# AI Foundation Models

Published articles for AI Foundation Models.

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## ModelExpress: Distributing Model Artifacts at the Speed of Light

DevFeed: [ModelExpress: Distributing Model Artifacts at the Speed of Light](<https://devfeed.tech/articles/modelexpress-distributing-model-artifacts-at-the-speed-of-light-6885.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/modelexpress-distributing-model-artifacts-at-the-speed-of-light/>)

Author: Elizabeth Goodman

Published: 2026-07-24T16:45:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [model-deployment](<https://devfeed.tech/topics/model-deployment.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [P2P](<https://devfeed.tech/topics/p2p.md>), [Amazon S3](<https://devfeed.tech/topics/amazon-s3.md>), [autoscaling](<https://devfeed.tech/topics/autoscaling.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>)

Tags: [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai-foundation-models](<https://devfeed.tech/tags/ai-foundation-models.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [autoscaling](<https://devfeed.tech/tags/autoscaling.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [dynamo-triton](<https://devfeed.tech/tags/dynamo-triton.md>), [featured](<https://devfeed.tech/tags/featured.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [inference](<https://devfeed.tech/tags/inference.md>), [inference-performance](<https://devfeed.tech/tags/inference-performance.md>), [jit](<https://devfeed.tech/tags/jit.md>), [llms](<https://devfeed.tech/tags/llms.md>), [local](<https://devfeed.tech/tags/local.md>), [model](<https://devfeed.tech/tags/model.md>), [s3](<https://devfeed.tech/tags/s3.md>), [scale](<https://devfeed.tech/tags/scale.md>), [speed](<https://devfeed.tech/tags/speed.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

### AI overview

NVIDIA ModelExpress (MX) reduces model-serving startup time by locating compatible model weights at the fastest available source. It prioritizes direct GPU-to-GPU transfer over P2P RDMA from an existing serving replica, while using object or local storage for initial bootstrapping and fallback. The article also describes reusing JIT kernel caches and distributing reinforcement-learning weight updates.

### Source excerpt

Every byte moved has a cost. As model checkpoints grow to hundreds of gigabytes or even a terabyte, that cost adds up quickly. To make things even worse, moving...

## NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x

DevFeed: [NVIDIA Ising Decoding Cuts Color Code Logical Error Rates by Over 300x](<https://devfeed.tech/articles/nvidia-ising-decoding-cuts-color-code-logical-error-rates-by-over-300x-6893.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/nvidia-ising-decoding-cuts-color-code-logical-error-rates-by-over-300x/>)

Author: Elizabeth Goodman

Published: 2026-07-13T19:00:00Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Ising](<https://devfeed.tech/topics/ising.md>), [Nvidia](<https://devfeed.tech/topics/nvidia.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Math and Logic](<https://devfeed.tech/topics/math-and-logic.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [agentic-ai-generative-ai](<https://devfeed.tech/tags/agentic-ai-generative-ai.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-foundation-models](<https://devfeed.tech/tags/ai-foundation-models.md>), [data-center-cloud](<https://devfeed.tech/tags/data-center-cloud.md>), [developer-tools-techniques](<https://devfeed.tech/tags/developer-tools-techniques.md>), [featured](<https://devfeed.tech/tags/featured.md>), [hpc-scientific-computing](<https://devfeed.tech/tags/hpc-scientific-computing.md>), [ising](<https://devfeed.tech/tags/ising.md>), [latency](<https://devfeed.tech/tags/latency.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [quantum](<https://devfeed.tech/tags/quantum.md>), [quantum-computing](<https://devfeed.tech/tags/quantum-computing.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [scale](<https://devfeed.tech/tags/scale.md>), [train](<https://devfeed.tech/tags/train.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

NVIDIA's Ising Decoder ColorCode 1 Fast is presented as a decoder for triangular color codes that improves logical error rates by more than 347.7x and runtime by 7.3x compared with Chromobius at d=31 and a 0.3% physical error rate. The article also describes an Ising decoding training pipeline for small 3D CNN-based pre-decoders supporting real-time quantum error correction operations.

### Source excerpt

Useful quantum computers will require fault tolerant logical operations. Researchers are actively exploring many different quantum error correction (QEC) codes...

## Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T

DevFeed: [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T](<https://devfeed.tech/articles/develop-humanoid-robot-policies-end-to-end-with-nvidia-isaac-gr00t-6803.md>)

Original publisher: [Read original article](<https://developer.nvidia.com/blog/develop-humanoid-robot-policies-end-to-end-with-nvidia-isaac-gr00t/>)

Author: Elizabeth Goodman

Published: 2026-07-07T17:05:42Z

Content type: article

Language: en

Sources: [NVIDIA Developer](<https://devfeed.tech/sources/nvidia-developer.md>), [NVIDIA Technical Blog](<https://devfeed.tech/sources/nvidia-technical-blog.md>)

Topics: [Robotics](<https://devfeed.tech/topics/robotics.md>), [Model Development](<https://devfeed.tech/topics/model-development.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [multimodal](<https://devfeed.tech/topics/multimodal.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Simulation and Design](<https://devfeed.tech/topics/simulation-and-design.md>), [Tooling](<https://devfeed.tech/topics/tooling.md>)

Tags: [ai-foundation-models](<https://devfeed.tech/tags/ai-foundation-models.md>), [apache](<https://devfeed.tech/tags/apache.md>), [building](<https://devfeed.tech/tags/building.md>), [data](<https://devfeed.tech/tags/data.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [featured](<https://devfeed.tech/tags/featured.md>), [humanoid-robots](<https://devfeed.tech/tags/humanoid-robots.md>), [images](<https://devfeed.tech/tags/images.md>), [integrations](<https://devfeed.tech/tags/integrations.md>), [isaac](<https://devfeed.tech/tags/isaac.md>), [isaac-sim](<https://devfeed.tech/tags/isaac-sim.md>), [manufacturing](<https://devfeed.tech/tags/manufacturing.md>), [model](<https://devfeed.tech/tags/model.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [nvidia](<https://devfeed.tech/tags/nvidia.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [robotics](<https://devfeed.tech/tags/robotics.md>), [robotics-simulation](<https://devfeed.tech/tags/robotics-simulation.md>), [robots](<https://devfeed.tech/tags/robots.md>), [simulation-modeling-design](<https://devfeed.tech/tags/simulation-modeling-design.md>), [software](<https://devfeed.tech/tags/software.md>), [tooling](<https://devfeed.tech/tags/tooling.md>), [training](<https://devfeed.tech/tags/training.md>), [workflows](<https://devfeed.tech/tags/workflows.md>)

### AI overview

NVIDIA Isaac GR00T Development Platform unifies humanoid robot development workflows, combining data collection, simulation-based training, evaluation, and deployment. The article highlights the open Isaac GR00T 1.7 vision-language-action model, which accepts language and images and can be adapted to robots, tasks, and environments through post-training.

### Source excerpt

As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids...