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MODEL · ENTITY #18767

Nemotron 3.5 Super VL

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MODELS · 2 SOURCES · TechCrunch AI · Hugging Face

Nvidia unveils Nemotron 3.5 Super VL and lightweight Bolt models for enterprise agents and local AI PCs

Nvidia announced new open-weight models aimed at always-on agents and edge/PC deployment: Nemotron 3.5 Super VL (120B parameters) for enterprise agents, plus two lightweight Bolt models including Nemotron 3.5 Bolt 74B-A9B 24GB and the smaller Bolt E4B for local AI PCs and workstations. Nvidia says Super VL and Bolt 74B-A9B are available now (Bolt E4B coming later this month), claims Super VL can match larger Nemotron 3 Ultra on instruction-following and outperform some competing models in post-trained agent tasks, and named early users such as Skydance, EY, Ramp, CrowdStrike, Perplexity and Salesforce.

8.0

RESEARCH · 1 SOURCE · Hugging Face

NVIDIA and AI4Bharat release Nemotron-pretraining-Indic-v1: 2.8T-token Indic corpus and open GPU data pipeline

NVIDIA, IIT Madras (AI4Bharat) and collaborators are releasing Nemotron-pretraining-Indic-v1, a synthetic pretraining corpus of about 2.8 trillion tokens spanning 22 Indian languages, together with an open, GPU-accelerated data-generation pipeline (including prompts, LLM workflows, and quality checks). The corpus includes native-script and romanized variants, preserves technical content (math, code, tables), traces outputs back to curated source documents, and was partially used to add 10 billion Indic tokens to continued pretraining of Nemotron 3.5 Super VL; the data and pipeline are being shared so others can reproduce and extend the workflow.

8.0

MODELS · 2 SOURCES · NVIDIA Developer · The Decoder

NVIDIA releases Nemotron 3.5 Super VL, a 120B-parameter open multimodal model for agents

NVIDIA announced Nemotron 3.5 Super VL, an open 120B-parameter mixture-of-experts multimodal model that activates 12B parameters per token to support agentic workflows combining reasoning, instruction following, tool use, and text, image, document, and video understanding. NVIDIA says it achieves up to 5× higher single‑GPU throughput on agentic tasks versus comparable open models, supports post‑training via NeMo (SFT, LoRA, RL) with resources on Hugging Face and a GitHub usage cookbook, and employs inference optimizations such as LiLiCorr speculative decoding and NVFP4 quantization; partners including Scale AI, Ramp, and AI21 report substantial cost, latency, or accuracy improvements in production workloads.

8.0