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  3. The Decoder47

    How Mistral and Nvidia use physics AI to accelerate industrial design

    Mistral and Nvidia join forces to combine Mistral Large 4 with physics AI, CUDA-X, Warp, and Omniverse, providing an integrated workflow for design, simulation, and AI training for the aerospace, automotive, and semiconductor industries. Mistral Large 4 has approximately one trillion parameters and is trained on 3800 Grace Blackwell GPUs, with plans to release as an open-weight model at the end of October.

  4. Ant Group inclusionAI · Hugging Face new models35

    inclusionAI/Ling-3.0-tiny-UBTree release: A fine-tuned model based on UBTree technology

    inclusionAI released the UBTree fine-tuned version of Ling-3.0-tiny, Ling-3.0-tiny-UBTree-B16, with 183.1M parameters, combining a five-layer DFlash backbone and a bigram selector, achieving 2.6-3.5 times speed improvement on benchmarks like GSM8K and MATH-500. This model needs to be used in conjunction with the target model. The UBTree adaptation for the open-source version of SGLang has not yet been released.

  6. Modal Blog75

    Modal Dojo: A library to easily start production-level LLM training.

    Modal Dojo is a library for easily performing production-level LLM training, supporting models from 500 million to 10 trillion parameters, providing automatically optimized training recipes and built-in observability dashboards. Users can select models, load data, and start training through simple prompts, supporting data sources such as Hugging Face and Harbor environments, and can adjust recipes through parameters.

  7. IBM Research Blog

    IBM vLLM-Hook MIA Plugin enables rapid runtime monitoring and control

    IBM releases vLLM-Hook MIA Plugin, implementing runtime monitoring and control in the vLLM inference engine, 20 times faster than its predecessor vLLM-Hook and 14 times faster than similar methods inserting code inside the model, with only a 2%-6% increase in latency. The plugin allows developers to observe and adjust model states in real-time, such as rejecting malicious prompts, marking hallucinatory facts, or editing response tone and accuracy, while almost not reducing performance.

  8. NVIDIA Blog62

    NVIDIA expands its family of open models to cover language, robotics, biology, and quantum computing

    NVIDIA expands its family of open models to cover language, robotics, biology, and quantum computing, including the new Nemotron 3.5 Super VL and Nemotron 3.5 Bolt.

    Why it matters: NVIDIA has rolled out open models across four lines: language, robotics, biology, and quantum, allowing readers to judge the coverage of its open-source ecosystem based on this.

  9. Mistral AI50

    Mistral collaborates with NVIDIA to advance AI in engineering

    Mistral is collaborating with NVIDIA to integrate AI models with NVIDIA accelerated computing and physics modeling tools, accelerating engineering simulations and enabling closed-loop AI-driven design. The collaboration will launch public benchmarks and open datasets, standardizing the evaluation of physics and agent AI in computer-aided engineering, and leveraging NVIDIA Omniverse for 3D simulations and resource creation.

  10. NVIDIA Blog55

    NVIDIA Confidential Computing expands, providing secure extension capabilities for cutting-edge model builders across global infrastructure

    NVIDIA Confidential Computing protects models and data execution through hardware root isolation and encrypted authentication, adding Confidential Bare Metal deployment options, enabling cutting-edge model builders to run sensitive workloads on third-party infrastructure while retaining model data control.

  11. NVIDIA Blog58

    NVIDIA European partners expand computing capabilities, building sovereign, open, and secure AI agent cloud ecosystems.

    NVIDIA European partners are expanding regional computing capabilities to support model training and inference, helping developers build agentic and physical AI applications. Nscale achieves 37% more GPUs and 50% higher inference throughput using the NVIDIA DSX platform, Crusoe develops a liquid-cooled AI factory for low-latency inference, and Nebius adopts the NVIDIA Groq 3 LPX to accelerate sensitive applications.

  12. NVIDIA Blog

    NVIDIA showcased its full lineup of local AI hardware: RTX Spark, DGX Spark, and DGX Station, helping to power next-generation intelligent agents.

    NVIDIA showcased its full lineup of local AI hardware at GTC Berlin, with RTX Spark now available to support personal agents on Windows PCs, DGX Spark enabling developers to build and scale Linux applications, and DGX Station designed for Linux enterprise teams to run cutting-edge workloads, with the Windows version launching in December.

