The auto-updated technology brief
NVIDIA introduces the MCG Toolkit to help software teams automate comprehensive and auditable AI model documentation amid increasing regulatory requirements such as California’s AB-2013 and the EU AI Act.
This article is the third in a series on model quantization, focusing on converting FP8 quantized checkpoints into high-performance inference engines using NVIDIA TensorRT, enabling faster inference and higher throughput.
NVIDIA introduces NVFP4 precision support in JAX and MaxText to accelerate large language model pretraining on Blackwell GPUs, improving throughput and reducing training time and costs.
NVIDIA explores building foundation models for financial intelligence using transaction data, highlighting the value of sequential patterns in customer histories over traditional hand-engineered features.
NVIDIA achieved a clean sweep in MLPerf Training v6.0, delivering the fastest training times at scale and highest per-accelerator performance across all benchmarks, being the only platform to submit on every test.
AWS DevOps Agent now offers release management capability in preview, reviewing code changes for release readiness and running autonomous release testing to help you ship code to production safely and with confidence.
NVIDIA introduces the Vera CPU, designed to advance agentic AI workloads in AI factories by building on new scaling laws beyond traditional GPU-based AI training and inference.
AWS introduces Web Search on Amazon Bedrock AgentCore, enabling AI agents to ground responses in current, cited web knowledge securely within AWS environments, simplifying integration and infrastructure management.
AWS launches AWS Lambda MicroVMs, providing VM-level isolated sandboxes with rapid launch, state preservation, and lifecycle control without infrastructure management.
NVIDIA introduces DFlash speculative decoding to boost inference performance by up to 15x on Blackwell GPUs, improving low-latency AI workflows by optimizing autoregressive LLM token generation.
NVIDIA demonstrates an open source example combining Hermes Agent with NVIDIA NemoClaw to accelerate and secure product research across Outlook, Slack, and GitHub, leveraging AI agents for data synthesis and decision-making.
NVIDIA explores Vision-Language-Action (VLA) and World-Action Models (WAM) that leverage pretrained large-scale VLM backbones to enable robots to generate actions from visual and language inputs.