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130

MODELS · 2 SOURCES · NVIDIA Developer · Hugging Face

NVIDIA BioNeMo releases RFD4-Proteina: reward-guided protein design model and NIM microservice

RFD4-Proteina, an open-model co-developed by NVIDIA BioNeMo and the Institute for Protein Design (UW), co-designs protein sequences and all-atom structures and uses reward-guided inference-time search to focus compute on promising candidates. The method uses a residue-level latent representation and engineered diffusion to cut sampling from ~400 to 60 steps, yielding ~3.8× faster generation and ~84% lower peak memory than RFD3 at 200 residues; the project provides an open GitHub model for development and a NIM microservice with an HTTP interface for rapid inference and automated campaigns (examples include PD-L1 binder design with hotspot constraints).

8.0

COMPANIES · 1 SOURCE · Mistral AI

Mistral and NVIDIA partner to build AI-native engineering workflows and public physics benchmarks

Mistral and NVIDIA are collaborating to integrate Mistral Large 4 (Le Chonk) with NVIDIA accelerated computing (CUDA‑X, Warp) and Omniverse for GPU‑accelerated 3D simulation, aiming to speed simulations and enable closed‑loop AI-driven design. The partnership will produce public benchmarks and open datasets for physics models and agentic AI in CAE/EDA, expand NVIDIA‑powered infrastructure for Mistral, and builds on prior work including Mistral NeMo and the Nemotron Coalition; Mistral Large 4 (~1T total / 49B active params) was trained on ~3,800 Grace Blackwell GPUs and its weights are planned for release at the end of October.

8.0

MODELS · 2 SOURCES · TechCrunch AI · Hugging Face

Nvidia unveils Nemotron 3.5 Super VL and lightweight Bolt models for enterprise agents and local AI PCs

Nvidia announced new open-weight models aimed at always-on agents and edge/PC deployment: Nemotron 3.5 Super VL (120B parameters) for enterprise agents, plus two lightweight Bolt models including Nemotron 3.5 Bolt 74B-A9B 24GB and the smaller Bolt E4B for local AI PCs and workstations. Nvidia says Super VL and Bolt 74B-A9B are available now (Bolt E4B coming later this month), claims Super VL can match larger Nemotron 3 Ultra on instruction-following and outperform some competing models in post-trained agent tasks, and named early users such as Skydance, EY, Ramp, CrowdStrike, Perplexity and Salesforce.

8.0

COMPANIES · 1 SOURCE · InfoQ AI, ML & Data Engineering

Google DeepMind makes SynthID Detector globally available and adds support for OpenAI, NVIDIA, Kakao

Google DeepMind announced global availability of SynthID Detector, a tool that checks images, audio, and video for SynthID cryptographic watermarks from partner providers (currently OpenAI, NVIDIA, and Kakao; Apple support is expected soon) via uploads to SynthID.com. The detector returns a yes/no signal but can produce false negatives when content is heavily altered and does not detect media from providers using other watermarking schemes (e.g., Meta or Microsoft).

7.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA details Sentry framework for hardware‑isolated AI agent monitoring on BlueField‑4

NVIDIA Sentry is a security framework in the OpenShell reference architecture that monitors AI agents from a hardware‑isolated trust domain on BlueField‑4 DPUs. It uses DOCA Tower (an Envoy‑based transparent proxy) for TLS termination and policy checks, DOCA Argus for real‑time memory introspection, DOCA Flow for in‑silicon layer‑4 firewalling, and specialized AI models to classify agent communications and contain misbehaving agents in milliseconds, while preserving attested telemetry for forensics and integration with enterprise security tools.

7.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA open-sources OpenShell reference architecture to secure and govern AI agent workloads

NVIDIA announced OpenShell, an open-source runtime and reference architecture that enforces least-privilege access and governance for long-running autonomous AI agents via components called Gateway, Supervisor, Sandbox, and a policy prover. NVIDIA positions OpenShell to run on its Vera CPUs, which it says are architected (Olympus cores, second-gen Scalable Coherency Fabric, LPDDR5X, PCIe Gen 6, confidential computing) to meet performance, bandwidth, I/O, and security needs of large-scale agent workloads.

