https://t.co/2EigydTqJC

NVIDIA AI on X: "https://t.co/2EigydTqJC"

•  Love seeing our partners post-training Nemotron 3.5 Lightning for their own domains, tools, and workflows, as well as those offering post-training support ⚡ Check out below to see what they’re building 🧵 
•  

Nemotron 3.5 Lightning is here, with Day-0 support on Prime Intellect 30B MoE with 3B active params, up to 4x higher throughput, 30% faster task completion Post-train it for your own domain with prime-rl and Prime Lab primeintellect.ai 

•  

We’re excited to see the latest NVIDIA Nemotron model and continue pushing the boundaries of what AI can do for cybersecurity. ⚡️ CrowdStrike customized Nemotron 3.5 Lightning for cybersecurity agent workflows, achieving analyst-grade accuracy with faster training and

•   Today,

@nvidia launched Nemotron 3.5 , its new open model built for fast, high-volume execution in agentic AI systems. Dream had early access to the model. Our researchers, led by Guy Feigenblat, Shai Nahum Gefen and Dmitry Basin, invested extensively in supervised fine-tuning,

•  

Easy to train. Smart at routing. Efficient at scale. In collaboration with Baseten, we fine-tuned NVIDIA Nemotron 3.5 Lightning on CodeRabbit's routing decisions in under three hours and for less than $100. The result? > ~4% higher accuracy than our previous GPT-class model. > 

•   In collaboration with

@nvidia we're releasing two new open weight models: Fastino-Nemotron-3.5-Lightning-Finance and Fastino-Nemotron-3.5-Lightning-Healthcare. Working closely with the Nemotron team, we developed both models on Nemotron 3.5 Lightning using the Fastino 

•   We tested

@NVIDIAAI 's Nemotron 3.5 Lightning against real enterprise agentic workloads & found: 5x throughput vs Gemma 4 31B IT at matched parameter count. Accuracy gains moved Nemotron 3.5 Lightning into evaluation for high-volume paths in the Uniphore's Business AI Cloud 

•  

Smaller, faster, capable: writing machine-checked proofs with a 30B open-weight model We fine-tuned NVIDIA’s latest open model Nemotron 3.5 Lightning on 4B tokens of synthetic Verus data. The result: it beats a model ~50x its size on per-attempt pass rate while nearly matching

•   We are proud to announce a collaboration between AgileRL and

@NVIDIAAI to support the post-training of Nemotron models. NVIDIA's Nemotron open-source model family is now available for fine-tuning on Arena, our platform for creating AI agents specialized at any task. NVIDIA gave 

•   We post-trained

@NVIDIAAI Nemotron 3.5 Lightning on Legal Agent Bench with

@trajectorylabs . Here's what we found: 1) Post-training improved agent performance from 0% to 8.3% on held-out LAB tasks, beating both Opus 4.6 and the much larger post-trained Nemotron 3 Ultra. 2)

•  
•  

Continual learning is a bet that the retraining loop will get cheaper over time. With larger models, you can maybe run this loop once every few weeks. But with smaller models, you can run it nightly, per customer. And it keeps recursing: a model per company, then a model per 

•   Nemotron 3.5 Lightning from

@NVIDIAAI is out. As one of a handful of European launch partners, we fine-tuned it against 4 comparable MoE models on 6 tasks. Prompted, third. Fine-tuned, first. Full numbers, and where it loses, from

@j_golebiowski : distillabs.ai/blog/the-best-… [Video 5](blob:https://x.com/e6a75e53-230c-49c2-a61a-c82a4080ddd9)

00:07

• 
•   Nemotron 3.5 Lightning by

@NVIDIAAI is now supported for training and inference on the Applied Compute Platform. On our agentic coding benchmark, decode throughput, time to first token, and median user latency remained effectively unchanged as concurrency and total token 

•  

NVIDIA Nemotron 3.5 Lightning is live on Baseten day 0! This is the fastest open model in its class, built for long-running agents. Compared to similar-sized open models, it offers: - 4x higher throughput - 50% lower cost - 63.4% fewer output tokens in production testing - 30B 

•  

Looking for a faster specialized model for your Agent Work? NVIDIA Nemotron 3.5 Lightning (30B MoE, 3B active params) is now live on Fireworks. It’s distilled from NVIDIA Nemotron 3 Ultra to be your high-volume agent engine. With strong performance on PinchBench and top-tier

•  

NVIDIA Nemotron 3.5 Lightning is now live on Together AI. The fastest open model in its class is built for always-on agents that need to complete high-volume, specialized work quickly. 

•   Excited to see

@NVIDIAAI continue pushing the open model ecosystem forward with Nemotron 3.5 Lightning. At

@DeepCogito , we are big believers in open weight, customizable models and the role they’ll play in making frontier intelligence broadly accessible and useful. NVIDIA has

•   Nemotron 3.5 Lightning from

@NVIDIAAI is out today and available on Tinker. With just 3B active parameters and optimized for throughput speed, 3.5 Lightning is designed for work where latency and cost matter.

•  

2-bit NVIDIA Nemotron 3.5 Lightning ran tool calls nonstop for 10 minutes on just 22GB of VRAM. 🤯 It cited 80+ websites, executed code & searched for 10 real-world locations. Run and train via Unsloth Desktop. GGUF: huggingface.co/unsloth/NVIDIA… Guide: unsloth.ai/docs/models/ne… [Video 6](blob:https://x.com/ce6e5f99-2371-48a3-af87-9adf9b67ac9f)

02:28

•   Congrats to

@nvidia on the launch of Nemotron 3.5 Lightning. We got early access to test it and found it to be fast, highly customizable and controllable. Read Gustavo A. Lujan, Allen Roush and Andy Nolan's findings 👉 thoughtworks.com/insights/blog/…