  13. Databricks Blog

    Databricks AI Runtime officially released

    Databricks releases AI Runtime (AIR), bringing managed GPU infrastructure directly into its platform, allowing customers to train high-demand models at their enterprise data location. AIR provides GPU infrastructure, developer tools, and data governance features, supporting large-scale training, rapid iteration, and load safety, with customers not needing to manage a separate GPU stack.

  14. NVIDIA Technical Blog65

    How NVIDIA OpenShell Reference Architecture Ensures Security and Governance for AI Agent Workloads at Scale

    NVIDIA OpenShell Reference Architecture ensures the security of AI agent workloads at scale by defining security boundaries and governance policies. The architecture allows agents to access files, tools, and the internet while performing complex tasks and enables dynamic collaboration. It mitigates risks associated with long-running agents through enhanced access control, audit logs, and permission management.

  15. AWS Machine Learning Blog58

    vLLM enhances the TTFT performance of multi-round agent workloads through bidirectional KV transmission.

    vLLM's bidirectional KV transmission mechanism reduces redundant calculations by allowing the decoding node to return previously computed KV cache to the pre-filled node in subsequent dialogue turns. In multi-round benchmark tests on AWS EC2 P5en instances, it improved the TTFT of Qwen3-32B by 1.6x and zai-org/GLM-5-FP8 by 3.3x. This feature is implemented by extending the NIXL KV connector and utilizing a KV cache-aware router to coordinate KV reuse across dialogue turns.

  18. AWS Machine Learning Blog58

    TwelveLabs' Pegasus 1.5 is now available on Amazon Bedrock

    TwelveLabs' Pegasus 1.5 video language model is now available on Amazon Bedrock, which can convert long videos into structured, timestamped metadata, supports analyzing the entire video based on user-defined standards and returns JSON data of matching segments, analysis duration extended to two hours per request, no pre-indexing required and data remains within AWS boundaries.

  21. AWS Machine Learning Blog34

    Building an AI agent semantic layer on AWS using Strategy Mosaic and Amazon Bedrock AgentCore.

    Strategy Mosaic, as a semantic layer, models C360 domains on Amazon Redshift and Amazon Aurora without ETL using two linked models. Its metric syntax captures the business definition of agent reasoning and exposes the model to AgentCore-hosted Strands agents via the Mosaic MCP server, supporting user identity propagation to achieve end-to-end row-level and object-level security.

  26. AWS Machine Learning Blog40

    Fine-tune GPT-OSS-20B on Amazon SageMaker HyperPod for multilingual inference

    GPT-OSS-20B is an open-source inference model that thinks in English by default, and only 22% met the target language requirements in multilingual inference tests. By using supervised fine-tuning (SFT) and reinforcement learning (GRPO) on Amazon SageMaker HyperPod, the team can improve the model's inference capabilities in French, German, Spanish, and Italian, where SFT is responsible for primary behavior teaching and GRPO optimizes when there is room for improvement.

  27. AWS Machine Learning Blog38

    How Amazon Quick and Amazon Bedrock AgentCore implement conversational intelligent decision-making in supply chains

    Amazon Quick combined with Amazon Bedrock AgentCore provides supply chain teams with real-time data access across systems. Users can get cross-system answers in seconds through conversational queries, such as identifying high-income SKUs and costs affected by port closures. Quick serves as the interface layer, and AgentCore as the intelligence layer, connecting backend systems like ERP, monitoring, and procurement via the MCP protocol to achieve a complete process from problem discovery to generating emergency purchase requests, all operations requiring manual confirmation.

  31. MIT Technology Review · AI58

    Goodfire researcher Nick Cammarata releases disruptive blog: The nature of neural networks might be easier to understand than previously thought

    Goodfire researcher Nick Cammarata published a blog post stating that LLMs might be easier to understand than previously thought, causing shockwaves in the AI world. Cammarata believes that the mechanisms of neural networks may not be as difficult to decipher as previously thought, a view that is particularly noteworthy against the backdrop of frequent LLM proxy out-of-control incidents.

  35. TechCrunch · AI

    Google releases open-source PyTorch version TorchTPU to attack Nvidia AI developer ecosystem

    Google announced the release of its highly anticipated PyTorch version TorchTPU as an open-source project, allowing AI application developers to run their applications on Google's Tensor Processing Units (TPUs) chips.

    Same story · 2 outlets

    How the accounts differ:MarkTechPost states TorchTPU is a native PyTorch backend for cloud TPU designed to replace PyTorch/XLA and supports eager-first execution; TechCrunch mentions it as an attack on Nvidia's AI developer ecosystem.