8.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA publishes DSX POD reference design for factory-built, rack-scale AI infrastructure

NVIDIA has published the DSX POD reference design, a standardized, factory-built architecture that pre-integrates rack-scale compute, networking, storage, power and cooling into validated containerized units aligned with the Vera Rubin platform. The design is intended for partners to manufacture, test, ship and connect pre-integrated AI infrastructure to sites more predictably, reducing onsite integration complexity and enabling repeatable capacity expansion.

6.0

COMPANIES · 3 SOURCES · OpenAI · TechCrunch AI · The Decoder

OpenAI acquires Orchestra Bio to bring AI to life‑sciences R&D

OpenAI is acquiring Orchestra Bio to integrate AI-powered operations into life‑sciences R&D, aiming to help biopharma teams accelerate drug development and advance new treatments. The move positions OpenAI to extend its AI capabilities into laboratory and research workflows in the life‑sciences sector.

8.0

MODELS · 1 SOURCE · Hugging Face

NVIDIA releases Cosmos3-H-Dreams — real-time generative surgical simulator trained on cholecystectomies

NVIDIA announced Cosmos3-H-Dreams, a Cosmos 3–based generative simulator trained on synchronized clinical cholecystectomy video and robot kinematics from the CMR Surgical Versius platform that supports live kinematic control and runs interactively on a single RTX PRO 6000 GPU. The release uses a teacher-to-student distillation pipeline, two student checkpoints (four-step and two-step denoising schedules), and FlashDreams accelerated streaming inference to trade off visual quality, temporal consistency, and low-latency control.

7.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA cuTile and Jane Street optimize Mamba‑2 forward pass with three Tensor Core kernels

Jane Street implemented the Mamba‑2 state‑space sequence model using NVIDIA's CUDA Tile (cuTile) Python DSL and worked with NVIDIA to optimize three custom forward‑pass kernels—chunk_cumsum, fwd_pass_state, and compute_out—targeting Tensor Cores. Optimizations include register‑resident tiling, TF32 casts, DRAM latency hints, operand routing to Tensor Memory, TMA‑friendly memory layouts, and an autotuner for block size and occupancy per input shape.

6.0

MODELS · 4 SOURCES · Google · Google DeepMind · The Verge AI · The Decoder

Google launches Gemini 4 Argon, a new frontier model, globally

Google today made Gemini 4 Argon globally available to developers and enterprises via the API in AI Studio and the Gemini platform, and to Google AI Ultra/Pro subscribers in Antigravity, the Gemini app, and AI Mode in Search. Google positions Argon as a high‑precision frontier model for complex multi‑step tasks across coding, finance, legal, cybersecurity, and science, citing top ranking on the Vals Index, example deployments inside Google (Chrome refactor, ADK optimizations), technical speedups (xTB and Gemma inference via vLLM), and introductory pricing ($2 per million input tokens, $10 per million output tokens, with cached inputs 95% off).

9.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA announces RTX Spark platform for local AI development on single PCs

NVIDIA unveiled RTX Spark, a GB10-based laptop/compact-desktop platform with up to 128GB unified memory and 1 PFLOP FP4 compute aimed at letting developers run coding agents, fine-tune models, and deploy locally. The announcement highlights performance comparisons versus Intel Panther Lake, a Microsoft partnership delivering a PyTorch-CUDA build and Arm-native Python 3.15, and a purpose-built Nemotron 3.5 Bolt 74B-A9B 2-bit model optimized for local agents on RTX Spark.

