AI engineering

NVIDIA NeMo AutoModel: 3.7x Faster MoE Fine-Tuning with One Import Change

NVIDIA NeMo AutoModel delivers 3.4-3.7x higher training throughput and 29-32% less GPU memory for MoE fine-tuning through a single import line change. By adding Expert Parallelism as a dedicated dimension, DeepEP fused dispatch, and TransformerEngine kernels on top of Transformers v5, it makes 550B-scale full fine-tuning feasible and produces standard HF checkpoints for downstream deployment.

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Cross-Origin Storage API: Ending duplicate downloads for browser AI

If you've ever watched your browser re-download 177 MB of Whisper model weights just because a second site lives on a different origin, this article explains exactly why that happens—and how the proposed Cross-Origin Storage API fixes it using cryptographic hashes instead of URLs. Transformers.js already supports it experimentally, and you can test the whole thing today with a Chrome extension.

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AT&T and GSMA build open telco AI on Gemma, hitting 91.7% accuracy

AT&T and GSMA built 30 open telco models on Gemma to solve the fundamental problem that general AI lacks telecom domain knowledge. Gemma-4-E4B-it hit 91.74% accuracy, and smaller fine-tuned models are outperforming frontier models several times larger. Over 18 million downloads later, this is a case study in why domain-specific fine-tuning beats general scale.

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Local Model Triage for OpenClaw: Real-Time, Cost-Free PR Classification

If you own a powerful local machine like the NVIDIA GB10, using models like gemma-4-26b-a4b or qwen3.6-35b-a3b in an agent harness can give you real-time, cost-free triage of open-source contributions. The article provides concrete numbers on precision, recall, and throughput tradeoffs, plus a practical hybrid architecture that uses a cheap cloud audit loop ($9/month) to catch misses.

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