Coding agents work in tasks, and the number that matters is cost per solved task, not price per token. In this post, you use the AWS CDK to deploy LiteLLM with the NVIDIA NeMo Switchyard routing plugin on Amazon Bedrock, then measure cost per solved task for a routed model group against single-model baselines.
Deploy OpenFold3-preview from Amazon SageMaker JumpStart to predict 3D structures of proteins, DNA, RNA, and ligand complexes in your own AWS account. The post covers asynchronous inference, multi-GPU parallelism, bringing your own MSAs for accuracy and privacy, and scaling to zero to reduce cost.
This walkthrough showcases a beverage can pick-and-place task that uses the UR17G arm , Robotiq 2F-140 gripper , and beverage can to compose a scene in Isaac Sim, check their behavior, and run a pick-and-place example with agent-generated controls.
Amazon SageMaker HyperPod Data Capture records live inference inputs and outputs to Amazon S3. This post walks through one end-to-end example: capturing a support router's production traffic, surfacing a new-product blind spot with Amazon Athena, fine-tuning on the captured data with the HyperPod training operator, and measuring the before-and-after lift.
llama.cpp makes it possible to run multi-modal LLMs across a wide range of hardware - from consumer-grade local machines to high-end enterprise servers. Today we release libraries v1.0.0, highlighting our responsibility to those developers.
SGLang-Neuron is now publicly available through an AWS Deep Learning Container. With SGLang-Neuron, you can serve models and generate reinforcement learning rollouts with SGLang on AWS Trainium using the same SGLang interface you already know, all running on native PyTorch.
LLMs rely on their context to generate the next set of tokens. To address this, we developed LLMON (LLM Object Notation, pronounced “lemon”), an LLM-native markup language for explicitly communicating structure and semantics to the models.
AI agents now run multi-step workloads for hours and act on behalf of users, raising the bar for secure, scalable agent compute. Learn how the next generation of Amazon Bedrock AgentCore runtime delivers hardware-isolated sessions with faster starts, more compute, usage-based billing, and isolation that now extends to your data.
TLDR; In this blog, we provide fine-tuning scripts and containers to fine-tune TRELLIS.2, a popular 3D model on your 3D assets, that can run both locally and on Hugging Face Jobs.
Every generative AI team keeps rebuilding the same components: RAG pipelines, document processors, and agent scaffolding. This post shows how to access a curated catalog of 23 production-ready, open source reusable assets from the AWS Generative AI Innovation Center, with real delivery results from DoorDash, Lyft, and Ricoh.
Tether AI Research introduces BotPsy, a 0.8B-parameter model designed for function calling and agentic workflows, built on top of Qwen3.5-0.8B and suitable for on-device deployment.
Kubernetes, the open source system that manages how applications run across thousands of AI factories, has become the foundation for AI, and the demands on it are only growing. To help meet them, NVIDIA is deepening its commitment to open source at KubeCon + CloudNativeCon, the flagship conference for the Kubernetes and cloud-native communities.
The Conference on Robot Learning (CoRL) takes place November 9–12, 2026, in Austin, Texas. BAIR faculty, postdocs and students will be presenting research at various sessions, including an Early Career Keynote from Professor Negar Mehr. See below for the full schedule of sessions with BAIR members, including papers co-authored with collaborators at other universities and companies.
To address these challenges, Elith and NVIDIA have released Nemotron-Personas-Finance-Japan , a financial domain extension of the open-source persona dataset " Nemotron-Personas-Japan ." To our knowledge, it is the first open persona dataset at million-record scale that pairs Japan’s official statistics with household finance and financial literacy data.
Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components.
NVIDIA is releasing Dynamo-optimized deployment recipes for Korea’s leading open models: A.X K2 from SK Telecom, K-EXAONE 2.0 from LG AI Research, Solar Open 2 from Upstage and Motif-3 from Motif Technologies.
As AI agents take on more work, they need capable open models and security that advances alongside them. Hyundai Motor Group is exploring NVIDIA NemoClaw blueprints and the NVIDIA OpenShell runtime, while AIM Intelligence is integrating its Starfort platform with NVIDIA OpenShell to support secure AI agents for employee productivity and enterprise workflows.
Korea’s Ministry of Science and ICT and NVIDIA hosted the MSIT-NVIDIA AI Startup Partnership Forum at COEX. Representatives from the Ministry of Science and ICT and NVIDIA met with over 20 Korean startups across foundation models, physical AI, AI infrastructure and AI semiconductors.
