benchmarking effort between NVIDIA and AI… Source

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Topping the GPU MODE Kernel Leaderboard with NVIDIA cuda.compute

Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to... Python dominates machine learning for its ergonomics, but writing truly fast GPU code has historically meant dropping into C++ to write custom kernels and to maintain bindings back to Python. For most Python developers and researchers, this is a significant barrier to entry. Frameworks like PyTorch address this by implementing kernels in CUDA C++—either handwritten or by leveraging libraries… Source

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How NVIDIA Extreme Hardware-Software Co-Design Delivered a Large Inference Boost for Sarvam AI’s Sovereign Models

As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost... As global AI adoption accelerates, developers face a growing challenge: delivering large language model (LLM) performance that meets real-world latency and cost requirements. Running models with tens of billions of parameters in production, especially for conversational or voice-based AI agents, demands high throughput, low latency, and predictable service-level performance. Source

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Introducing EVMbench

OpenAI and Paradigm introduce EVMbench, a benchmark for evaluating AI agents on smart contract vulnerability detection and exploitation.

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Build AI-Ready Knowledge Systems Using 5 Essential Multimodal RAG Capabilities

Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms,... Enterprise data is inherently complex: real-world documents are multimodal, spanning text, tables, charts and graphs, images, diagrams, scanned pages, forms, and embedded metadata. Financial reports carry critical insights in tables, engineering manuals rely on diagrams, and legal documents often include annotated or scanned content. Retrieval-augmented generation (RAG) was created to ground… Source

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Scaling social science research

OpenAI releases GABRIEL, an open-source toolkit using GPT to convert qualitative text and images into quantitative data for social science research.

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Introducing GPT-5.3-Codex-Spark

OpenAI releases GPT-5.3-Codex-Spark, a real-time coding model with 15x faster generation and 128k context, in research preview.

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