8.0

STARTUPS · 1 SOURCE · TechCrunch AI

Ex‑NVIDIA exec Amulya Vishwanath says robotics is in a 'Lego era' and hasn't had a ChatGPT moment

Amulya Vishwanath, who led product marketing for NVIDIA’s robotics platforms for seven years and is now founder and managing partner at Techable VC, told TechCrunch’s Equity podcast that robotics remains in a "Lego era" with core components like actuators, hands, software and safety still being rebuilt, and that it hasn’t had a single-foundation‑model "ChatGPT moment." Her view contrasts with proponents who expect general foundation models to rapidly consolidate specialized robotics work.

5.0

STARTUPS · 1 SOURCE · TechCrunch AI

NVIDIA veteran Amulya Vishwanath backs 'physical AI' beyond robots through Techable VC

Amulya Vishwanath, who led product marketing for NVIDIA’s robotics platforms for seven years, has founded Techable VC and is writing early checks into what she calls 'physical AI' — covering robotics, materials science and life sciences. She argues physical AI includes molecules, materials and medicines as well as machines, and rejects the idea that a single general foundation model will instantly produce a ChatGPT-style breakthrough for robotics.

4.0

RESEARCH · 1 SOURCE · Hugging Face

NVIDIA and AI4Bharat release Nemotron-pretraining-Indic-v1: 2.8T-token Indic corpus and open GPU data pipeline

NVIDIA, IIT Madras (AI4Bharat) and collaborators are releasing Nemotron-pretraining-Indic-v1, a synthetic pretraining corpus of about 2.8 trillion tokens spanning 22 Indian languages, together with an open, GPU-accelerated data-generation pipeline (including prompts, LLM workflows, and quality checks). The corpus includes native-script and romanized variants, preserves technical content (math, code, tables), traces outputs back to curated source documents, and was partially used to add 10 billion Indic tokens to continued pretraining of Nemotron 3.5 Super VL; the data and pipeline are being shared so others can reproduce and extend the workflow.

8.0

MODELS · 2 SOURCES · NVIDIA Developer · The Decoder

NVIDIA releases Nemotron 3.5 Super VL, a 120B-parameter open multimodal model for agents

NVIDIA announced Nemotron 3.5 Super VL, an open 120B-parameter mixture-of-experts multimodal model that activates 12B parameters per token to support agentic workflows combining reasoning, instruction following, tool use, and text, image, document, and video understanding. NVIDIA says it achieves up to 5× higher single‑GPU throughput on agentic tasks versus comparable open models, supports post‑training via NeMo (SFT, LoRA, RL) with resources on Hugging Face and a GitHub usage cookbook, and employs inference optimizations such as LiLiCorr speculative decoding and NVFP4 quantization; partners including Scale AI, Ramp, and AI21 report substantial cost, latency, or accuracy improvements in production workloads.

8.0

STARTUPS · 1 SOURCE · TechCrunch AI

TechCrunch Disrupt 2026 set for Oct 13–15 in San Francisco; tickets and program details

TechCrunch Disrupt 2026 will take place Oct 13–15 at Moscone West in San Francisco, bringing more than 10,000 founders, investors and tech leaders, 250+ speakers across 200+ sessions, and 300+ startups in the Expo Hall. Ticket promotions include up to $100 off, a 50% off second-pass offer, and a Layoff to Liftoff Expo+ pass for $75; door rates rise on Oct 13 at 8 a.m. PT.

4.0

STARTUPS · 1 SOURCE · TechCrunch AI

TechCrunch reveals Disrupt Stage lineup for Disrupt 2026 with AI leaders from Cerebras, Ricursive, Replit and more

TechCrunch published the final Disrupt Stage lineup for TechCrunch Disrupt 2026, taking place Oct. 13–15 at Moscone West in San Francisco, featuring sessions from leaders including Cerebras CEO Andrew Feldman, Ricursive Intelligence co-founders Anna Goldie and Azalia Mirhoseini, Replit CEO Amjad Masad, and many founders, investors, and Startup Battlefield finalists.

