NVIDIA Nemotron 3 Ultra
The first open frontier model built for agents
What it does
NVIDIA's 550B Mixture-of-Experts model with hybrid Mamba-Attention architecture, delivering 300+ tokens/sec with a 1M-token context window. Top-ranked US open-weights model on the Artificial Analysis Intelligence Index. Built specifically for multi-step agent loops where frontier reasoning at open-source economics actually matters. Available now on Hugging Face, OpenRouter, ModelScope, and build.nvidia.com as a NIM microservice.
Does the same job
all alternatives →- Nemotron 3 Ultra by NVIDIAJun 2026 · developer.nvidia.com · ▲179
Powers faster, efficient reasoning for long-running agents
- MOMoonshine Open-Weights STT models – higher accuracy than WhisperLargev3Feb 2026 · github.com · ▲316
I wanted to share our new speech to text model, and the library to use them effectively. We're a small startup (six people, sub-$100k monthly GPU budget) so I'm proud of the work the team has done to create streaming STT models with lower word-error rates than OpenAI's largest Whisper model. Admittedly Large v3 is a couple of years old, but we're near the top the HF OpenASR leaderboard, even up against Nvidia's Parakeet family. Anyway, I'd love to get feedback on the models and software, and hear about what people might build with it.
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I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
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Today, I’m proud to announce Homebrew 6.0.0. The most significant changes since 5.1.0 are a new tap trust security mechanism, the new faster, smaller, default internal Homebrew JSON API, sandboxing on Linux, better defaults informed by our user survey, many brew bundle improvements, improved performance and initial support for macOS 27 (Golden Gate). Happy to discuss any questions here!
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