NVIDIA Nemotron 3.5 Bolt
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.
From NVIDIA, Nemotron 3.5 Bolt 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
- Oct 2026
- Pricing
- Free, runs on your hardware
- Inputs
- Text, Images
Samples
Generated with Nemotron 3.5 Bolt 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 way for software programs to talk to each other. It acts like a bridge that lets one program request information from another without knowing how it works. When you check the weather on your phone, your app uses an API to get data from a service.
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.
Use a hosted authentication provider. A two-person team lacks the bandwidth and security expertise to build, maintain, and harden a system that is not your core product, and a single mistake can cause a catastrophic breach. Offloading login, session management, and compliance lets you focus on your actual value proposition. The strongest argument against this recommendation is that you surrender control of a fundamental part of your product and your users' sensitive data to a third party, creating dependency on their pricing, uptime, and data policies, which can result in vendor lock-in, unexpected cost increases, or painful migration difficulties if the service changes or you outgrow it.
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.
Executives often find AI spending harder to forecast than traditional IT budgets because the technology evolves in unpredictable ways. First, model performance depends on data quality and domain specificity, meaning initial pilots may succeed in controlled environments but fail when exposed to real-world variability, requiring unplanned remediation work. Second, the pricing landscape shifts constantly as vendors introduce new consumption models, token-based billing, and enterprise tiers that make long-term cost estimates unreliable. Third, talent and infrastructure requirements are fluid; hiring specialized engineers or upgrading cloud capacity cannot be precisely scheduled because breakthroughs in efficiency or new regulatory demands can suddenly change resource needs. To manage this uncertainty, leadership should adopt modular budgeting that allocates funds in small tranches tied to measurable outcomes rather than large upfront commitments. Establishing a cross-functional AI governance committee helps align spending with business priorities and prevents vendor lock-in. Finally, investing in internal data governance and reusable model components creates platform economics that reduce the need for repeated expensive custom builds. By treating AI as an iterative capability rather than a fixed capital project, companies can control costs while remaining agile enough to exploit genuine breakthroughs. Such disciplined experimentation protects shareholder value while positioning the organization to benefit from future innovations continually.
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 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.