Teaching your AI new tricks.
Santa Clara, CA
Joined June 2016
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NVIDIA AI retweeted
That's a wrap on the #NVIDIAGTC Berlin keynote. 💚
ICYMI, watch the replay: youtube.com/live/PvFjbPYEOZI
NVIDIA AI retweeted
Our CEO Jensen Huang is LIVE at #NVIDIAGTC Berlin.
Watch the keynote now ⬇️ youtube.com/live/PvFjbPYEOZI…
NVIDIA AI retweeted
Qwen3.8-Flash-Next and GLM-5.3-Flash, optimized for local AI. ⚡️
NVIDIA’s FP4-Local checkpoints reduce memory requirements, giving you more room for agents and longer context on DGX Spark and RTX PRO 6000 Blackwell systems.
Explore the NVIDIA Local AI Optimized Checkpoints collection: huggingface.co/collections/n…
@Alibaba_Qwen @Zai_org
NVIDIA AI retweeted
Four cities. One clear message: Europe is building what’s next in AI.
Tomorrow, Jensen takes the stage at #NVIDIAGTC Berlin to continue the conversation.
See you there.
NVIDIA AI retweeted
Can’t make it to #NVIDIAGTC Berlin? We’ve got you covered. 🎙
Join us for 3 days of livestreams covering the biggest moments of the week, from the keynote recap to real-world AI demos and developer stories straight from Berlin. Add the following streams to your calendar:
→ Mon, Oct 19 | Road to GTC: Europe Recap nvda.ws/3U61a1U
→ Wed, Oct 21 | GTC Berlin Day 1: Keynote Recap nvda.ws/4yOS9JN
→ Thu, Oct 22 | GTC Berlin Day 2: Inside the Experience nvda.ws/4zwMesL
NVIDIA AI retweeted
Nvidia’s Nemotron 3.5 Super VL is now the leading US open weight model, scoring 26 on the Artificial Analysis Intelligence Index
Nemotron 3.5 Super VL achieves twice the intelligence of its predecessor, Nemotron 3 Super (13), and does so using ~60% fewer output tokens per task
It launches alongside Nemotron 3.5 Bolt 74B-A9B 24GB, the first model in its new Bolt family, which scores 21 on the Intelligence Index
Congratulations to @NVIDIAAI on the releases!
Key Takeaways:
➤ Leading US open weight model: Nemotron 3.5 Super VL scores 26.5, edging out Thinking Machines' Inkling Small (25.7) and ahead of NVIDIA's Nemotron 3 Ultra (23) and Meta's Muse Glimmer (17)
➤ Bolt comes within 2 points of Ultra: Nemotron 3.5 Bolt 74B-A9B 24GB scores 21, 1.5 points behind Nemotron 3 Ultra 550B A55B (23). The 2 bit memory has about a seventh of Ultra's parameters (74B vs 550B) and a sixth of its active parameters (9B vs 55B)
➤ Super 3.5 VL near lossless in NVFP4: Nemotron 3.5 Super VL releases in NVFP4, scoring within 0.1 points of its BF16 weights on the Intelligence Index, with no component moving by more than 2.6 points
Nemotron 3.5 Super VL details
Context window: 1M tokens
Modalities: Vision and text input; text output
Parameters: 120B / 12B
Nemotron 3.5 Bolt 74B-A9B 24GB details
Context window: 256k tokens
Modalities: Vision and text input; text output
Parameters: 74B / 9B
NVIDIA AI retweeted
I've been traveling the world a bit. One of the most incredible experiences was getting to visit Taiwan w/ @JensenHuang. It was wild 🤯.. huge crowds of engineers, insanely good street food at night markets, and of course a lot of good conversations across many days. I caught some of it on video, see below. Alright, that's it from me, love you all ❤️
Timestamps:
0:00 - Highlights
0:46 - Intro
3:25 - Computex: crowds, engineers, tech, future of AI
9:12 - Chat with Jensen: keynote prep & nerves
13:03 - Chat with Jensen: Taiwan's tech history
19:47 - Raohe night market
29:37 - Chat with Jensen: memories from childhood
33:44 - NVIDIA party
40:29 - Lunch: braised pig's feet
43:09 - Chat with Jensen: GPUs, AI, and competition
55:07 - Ningxia night market
1:10:14 - Taiwanese breakfast
1:29:54 - Outro
NVIDIA AI retweeted
Qwen3.8 Flash Next on Vera Rubin with NVIDIA Dynamo + SGLang:
Up to 6.6× throughput per chip and up to 76% lower modeled cost per total token vs. GB300, at matched per-user speed.
