NVIDIA AI

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NVIDIA AI

NVIDIA AI

@NVIDIAAI

Teaching your AI new tricks.

Santa Clara, CA Katılım Haziran 2016
878 Takip Edilen312.9K Takipçiler
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NVIDIA AI
NVIDIA AI@NVIDIAAI·
Today we're shipping Nemotron 3 Ultra. A 550B MoE frontier-intelligence open model built for long-running agents. It delivers 5x faster inference and lowers the cost of complex agentic tasks by up to 30% versus other open frontier models.
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Clipit
Clipit@clipitdev·
Hackathon entry: Clipit, an agent-first, video-forward social media tool. Clipit helps agents turn long-form content into short form clips and distribute them across social platforms. Clipit plugs into the agent environments people already use. The workflow is simple: Give the agent a video, stream, podcast, or content library Clipit digests the source and finds usable moments The agent generates clips, captions, thumbnails, reframes, and post copy Clipit queues or publishes the final assets The human stays in the loop for taste, approval, and brand judgment The bigger idea: agents need tools that let them operate, not just answer. Clipit is the creation, editing, and posting layer for short-form video. We are building the searchable memory layer behind it, helping agents find old material, resurface context, and keep a brand’s video history useful. Together, they give agents a real media workflow: find content, generate clips, edit them, and distribute them. Clipit is the video production layer for AI agents. @NousResearch @NVIDIAAI @stripe
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NVIDIA AI
NVIDIA AI@NVIDIAAI·
3D scene reconstruction works great until the camera never sees part of the scene. ArtiFixer from NVIDIA Research is an open autoregressive model that fills in the missing geometry that other methods leave blank. #SIGGRAPH2026 paper, code + demo: nvda.ws/4oILqNd
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Peter Holderrieth
Peter Holderrieth@peholderrieth·
Hi everyone! I’ve moved to the Bay Area for a summer research internship at @nvidia. Beyond exciting work, I'd love to meet new people doing exciting stuff (incl. stuff I don't work on myself rn!). If you’re around, I’d love to connect! Even if just for a jam session!
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Caleb Eom
Caleb Eom@calebfoundry·
my first ever DGX Spark.. i will do my best to use it to share deeper knowledge around local models, inference, agents, coding, etc. thank you @NVIDIAAI
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Physical Intelligence
Physical Intelligence@physical_int·
New work with @nvidia: evaluating robot policies entirely inside a world model. The policy acts, the model imagines the consequences, and the imagined evals predict real-world results. 🧵 real vs world-model rollout side by side📷
GIF
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Mike Gannotti
Mike Gannotti@MichaelGannotti·
And…. IT HAS ARRIVED!!!!! My first @NVIDIAAI DGX Spark with 128 gb Unified Memory and a 4 tb ssd. To say I’m excited would be … and understatement! Can’t wait to load up some @nvidia LLMs and start rocking!
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NVIDIA AI retweetledi
Onur Solmaz
Onur Solmaz@onusoz·
16x parallel Gemma-4-26B-A4B-NVFP4 runs 🤯🤯🤯 18 output tokens/s, aggregate 300 tok/s 🫪 1 DGX Spark with 128 GB unified memory Concurrency so high I had to demo it programmatically It can go up to 32 even! 🤯 But then my screen would not have been readable for you And this is not even using flashinfer yet! Please reply if you know whether support is on the way Note that this is not dumb e4b or e2b that you can run on the average laptop. This is the big Gemma MoE Model link: huggingface.co/nvidia/Gemma-4…
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leyten
leyten@leyten·
Wow, it has happened! 30.55 tok/s on GLM-5.2 4-bit (from @Zai_org) ran by six RTX Pro 6000's across the USA scattered over WAN! I can't believe this. It was an insane build, you can read more about it on github.com/leyten/shard
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(株)松尾研究所
(株)松尾研究所@MatsuoInstitute·
松尾研究所でDGX Sparkを2台導入しました。 こちらが、DGX SparkとLLM社員の2ショット写真です
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Richard Palethorpe
Richard Palethorpe@jichiep·
parakeet.cpp transcription running on a Raspberry Pi 5 CPU at 7x real-time on a 60s clip under thermal throttling. Of course speed and power consumption could be better on a GPU, but still, nice of @NVIDIAAI to release this model.
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Poolside
Poolside@poolsideai·
Today we’re releasing the weights for Laguna M.1, our most capable model to date, with a 256K context length. Both base and post-trained checkpoints are now available on Hugging Face under Apache 2.0.
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Raid Owl
Raid Owl@RaidOwlTweets·
The DGX Spark cluster has a new home…lots of room to grow 🤔
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Josh Schultz
Josh Schultz@joshuamschultz·
The mini, mobile AI lab
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merve
merve@mervenoyann·
I'm in SF starting 27th, who should I meet and which event do I join 👀
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