Search “NVIDIA”

rgeKernel; its agent completed 42 iterations in about 22 hours and produced 32 kernel versions, ultimately achieving 1.098x performance on the DeepSeek V4.1 FlashMLA Prefill on an NVIDIA B200, at 472 µs versus the 518 µs baseline. The official FlashMLA implementation remained closed throughout; the agent could only run and time its black-box CLI and could not read source code, documentation, or profiling data. The open-sourced content includes the optimized DeepSeek V4.1 fused prefill kernel, th

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7 billion (about EUR 23.9 billion), led by Schwarz Group, the parent company of Lidl and Kaufland, with participation from existing investors including the Canadian government and Nvidia. Cohere had previously merged with Heidelberg-based Aleph Alpha—in effect, Cohere acquired the latter—and Heidelberg continues to serve as a research and development base. Cohere focuses on Sovereign AI for enterprises and governments, and its technology can run in private clouds, on proprietary infrastructure,

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Eclypsium demonstrated, with NVIDIA's support, that an attacker using a compromised DPU and lax memory isolation can modify runtime model weights during inference, causing the model's answers to change while the model files on disk and hash verification still appear normal. The experiment was conducted in an NVIDIA Grace Hopper environment and depended on conditions such as IOMMU passthrough and lax PCIe isolation, which the researchers explicitly noted are not Linux default settings. The report

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leased beta versions of two products, Akamai Inference and Akamai Agent Sandbox, allowing users to run AI models on Akamai Cloud and securely execute code generated by AI agents. Akamai Inference supports 12 open-weight models, including OpenAI's gpt-oss-120b, Google's Gemma 4, and Nvidia's Nemotron 3 Ultra, and provides an OpenAI-compatible API. Agent Sandbox is based on WebAssembly isolation technology and restricts code access to computing resources, network connections, and system functions.

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CNS reported Q3 revenue of 1.6643 trillion won and operating profit of 138.2 billion won, up 9.3% and 15.0% year-on-year respectively, with operating margin rising to 8.3%. Financial IT business revenue grew 21.3% to 408.2 billion won, while AI and cloud business revenue grew 9.3% to 961.5 billion won, accounting for 57.8% of total revenue. In September, the company decided to purchase equipment worth 381.4 billion won, including Nvidia's next-generation Vera Rubin GPUs, to build an AI factory.

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hts of Mistral Large 4 (codename le Chonk). This is a natively multimodal model with 1 trillion total parameters and 49 billion activated parameters, trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its own data center in Europe. The company says it scores 48% on the Artificial Analysis Coding Agent Index, roughly on par with GLM-5.3, but consumes 47% fewer Tokens per task on average. The model is released under a modified MIT license: personal, academic, fine-tuning, and conventiona

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or download after ending its public preview. The model supports text and image processing, with a focus on strengthening defensive cybersecurity, agentic capabilities, and geospatial and grounding capabilities, and is said to achieve state-of-the-art results on agentic benchmarks such as SWE-bench and Terminal Bench. The model was trained on more than 160 languages and is available via Mistral Studio, the Mistral API, OpenRouter, Hugging Face open weights, Vibe Code, and Nvidia-hosted endpoints.

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Teradyne Robotics and Sereact announced a formal partnership at Nvidia GTC Berlin 2026, running Sereact's physical AI platform Cortex on Universal Robots' collaborative robots for unstructured tasks such as returns processing and picking. The two parties have already delivered dual-arm systems composed of hundreds of UR robots to European customers including Zalando, and are autonomously processing returned goods at Zalando fulfillment centers in Greven, Germany, and Świebodzin, Poland. Cortex i

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rvices, Akamai Inference and Akamai Agent Sandbox: the former provides 12 open-weight models via an OpenAI-compatible API, with parameter counts ranging from 8B to 2.8T, including NVIDIA Nemotron 3 Ultra, OpenAI gpt-oss-120b, and Google Gemma 4; the latter runs code generated by AI agents in isolation based on WebAssembly, and is released as an open-source component of the Spin framework. Akamai has also expanded its NVIDIA Blackwell Ultra GPU clusters, with each eight-GPU node equipped with 2.3

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racle announced that it will deploy Google TPU on OCI, along with inference chips from OpenAI, Cerebras, SambaNova, and Positron AI, giving customers more computing options beyond Nvidia GPUs. Oracle says it will become the first hyperscale cloud provider other than Google Cloud to offer TPU; TPU and the Cerebras wafer-scale engine are planned to launch in 2027, and Positron Atlas is already available. At the same time, Oracle is expanding its partnership with Nvidia, with the Vera Rubin NVL72 s

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been released on Hugging Face, and lightweight testing can run on a single 24GB GPU. The model ranks first on olmOCR-Bench, OmniDocBench, and DocParsingBench, achieves a decoding speed of 558 tokens/s on a single NVIDIA RTX PRO 6000 GPU, and parses 100 pages end-to-end in about 101 seconds. Its post-training stage employs a recursive self-improvement (RSI) loop, in which a general-purpose VLM judges parsing results and generates newly annotated pages, eliminating the need for manual annotation.