6.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA adds 'cuDF skills' to help AI agents rewrite Spark UDFs for GPU acceleration

NVIDIA describes a workflow and a new 'cuDF skills for Apache Spark' capability that uses agent-driven tooling to rewrite Spark UDFs into GPU-accelerable forms (via Spark SQL expressions, the RapidsUDF Java interface, or custom CUDA kernels), eliminating GPU fallback and achieving 22x+ speedups in tests. NVIDIA cites a case where a large consumer tech company converted 30+ production UDFs and reduced an operator's runtime from 274.55 to 12.78 hours on a 600‑billion‑row workload.

6.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA open-sources Run:ai platform as part of DSX AI Factory

NVIDIA has open-sourced the full Run:ai platform as part of its DSX AI Factory Platform, publishing the code on GitHub and offering it as a Community Product on NGC. The release includes four modular components (KAI Scheduler, GPU Fractioning Operator, KAI Resource Management, Workload Map), complements projects like Model Streamer and Grove, and is being integrated by multiple DSX ecosystem partners such as Crusoe, Mirantis and Nebius.

8.0

RESEARCH · 1 SOURCE · NVIDIA Developer

NVIDIA and Dao AI Lab publish ReplaySSM to speed SSM-based hybrid-model decoding

NVIDIA and the Dao AI Lab (Princeton) released ReplaySSM (paper on arXiv and code on GitHub), a technique that caches recent SSM inputs instead of full state to halve state memory traffic and speed SSM decoding kernels by ~1.46–1.80× (batch 256 on Blackwell Ultra B300). ReplaySSM also simplifies speculative-decoding rollback (1.85–2.10× higher speculative throughput on 120B-class models) and enables mixed-precision state (three of four tested models stay within ~2 percentage points of FP32 on GPQA‑Diamond); it's integrated into TensorRT‑LLM, vLLM, and FlashInfer and runs in production in GLM‑5.3‑Flash and Kimi‑K3.

8.0

COMPANIES · 3 SOURCES · Google · TechCrunch AI · The Decoder

Google makes SynthID Detector available globally in English

Google (Google DeepMind) has expanded access to its SynthID Detector, making the tool available globally in English to let anyone check whether images, video, or audio were generated with AI by Google or partner providers (OpenAI, NVIDIA, Kakao, and soon Apple). The company says it has watermarked over 180 billion images and videos and 240,000 years of audio, and that SynthID is integrated into Search, the Gemini app, and Chrome, which together now handle over 1 million verification requests daily.

7.0

MODELS · 1 SOURCE · Hugging Face

Open multimodal decision models d1-3B and d1-omni-600M released for edge inference

The team released two open-weight decision models, d1-3B and experimental d1-omni-600M, built on their Liquid Foundation Models. d1-3B (decoder-only) supports text+images, scores 48.57 on Decision Index v0.2.1 (best under 10B) and runs in 16–50 ms on NVIDIA Jetson devices; d1-omni-600M (encoder-based) supports text+image or text+audio, is an early research release, and both models (and demos) are available on Hugging Face.

8.0

COMPANIES · 1 SOURCE · NVIDIA Developer

NVIDIA cuOpt adds mPDLP multi‑GPU LP solver that scales to 100M+ variables

NVIDIA released mPDLP, a Multi‑GPU Primal‑Dual hybrid gradient solver in cuOpt that shards large linear programs across NVLink‑connected GPUs. mPDLP cuts per‑GPU peak memory (up to 6× vs single‑GPU PDLP), supports problems up to 2.1B nonzeros, and in DGX B200 benchmarks shows PDLP‑step speedups up to 11.4× and typical overall gains of 1.2×–2.5× vs prior D‑PDLP; partner cases reported ~3.3× (Kinaxis, 135M variables) and >5× (PSR, 185M variables). A tutorial and source code are available on GitHub.

