
NVIDIA has released the Nemotron 3 Embed, a collection of embedding models that have achieved top rankings on the RTEB leaderboard. The 8B model leads in retrieval quality, while smaller variants offer efficient deployment options. These models are designed for production-scale retrieval, supporting multilingual and code retrieval tasks. With open weights and datasets, developers can customize and deploy these models on their infrastructure. The release marks a significant advancement in retrieval technology, providing enhanced tools for developers to improve retrieval efficiency and accuracy.
Read originalEarlier coverage that leads up to this article, and what followed. Lines connect each piece to the closest one after it, converging here.
© Hugging Face BlogLiquid AI is tackling the latency bottleneck in vision-language models with a specialized speculative decoding drafter. The LFM2.5-VL-DSpark model adds only 280M parameters to the base 3B architecture but delivers up to 3.13x faster on-device decoding and 2.66x speedups on H100 GPUs. By integrating directly with llama.cpp, MLX-VLM, and SGLang from day one, they are making high-performance VLM inference accessible without complex custom infrastructure. This shifts the focus from raw model size to efficient token drafting for edge deployment.
© Hugging Face BlogThis release quietly expands llama.cpp's hardware support to include Qualcomm's Hexagon NPU on Linux arm64, a significant step for local inference on Snapdragon devices. It also updates CUDA builds to version 13.4 and introduces ROCm 10.0 binaries, keeping the project aligned with the latest NVIDIA and AMD driver ecosystems. KleidiAI on Apple Silicon is temporarily disabled in this build, likely due to stability checks rather than a feature rollback. For developers targeting edge AI or diverse GPU stacks, this update ensures broader compatibility without requiring custom compilation.
© Lev SelectorNVIDIA introduced the NVFP4 4-bit format and SoL-Pi technology, which uses 2x fewer tokens for improved efficiency.
Together AI Blog · April 28, 2026 · Background
NVIDIA Blog · April 28, 2026 · Related
Ollama Blog · June 4, 2026 · Related
Sam Witteveen · June 4, 2026 · Related
Matt Wolfe · June 5, 2026 · Background
Sam Witteveen · June 7, 2026 · Related
NVIDIA Blog · July 8, 2026 · Related
Hugging Face Blog · July 8, 2026 · Related
Hugging Face Blog · July 17, 2026 · Background
Ollama Blog · August 11, 2026 · Background
Matt Wolfe · August 14, 2026 · Background
Lev Selector · August 14, 2026 · Background
TechCrunch AI · August 21, 2026 · Background
NVIDIA is pushing hard to make GPU-accelerated physics the standard for robot learning. This deep dive into MuJoCo Warp (MJWarp) shows how to scale a single SO-101 arm simulation to 2,048 parallel environments on CUDA hardware. The real value isn't faster single-step latency, but massive aggregate throughput for reinforcement learning data collection. By leveraging Warp's kernel compilation and CUDA graph capture, developers can batch thousands of physics steps simultaneously, turning the GPU into a high-throughput experience generator rather than just a fast simulator.