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

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

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

We report operational experience full-fine-tuning a 32. 76B-parameter dense model (Qwen3-32B) on 16 x NVIDIA B300 (two nodes, FSDP / ZeRO-3) -- among the first published field accounts on this accelerator.

Benchmarking Confidential GPU Inference on NVIDIA H100 under Intel TDX

arXiv:2607. 19353v1 Announce Type: new Abstract: Confidential computing is becoming a practical deployment requirement for AI inference workloads that process sensitive inputs or protect proprietary model assets.