NVIDIA Nemotron 3.5 Lightning
NVIDIA's 30B mixture-of-experts model with 3B active parameters, built as the execution layer for always-on agents.
From NVIDIA, Nemotron 3.5 Lightning is an open-weight text model. It takes a text prompt and replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Aug 2026
- Pricing
- Free, runs on your hardware
- Text rank
- #217
Samples
Generated with Nemotron 3.5 Lightning via Ollama. The same three prompts run against every text model in the catalog, shown verbatim, so the only thing that changes between two models’ answers is the model.
Explain what an API is to someone who has never written code. Do not use a restaurant or waiter analogy. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 50 words.
An API, or Application Programming Interface, is a set of rules letting one software program request services or data from another. It handles communication behind the scenes, so users avoid needing to understand either program’s internal code. It acts as a shared language enabling apps to work together securely.
A two-person startup is deciding whether to build their own user authentication or use a hosted provider. Give them a clear recommendation, then the single strongest argument against your own recommendation. Plain prose only — no headings, bullet points, or markdown formatting. Maximum 150 words.
For a two-person startup, the clear recommendation is to use a hosted authentication provider rather than building it in-house. Off-the-shelf solutions eliminate the months of engineering time required to implement, secure, and maintain login flows, password recovery, MFA, and compliance with privacy regulations. This lets the founders focus entirely on product differentiation and user value from day one. The single strongest argument against this recommendation is that hosted providers introduce cost and dependency risk as the user base scales; per-authentication pricing can escalate quickly, and becoming dependent on an external service creates vulnerability to price changes, API shifts, or outages that directly impact your product’s critical login functionality, potentially negating the time savings if the startup grows larger than anticipated.
Write a briefing for a non-technical executive explaining why their company's spending on AI is hard to predict, and what they can do about it. Cover at least three distinct causes. Plain prose only — no headings, bullet points, or markdown formatting. Write exactly 205 words.
As your organization scales artificial intelligence initiatives, the predictability of associated expenditures has become increasingly uncertain. Three interconnected factors drive this volatility. First, the pace of technological evolution means models and tools upgrade frequently; a solution deemed optimal six months ago may now be superseded, prompting re-evaluation, re-licensing, or full replatforming costs that were not part of the original budget. Second, many AI projects stall at the prototype stage, where initial spending appears manageable, but transitioning to production reveals hidden infrastructure demands—such as high-performance computing, data storage, and rigorous validation pipelines—that were omitted from early estimates. Third, indirect costs compound unpredictably. Securing specialized talent, managing model drift and retraining, ensuring compliance with emerging regulations, and integrating AI outputs into existing workflows all require sustained investment that is easy to underestimate or overlook until mid-project. Together, these dynamics create a spending landscape that shifts as initiatives mature. To regain financial visibility, leadership should establish clear scope boundaries, mandate cost-benefit checkpoints at defined milestones, and maintain a dedicated contingency line item for the inevitable adjustments that AI’s rapid lifecycle demands. This structured approach will transform opaque expenditure patterns into manageable, forecastable budget categories. Organizations should revisit assumptions, track spend by category, and align stakeholders around realistic timelines.
NVIDIA
NVIDIA builds the open-weight Nemotron family of language and multimodal models, tuned for enterprise reasoning, agentic workflows, and document intelligence.
developer.nvidia.com/nemotron ↗NVIDIA's open multimodal mixture-of-experts model (120B total, 12B active parameters) for coding, reasoning, tool use and document understanding, with image input and a 1M-token context window.
NVIDIA's compact vision-language model (74B total, 9B active parameters), distilled from Nemotron 3.5 Super and tuned to fit a 24 GB NVIDIA GPU for coding, reasoning and tool use.
NVIDIA's small vision-language model (8B parameters, 4B effective), post-trained from Gemma 4 E4B for fast local conversations, image understanding and multi-turn tool use; a bundled draft model speeds up generation.
NVIDIA's multimodal Nemotron mixture-of-experts model unifying text and image understanding for enterprise Q&A, summarization, and document intelligence.