Thanks @NVIDIAAI for the partnership and early access, and @SemiAnalysis_ for the results!
ALERT🚨: NVIDIA SGLANG INFERENCE RUBIN IS 4.23x BETTER 🚀 PERF PER DOLLAR THAN EVEN GB300 NVL72. This is on the widely used production LLM engine called @sgl_project. (1/2)🧵
NVIDIA AI retweeted
A new beginning for @Windows PCs deserves a global celebration.
Join us at one of our @NVIDIARTXSpark launch events. Try out demos on the new laptops, pick up some swag, and enter for a chance to win an @ASUS ProArt P14 RTX Spark laptop signed by Jensen. Plus, more exclusive experiences to discover at each stop.
Here's where we'll be:
• Beijing
• Berlin
• Istanbul
• Seoul
• San Francisco
• Taipei
📍Event details and how to join us coming soon.
NVIDIA AI retweeted
Berlin, you look good in CUDA. 💚
#NVIDIAGTC is coming to town. We thought we’d say hello from the TV Tower.
Spot it? Tag @NVIDIA and show us your view. #FestivalOfLights
NVIDIA AI retweeted
Awesome day, @satyanadella!
Windows sparked a platform shift that created a new industry for NVIDIA. Then we invented programmable shading GPUs for DirectX, which led to CUDA.
Then we partnered to bring GPU supercomputers to Azure, which helped OpenAI train GPT. That collaboration inspired us to reinvent Windows for the age of personal agents.
4 years. Thousands of engineering years between us.
So proud of what we built together.
Great to partner with NVIDIA as we kick off this next chapter for Windows.
Thank you @JensenHuang and @sriramk for joining us today!
NVIDIA AI retweeted
That's a lot of daily tokens @togethercompute 👀
Together is the #1 provider by volume of tokens (across closed and open endpoints) on @OpenRouter
openrouter.ai/providers
NVIDIA AI retweeted
1/8) FastVideo FastH3 now runs on a single consumer machine: @NVIDIA RTX GPUs 💚, DGX Spark and @Apple Silicon 🍎
FastH3 V2 beats base @MiniMax_AI H3 in 8 steps. A 5 s 480p clip with audio takes 15 s on one RTX 5090.
We also release FastH3 Trim: 4.2× smaller than base H3, and it runs in as little as 8 GB of GPU memory.
Every clip here was generated locally. @NVIDIAAI @NVIDIARTXSpark
NVIDIA AI retweeted
Take us back: What was your first @Windows PC and what did it spark a love for?
With NVIDIA RTX Spark launch coming soon, we want you to share your story for a chance to win an @ASUS P14 RTX Spark laptop signed by Jensen Huang.
Reply with your memory and include #ShareYourSpark, or use the hashtag in your own original post. Photos are encouraged, but not required!
NVIDIA AI retweeted
RL environments now have a home on the @huggingface Hub.
Every RL framework has its own way to find environments. Custom hubs, runtime registries, GitHub lists with a custom loader. If you publish for one framework, users of the others can’t load it.
We think that is wrong. Environments should be just like datasets because it’s made from tasks, tests, containers and reward functions. The Hub already stores, versions, gates and previews data for millions of people so it doesn’t need a second system.
Here’s how we’re shipping environments on the hub: huggingface.co/blog/rl-envir…
