How to Run NVIDIA’s Nemotron Locally on Your Laptop or Desktop

NVIDIA released Nemotron, a free open-source AI model designed for reasoning and agentic tasks, which can run locally on personal computers without cloud dependency. The model uses a hybrid Mamba-transformer architecture with mixture-of-experts, offering a 4-billion-parameter version for laptops and a 30-billion-parameter version for desktops. Users can run it via Ollama, a free tool that handles installation and GPU acceleration.

NVIDIA gives Nemotron away for free, and it’s built differently from most open models, designed from the ground up for reasoning and AI agents. You can run it right on your own laptop or desktop, no cloud, no monthly bill, no data leaving your machine. Here’s exactly which version your hardware can handle, the real commands to get it going, and the gotchas that will trip you up. NVIDIA’s Nemotron is one of the more interesting open models you can run on your own computer, and not only because it’s free. It was built specifically for reasoning and agentic work, the kind of tasks where a model thinks through a problem step by step rather than just chatting. It even shows its reasoning before giving a final answer, which you can toggle on or off. And it runs entirely on your own machine, with no API bill and nothing sent to anyone else’s servers. The one thing to get right is matching the model to your hardware, because Nemotron comes in different sizes and your computer decides which one you can actually run. The good news is there’s a small version built for ordinary laptops and a larger one for serious desktops, so almost any modern personal computer can run some form of it. This guide covers exactly that: which Nemotron fits your machine, the precise steps to install and run it, and how to deal with the limits you’ll hit. No cloud, no data center, just your own computer. First, the thing that makes Nemotron a little different. Before the setup, two quirks of Nemotron are worth knowing, because they shape everything about running it. The first is the architecture. Most open models are straightforward transformers. Nemotron uses a hybrid design that mixes a newer approach called Mamba with the usual transformer layers, wrapped in a mixture-of-experts structure. In plain terms, even the larger model only activates a small slice of itself for any given request, around three and a half billion of its thirty billion parameters, which makes it run more efficiently than its size suggests. The practical upshot is that a 30-billion-parameter Nemotron runs lighter and faster than the raw number would lead you to expect. The second is that Nemotron is a reasoning model by default. It first generates a chain of thought, working through the problem, and then gives its final answer. You can turn that reasoning off for simple tasks to save time, but it’s on by default because the model was built for problems that benefit from thinking first. That makes it genuinely good at coding, math, and agent-style tasks, and a little slower and more verbose on trivial ones. Both of these explain its hardware needs and its behavior, so keep them in mind. For running on a personal computer, two models matter: the small Nemotron Nano at 4 billion parameters, built for laptops and ordinary machines, and the larger Nano at 30 billion, which needs a serious desktop GPU. There are bigger Super and Ultra tiers too, but those are built for data-center hardware and aren’t something you run on a personal computer, so we’ll leave them aside. Which of the two realistic options you pick comes down to your hardware, so let’s go through both. Across every personal computer, one free tool handles all the hard parts of running Nemotron, so it’s worth naming up front. Ollama is an open-source app that manages the downloading, the compression, the memory handling, and the GPU acceleration automatically. You install it once, and from then on running a model is a single command. It works the same way on a Mac, a Windows PC, or a Linux machine, and it’s what we’ll use throughout. Installing it is quick. Go to ollama.com, download the installer for your system, and run it. On a Mac you drag it to your Applications folder; on Windows it installs like any normal program; on Linux it’s a single command, curl -fsSL https://ollama.com/install.sh | sh. Once it's installed, it runs quietly in the background and starts a local server automatically. Confirm it's working by opening your terminal, or PowerShell on Windows, and typing ollama --version. If it prints a version number, you're ready. Start here, because this covers most people, and Nemotron has a model built exactly for ordinary hardware. The small Nemotron, the 4-billion-parameter Nano, is the model for a laptop or a typical desktop. It needs only around 5 gigabytes of memory to run, which means it fits comfortably on a machine with 16 gigabytes of RAM, or on any Apple Silicon MacBook. Despite its small size, it carries the same reasoning-first design as its bigger sibling and a large context window, so it’s genuinely useful for coding help, reasoning problems, and agent experiments, not just light chat. For a free model running entirely on your own laptop, it’s a strong place to start. Getting it running takes about a minute once Ollama is installed. curl http://localhost:11434/api/chat -d '{ "model": "nemotron-3-nano:4b", "messages": {"role": "user", "content": "Explain mixture-of-experts in one sentence."