hardware

arXiv:2605.18015v1 Announce Type: new Abstract: Production log analytics in self-hosted, resource-constrained environments requires natural-language access to massive log streams without the cost of r

arXiv:2605.18739v1 Announce Type: new Abstract: We present LongLive-2.0, an NVFP4-based parallel infrastructure throughout the full training and inference workflow of long video generation, addressing

arXiv:2602.06694v2 Announce Type: replace Abstract: Weight-only quantization has become a standard approach for efficiently serving large language models (LLMs). However, existing methods fail to effi

arXiv:2604.02184v2 Announce Type: replace Abstract: We address the inverse problem of designing two-dimensional reflectors that transform light from a finite, extended source into a prescribed far-fie

At this year’s Google I/O conference, NVIDIA and Google Cloud are accelerating the work of more than 100,000 developers in the companies’ joint developer community, which provides curated learning pat

NVIDIA agent skills enable specialized agents to deliver targeted capabilities across enterprise workflows , with governance provided through verified, scanned, and signed skills. JFrog AI Catalog aut

arXiv:2605.18654v1 Announce Type: cross Abstract: A fraud scorer needs to answer in under 2 ms. The best tabular foundation models (TFMs) take 151-1,275 ms on GPU. We close this gap by distilling the

arXiv:2605.17428v1 Announce Type: cross Abstract: Our preliminary experiments on gym-DSSAT maize irrigation tasks revealed that +/-2 degrees C temperature noise causes an 11.9% reduction in economic r

arXiv:2605.16311v1 Announce Type: new Abstract: Distributed training of large neural networks is bottlenecked by full-precision gradient communication and by coordinatewise optimizers that ignore the

Anna Tong / Forbes: Sources: Zyphra, which trains and runs inference for its open-weight models on AMD hardware, is raising a 500M Series B at a valuation of at least 5B — The Series B round, which ch

arXiv:2605.17156v1 Announce Type: cross Abstract: Quantum error correction (QEC) is essential for building fault-tolerant quantum computers, requiring decoders that are simultaneously accurate, fast,

arXiv:2605.18389v1 Announce Type: new Abstract: Optimal transport provides a powerful framework for comparing measures while respecting the geometry of their support, but comes with an expensive compu

arXiv:2605.17991v1 Announce Type: cross Abstract: Stable Audio 3 is a family of fast latent diffusion models (small, medium, large) for variable-length audio generation and editing. Since our models c

arXiv:2605.17019v1 Announce Type: new Abstract: Streaming video effect generation is highly desirable for live human-centric applications such as e-commerce streaming, entertainment, and vlogging, yet

arXiv:2605.16259v1 Announce Type: cross Abstract: While real-time image generation using diffusion models has advanced rapidly on NVIDIA GPUs, systematic optimization research on non-CUDA platforms su

arXiv:2502.20969v4 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain

arXiv:2605.17821v1 Announce Type: cross Abstract: Large Language Model (LLM) training is frequently interrupted by a heterogeneous spectrum of failures, from common GPU crashes to catastrophic cluster

arXiv:2605.18177v1 Announce Type: new Abstract: Query-based Vision Transformer segmentation models typically reconstruct dense spatial feature maps to predict masks, inheriting design patterns from co

arXiv:2605.17170v1 Announce Type: new Abstract: Agentic workloads have emerged as a major workload for LLM inference. They differ significantly from chat-only workloads, requiring long-context process

arXiv:2510.06388v2 Announce Type: replace Abstract: Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used t

arXiv:2605.17613v1 Announce Type: cross Abstract: The large size of the KV cache has become a major bottleneck for serving LLMs with increasing context lengths. In response, many KV cache compression

Vultr expanded its global cloud infrastructure by opening a new data center region in Milan, Italy, marking the company's 33rd cloud data center location worldwide. This expansion provides European cu