A year ago, NVIDIA open sourced its Aerial wireless software, joining Sionna and a broader suite of open source tools for accelerating next-generation 6G network development.
Today we're introducing a framework for creating these environments; we call them FoundationWorlds. Our first foundation world is ToyBlocks : an open simulation framework built around a robot arm and blocks on a tabletop.
Back to Articles A few months ago, we added private data to the Open ASR Leaderboard : 11 splits from Appen and DataOceanAI covering scripted and conversational speech from five accents ( blog ). The community response was great, with many highlighting the importance of held-out sets for trustworthy evaluations .
Chris Alexiuk has a simple rule about open source AI: If you believe in it, be loud about it. “We should be shouting about this technology from the rooftops,” said Alexiuk, a Toronto-based senior product research engineer in deep learning at NVIDIA who specializes in making open large language models (LLMs) practical for working developers.
Two weeks ago, we opened up Phonon, our small text-to-speech model that runs on your own device. It needs no server and no network, and what you type stays with you.
Machine learning models learn to recognize the world around us from examples labeled by humans. Underlying this process is an assumption that the person doing the labeling has a clear and stable definition of the category in mind before they begin.
A 2-billion-parameter model that extracts exactly the fields you ask for, from any document type, outperforming every frontier LLM we tested, at a fraction of the cost.
In production AI agents, context management is the real bottleneck. This post presents a four-layer context engineering framework (architecture, prompts, file-based tools, and verification agents), implemented in working code from Deep Insight, an open source multi-agent data analysis solution on the Strands Agents SDK and Amazon Bedrock AgentCore.
We fine-tuned a 20B model to build decks the way people do: a designer plans the deck and a coder draws each slide. Its training data follows the IBM Design Language, and on PresentBench it ranks first on two of the three domains we measured.
An IBM team compared CUGA, Hermes and Deep Agents on 90 AppWorld tasks using the same open-weight model, GLM-5.3-flash. Task pass rates were 81.1%, 46.7% and 30.0%, and full-scenario pass rates were 66.7%, 26.7% and 6.7%.
This post shows how to use Crawl4AI, an open source web crawler, as a content preparation layer for Amazon Bedrock Knowledge Bases, handling JavaScript rendering, selective extraction, and custom metadata enrichment before web content reaches an Amazon S3 data source.
NVIDIA Nemotron 3.5 Super VL is a 120B-parameter mixture-of-experts model that pairs reasoning and tool use with multimodal understanding. This post explores its architecture and use cases, then shows how to deploy it on Amazon EKS, EKS Auto Mode, and Amazon SageMaker HyperPod EKS with NVIDIA Dynamo and vLLM.
Mistral Large 4 (aka Le Chonk) is a multimodal, multilingual, Mixture-of-Experts (MoE) model from Mistral. It takes text and images as input and generates text. It has about 1T total, 49B active parameter, and a context window of 1M tokens.
Today, we're announcing Project ProtoStar, an initiative to unlock the potential of publicly funded scientific research by making datasets, models, and code more accessible and reusable, particularly for AI-driven research.
The weights of Mistral Large 4 (Le Chonk) are now open. A 1T parameter multimodal model, and best open-weight model for coding, trained in Europe.
Today we're announcing two packages: AnySpeechRecognizer implements Apple's new speech-to-text API with a choice of recognition engine, including NVIDIA's Parakeet and Alibaba's Qwen3-ASR running locally on Core ML, and adds speaker diarization to track who speaks when.
Just over a week ago, we released Granite Switch 4.1 , showing that our modular Switch architecture lets you configure a language model with specialized, cache-efficient adapters — adding new expertise on a single endpoint. Granite Switch 4.1.1 is a patch release.
The Ray Train Deep Learning Container ships the full CUDA-through-EFA stack, so you build no image. This post walks through a reproducible sample that fine-tunes Qwen2.5-1.5B with PyTorch FSDP across eight GPUs on two EFA-enabled g6.12xlarge instances on Amazon EKS, using the container exactly as published.
One open model for multilingual transcription, speaker attribution, translation, and audio Q&A, with top leaderboard results, plus the open Granary v2 data pipeline and fine-tuning recipes to adapt it.
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI.
LFM3 is our new generation of Liquid Foundation Models (LFMs), built to be the starting point for specialized intelligence. It brings text, vision, audio, omni, and decision models onto one shared backbone, all in the 2B parameter range.
There are 57 million people worldwide living with dementia today. For the researchers working to help them, the obstacle isn’t a lack of effort — it’s too much data, too few experts to synthesize it and a pace of discovery that can’t keep up with the disease.