The Open Evaluation Standard: Benchmarking NVIDIA Nemotron 3 Nano with NeMo Evaluator

Accelerating Transformers Fine-Tuning with NVIDIA NeMo AutoModel

Welcome the NVIDIA Llama Nemotron Nano VLM to Hugging Face Hub

Measuring Open-Source Llama Nemotron Models on DeepResearch Bench

Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents

TokenPowerSandbox: Evidence-Gated CPU-First Screening for Energy-Aware LLM Serving

TokenPowerSandbox is an evidence‑gated workflow that uses a CPU‑resident projector, brief GPU probes, full‑workload verification, and tamper‑evident provenance to predict energy usage of large language model serving. In experiments on an NVIDIA H100 80GB running Qwen2.5‑7B‑Instruct with vLLM, the method achieved energy MAPE of 6.23% and 7.35% on blind holdout and no‑refit confirmations, with high Spearman rank correlations. A predeclared TTFT gate demonstrated that energy accuracy alone cannot guarantee latency, as it passed at concurrency four but abstained below that level.

Mistral AI partners with NVIDIA to accelerate open frontier models

What Input Resolution Is Required for Bird Species Identification, and What Is Its Latency Cost on an Edge Device? A Study of 14 Input Resolutions and Six Architectures with On-Device Measurements

arXiv:2609.14247v1 Announce Type: cross Abstract: Bird-strike mitigation at wind farms requires identifying distant birds that span only tens of pixels, so the classifier's input resolution N is a de...

Operating Multi-Node Full Fine-Tuning on NVIDIA B300: A Field Report on Telemetry-Based Triage, Negative Results, and Operational Hardening

arXiv:2608. 05944v1 Announce Type: cross Abstract: We report operational experience full-fine-tuning a 32.

How Much Progress Has There Been in NVIDIA Datacenter GPUs?

arXiv:2601. 20115v3 Announce Type: replace-cross Abstract: As the role of modern Graphics Processing Units (GPUs) becomes increasingly essential for several computing tasks, analyzing their past and current progress is paramount for determining future constraints on scientific research.

NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets

Faster Training and Inference: Habana Gaudi®2 vs Nvidia A100 80GB

ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers

ENAS is a hardware‑aware neural architecture search framework tailored for TinyML on microcontrollers. It uses a static feasibility check, a cell‑based search space with various block types and skip connections, and a three‑stage hybrid search strategy (random → top‑K → mutation) with cross‑run caching. The framework runs efficiently without GPUs, achieving significant search‑time speedups and competitive accuracy on Visual Wake Words and Melanoma Cancer benchmarks across a range of microcontrollers.