7.0

CODING · 1 SOURCE · NVIDIA Developer

Megatron Core PR #7262 adds workflow for bitwise-deterministic pretraining

Megatron-LM PR #7262 introduces a workflow and tooling in NVIDIA Megatron Core to achieve and validate bitwise-deterministic large-scale pretraining, including ordered per-rank tensor fingerprinting for offline divergence localization. Kernel-level fixes (e.g., private output slots for grouped-GEMM writers and a fixed reduction order), recipe/kernel/module validation across parallelism settings (including FP8/FP4), and optimizations on Nemotron workloads reduced the determinism overhead to about 2% at 2,432 GPUs while preserving bitwise identity over 800 steps.

7.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA's DOCA GPUNetIO unifies GPU-initiated networking across DOCA and open-source stack

DOCA GPUNetIO provides a common GDA‑KI foundation that lets CUDA kernels directly drive Ethernet, RDMA/Verbs and DMA operations to keep CPUs out of the application critical path. NVIDIA ships GPUNetIO as a full DOCA SDK implementation and a lighter open‑source Verbs‑focused library; major libraries (NCCL GIN since 2.27, NVSHMEM 3.7, UCX/NIXL, NVQLink and Holoscan components) are adopting the shared path, with reported benefits such as improved small‑message scaling and sub‑microsecond-to-microsecond latencies in specialized workflows.

7.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA releases AICR v1.0 to standardize GPU Kubernetes cluster configs

NVIDIA published AI Cluster Runtime (AICR) v1.0, a version-locked system that pins validated component combinations and establishes a stable compatibility contract across the CLI, REST API, Go SDK, bundle layout, and artifact schemas for GPU-accelerated Kubernetes clusters. The release includes four capabilities (Snapshot, Recipe, Bundle, Validation), a live validation dashboard with signed evidence, and ecosystem integrations from Pulumi Labs and Mirantis’s k0rdent to consume recipes via different IaC and multi-cluster tools.

7.0

MODELS · 3 SOURCES · Mistral AI · The Decoder · TechCrunch AI

Mistral launches public preview of Mistral Large 4 (ML4), a 1T-parameter open-weight multimodal model

Mistral has opened a public preview of Mistral Large 4 (ML4), a natively multimodal model with 1 trillion parameters and 49 billion active parameters; the preview API is available now and Mistral plans to publish the model weights by the end of the month. ML4 was trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral’s European datacenters, targets enterprise and cybersecurity workflows, and Mistral says it matches or exceeds other open-weight models on several benchmarks while enabling self-deployment and an EU-operated region.

9.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA publishes DOCA AI agent skills on GitHub to speed BlueField development

NVIDIA published DOCA AI agent skills on GitHub, a lightweight SKILL.md-based format that supplies agents with verified DOCA API signatures, hardware capability requirements, and build constraints for components like Flow, GPUNetIO, PCC, and RDMA. NVIDIA reports a 65-prompt evaluation where agents with the skills satisfied 100% of checklist items versus 19% without skills, and a demo where the with-skills agent used 73% less handwritten code and 46% fewer hardware commands to build a Go RDMA app on BlueField-3.

6.0

CODING · 1 SOURCE · NVIDIA Developer

NVIDIA publishes DIN Deploy C++ samples using ONNX Runtime + TensorRT RTX

Do Inference Now (DIN) Deploy is an open-source repository of C++ samples that pair ONNX Runtime with the NVIDIA TensorRT RTX execution provider to accelerate local AI inference on Windows and Linux. Samples demonstrate ASR (OpenAI Whisper, Parakeet TDT, Nemotron), Meta SAM 2.1 segmentation, and FLUX.2-klein-4B image generation, include CMake presets (x86-64, Arm64), show graphics interop using ONNX Runtime 1.25 with Vulkan/DirectX, and report GPU speedups (e.g., 206x for Parakeet TDT, 39x for Nemotron streaming, SAM 2.1 at 38.3 FPS vs 0.5 FPS on CPU).

6.0