} }' The bottleneck on an everyday machine is memory, and the fix is to stay with the 4B model rather than reach for the 30B, which won’t run well on typical laptop hardware. If even the standard 4B feels tight on an older or lighter machine, there are smaller quantized versions, like the one tagged nemotron-3-nano:4b-q8 0, that trade a little quality for a smaller, faster footprint. Closing other memory-hungry apps before you run it helps too. For most people on a normal computer, the 4B Nemotron is the sweet spot, a capable reasoning model running free and private on hardware you already own. Now the more powerful personal computer, the desktop built around a strong graphics card, where the larger 30-billion-parameter Nemotron becomes an option, with one honest caveat about how much graphics memory you really need. The 30B Nemotron is the model for a serious desktop, but it’s demanding. To run it well, you want a GPU with around 24 gigabytes of video memory, which in practice means a high-end card like an RTX 3090, 4090, or the newer 5090. On a 5090 with its 32 gigabytes, the 30B runs comfortably and fast, entirely on the graphics card. Here’s the honest part, though. On a more typical gaming PC with a smaller card, say one with 8 gigabytes, the model still runs, but it spills over onto your system memory and processor, which works but is slow and makes your fans roar. Someone running it on a gaming laptop with an 8-gigabyte card found it used about 20 gigabytes of system RAM and only 6 of video memory, with long response times. So the 30B will run on modest hardware. It just won’t run quickly. The steps are the same simple Ollama flow. The bottleneck is video memory, and the real fix is matching your expectations to your card. With 24 gigabytes or more, the 30B runs beautifully. With less, it runs but leans on your processor and slows down, so on a smaller card you may genuinely be happier with the fast 4B model than the sluggish 30B. The other practical lever is the model’s large context window, which defaults to a smaller size to save memory and which you can raise only if your hardware has room. For a desktop with a big graphics card, the 30B Nemotron is a powerful, private, free reasoning model running right on your own machine. For a smaller card, temper expectations or stay with the 4B. Two real pitfalls, because they’ll save you a frustrating afternoon. First, if you’re on an NVIDIA GPU, avoid the CUDA 13.2 driver version specifically, because at the time of writing it can cause Nemotron to produce gibberish output, and NVIDIA is working on a fix. Stick to a known-good driver version. Second, the multimodal version of Nemotron, the one that handles images and audio, doesn’t yet work cleanly through Ollama because of how its vision files are packaged. So if you specifically need the vision features, you’ll need a llama.cpp-based backend rather than plain Ollama. For text, reasoning, and coding, which is what most people want, plain Ollama works fine. Step back and the choice is simple. If you have a laptop or an ordinary desktop, run the 4B Nemotron with Ollama and you have a capable, free, private reasoning model in about five minutes. If you have a desktop with a strong graphics card, around 24 gigabytes of video memory or more, the 30B runs beautifully at home and gives you noticeably more capability. And if your graphics card is smaller, stay with the 4B rather than suffer a sluggish 30B, because a fast small model beats a crawling large one for almost everything. The throughline is the one that governs every local model: memory decides what you can run, and Nemotron’s efficient design means it punches a little above its size. The best part is that you can start right now, on whatever you already own. Install Ollama, run ollama run nemotron-3-nano:4b, and you've got one of NVIDIA's open reasoning models thinking through problems on your own computer, for free, in the next five minutes. This is another in a set of hands-on guides to running the major open models yourself. If you’ve run Nemotron on your own machine, drop a comment with your hardware, the size you landed on, and how the reasoning-first behavior worked for your tasks. The honest experience helps the next person more than any spec sheet. How to Run NVIDIA’s Nemotron Locally on Your Laptop or Desktop https://pub.towardsai.net/how-to-run-nvidias-nemotron-locally-on-your-laptop-or-desktop-5d5e6c3359aa was originally published in Towards AI https://pub.towardsai.net on Medium, where people are continuing the conversation by highlighting and responding to this story.