We are releasing Carbon: a crazy fast DNA model Carbon is 275x faster than the next best model. So fast you can process the whole human genome on a single GPU in <2 days. Here are the tricks we used:

arXiv:2605.15617v1 Announce Type: cross Abstract: Large language model (LLM) training today runs on clusters spanning thousands of GPUs. While this scale enables rapid model advances, developing, debu

arXiv:2605.15475v1 Announce Type: new Abstract: We introduce an empowered transposed Fully Connected Weighted (t-FCW) graph representation to embed point clouds into a metric space. While original t-F

arXiv:2605.16076v1 Announce Type: cross Abstract: Plant disease detection is still largely manual in Bangladesh, where extension workers eyeball leaf samples across millions of smallholdings. We built

arXiv:2605.15652v1 Announce Type: cross Abstract: Vector-HaSH and the Tolman-Eichenbaum Machine (TEM) propose that the hippocampal-entorhinal circuit factorizes content from a prestructured grid-cell

Robbie Whelan / Wall Street Journal: Decart, which offers real-time generative video and GPU optimization tech, raised 300M at a ~4B valuation, up from 3.1B after raising 153M in August 2025 — Decart'

arXiv:2605.15824v1 Announce Type: new Abstract: Human-centric video customization, particularly at the garment level, has shown significant commercial value. However, existing approaches cannot suppor

This guide demonstrates how to fine-tune NVIDIA's Cosmos Predict 2.5 video generation model using parameter-efficient techniques like LoRA (Low-Rank Adaptation) and DoRA (Mixture of Experts-based adap

arXiv:2604.04310v2 Announce Type: replace Abstract: In robot control, planning, and learning, there is a need for rigid-body dynamics libraries that are highly performant, easy to use, and compatible

Alessandra Ram / Wired: How the AI industry's intense pressure to keep up and its financial rewards are creating “walls of resentment” between Silicon Valley workers and their spouses — Are you marrie

Agentic AI inference at one-tenth the cost per token with NVIDIA Vera Rubin NVL72. Agent sandboxes run 50% faster on NVIDIA Vera than traditional CPUs — while enterprise data queries are up to 3x fast

Eduard Kovacs / SecurityWeek: Pwn2Own Berlin 2026: participants earned a total of $1,298,250 for 47 vulnerabilities, with successful exploits of AI products like Codex, Cursor, and LM Studio — Partici

Together with SpaceXAI, we’re training a significantly larger model from scratch, using 10x more total compute. With Colossus 2’s million H100-equivalents and our combined data and training techniques

arXiv:2412.12636v3 Announce Type: replace-cross Abstract: Large-scale ML training jobs are frequently interrupted by hardware and software anomalies, failures, and management events. Existing solution

The first NVIDIA Vera CPUs arrived at three of the world's leading AI labs on Friday — Anthropic in San Francisco, OpenAI in Mission Bay, SpaceXAI in Palo Alto — followed by a delivery to Oracle Cloud

Setting a GPU power limit using nvidia-smi reduces heat output by approximately 20% with only a 5-8% inference speed loss. Undervolting the GPU can reduce power consumption by 5-15% with zero performa

Deedy / @deedydas: SF vibes are frenetic over the huge divide in outcomes and career uncertainty for software engineers; over 5 years ~10K people in AI attained retirement wealth — The vibes in SF fee

Zinnia Lee / Forbes: Shenzhen-listed RoboTechnik, which claims to be the largest silicon photonics tool maker and whose stock is up 340% over the past year, files for a HK listing — RoboTechnik Intell

Rebecca Bellan / TechCrunch: A profile of AI video generation startup Runway, which is training models directly on observational data, is now valued at 5.3B, and added 40M in ARR in Q2 — Every major A

arXiv:2605.14624v1 Announce Type: cross Abstract: A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational e

arXiv:2605.14438v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standa

arXiv:2605.14526v1 Announce Type: cross Abstract: Differentiable simulation of soft bodies is a foundation for system identification, trajectory optimization, and Real2Sim transfer. Yet, existing meth

arXiv:2605.13942v1 Announce Type: new Abstract: Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditi

arXiv:2605.14249v1 Announce Type: new Abstract: We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducin