NVIDIA Nemotron 3
NVIDIA's multimodal Nemotron mixture-of-experts model unifying text and image understanding for enterprise Q&A, summarization, and document intelligence.
NVIDIA's Nemotron 3 is an open-weight text model. It is multimodal: alongside a text prompt it accepts images, then replies with generated text. It runs entirely on your own hardware through Ollama: no API key and no per-use fees.
- Released
- Apr 2026
- Pricing
- Free, runs on your hardware
- Inputs
- Text, Images
Samples
Generated with Nemotron 3 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 is a set of rules that lets one piece of software talk to another, defining what requests can be made and what responses look like, so developers can use services or data without knowing the internal details.
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.
I recommend that the startup use a hosted authentication service rather than building its own solution. A managed provider handles user storage, password hashing, multi‑factor options and regulatory compliance, letting the founders focus on product development and reducing the risk of security flaws. The single strongest argument against this recommendation is that you are surrendering control of a critical security boundary to an external vendor; a breach or service outage at the provider can expose user data, force a costly migration, and damage trust, potentially outweighing the time and expertise saved by using a hosted API.
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.
Predicting how much we will spend on artificial intelligence is difficult because the technology evolves rapidly and because projects often shift between experimental pilots and full production, each with different cost structures. First, the pricing model for AI services is not fixed; usage‑based pricing, tiered subscriptions, and custom enterprise contracts change as vendors introduce new features or negotiate volume discounts, so the same workload can cost more or less month to month. Second, the scope of AI initiatives is fluid; early‑stage models may require costly data acquisition, labeling, and infrastructure before any measurable value appears, and the decision to expand a pilot into a company‑wide deployment can happen without a clear prior budget. Third, internal talent shortages mean we frequently rely on external consultants or specialized vendors, whose rates fluctuate with market demand and with the need for rapid skill transfer, making expense forecasts unstable. To bring more predictability, we should establish a phased budgeting process that separates exploratory spending from scale‑up costs, negotiate fixed‑price contracts for core services, and build an internal competency pool to reduce reliance on pricey external resources. Regularly reviewing usage metrics and vendor proposals will also help align actual spend with the original financial plan for senior leadership review today.
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 30B mixture-of-experts model with 3B active parameters, built as the execution layer for always-on agents.
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.