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work as usual: in the Gemma 4 E4B robot dog test, text on a poster was treated as an instruction and changed its movement; in a hospital service robot simulation using Nemotron + NVIDIA Jetson AGX Orin, constructed audio inaudible to the human ear changed the simulated behavior. Researchers also injected messages into a ROS 2/DDS robot that was supposed to remain stationary during a robot bug bounty event, causing it to move. VicOne recommends testing safety limits with adversarial scenarios be

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of $2.9 billion, led by the Korea National Growth Fund with a $280 million investment. The company says its TCP chip, based on the RNGD architecture, consumes less than half the power of Nvidia GPUs and is compatible with PyTorch and vLLM; this round of funding will be used to expand production capacity for its next-generation TCP chips, with plans to produce 20,000 units this year and double that to 40,000 units in 2027, while also jointly developing a third-generation processor with Broadcom.

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al Large 4 (codename Le Chonk), which has 1 trillion parameters with 52 billion active parameters and native multimodal support. ML4 was trained in a European data center on 3,800 Nvidia Grace Blackwell GPUs, improving from 28% to 41% on Terminal-Bench 4.0, tying GLM-5.3 for the best coding-agent score while using an average of 47% fewer tokens per task. Open weights mean enterprises can deploy it on their own hardware and behind their firewalls, and the model offers three reasoning modes: low,

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continued reinforcement learning over three weeks, raising the Terminal-Bench 4.0 score from 28% to 41%; the Artificial Analysis coding index is 48%, on par with GLM-5.3 while using an average of 47% fewer tokens per task. API pricing drops to $0.70 per million input tokens and $2.10 per million output tokens; the new Mistral Ecosystem Open-Weight License permits commercial use, but reselling model services requires a separate agreement, and the full version requires 8 Nvidia B200 or B300 GPUs.

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-site information when collective communication hangs, and can be reverted via TORCH DIST USE NCCL2=0 or nccl-legacy. This version also adds float8 e4m3fn and scaled mm support for Apple Silicon, and the new grouped mm improves MoE decoding throughput by 1.32–1.34x on M3 Ultra. The default wheel is upgraded to CUDA 13.2, while a CUDA 13.4 wheel is also provided with experimental support for NVIDIA Rubin GPUs; a ROCm 10.0 wheel is newly added, and Python 3.10 and macOS 14 are no longer supported.

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AnySpeechRecognizer supports engine switching on top of the Apple SpeechAnalyzer API, with options including NVIDIA Parakeet TDT 0.6B v3 (supporting 25 European languages), Moondream post-trained Parakeet Ultra, the English version Parakeet Unified, and Alibaba Qwen3-ASR (0.6B/1.7B, covering 30 languages and 22 Chinese dialects). These models have been converted to Core ML and published on the Hugging Face Hub; Parakeet takes about 20–40 seconds to transcribe 1 hour of audio, while Qwen3-ASR is

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After a three-week preview, Mistral AI has released the weights of Mistral Large 4 on Hugging Face. This multimodal model was trained on 3,800 Nvidia accelerators and has a parameter count of one trillion, and it is currently still difficult to run on a gaming PC or a single GPU. Mistral says it leads GLM-5.3 and Kimi K3 on the cybersecurity benchmark CyberGym-E2E, and is on par with GLM-5.3 on the agentic coding benchmark Terminal Bench 4.0, but there is no independent evaluation yet; preview p

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LC image ray:train-ml-cuda-v1.1, which comes pre-integrated with Ray 2.58.0, PyTorch 2.13.0, CUDA 13.0.2, NCCL 2.29.7, and EFA along with the AWS OFI NCCL plugin, enabling distributed training on EKS without the need to build images yourself. The example uses PyTorch FSDP to fine-tune Qwen2.5-1.5B on 8 NVIDIA L4 GPUs across two g6.12xlarge instances, with cross-node communication going directly over EFA. The image is publicly hosted on Amazon ECR Public, so nodes can pull it without credentials.

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