DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
Nano VLLM: The Minimalist Powerhouse Redefining AI Inference
A DeepSeek employee just dropped a brand new open source project that’s shaking up the AI world, Nano VLLM. It’s fast, it’s clean, and it’s written in just 1,200 lines of Python. No bloated frameworks, no black box complexity, just raw, stripped-down performance that runs powerful AI models at full speed, even on your own machine.
Built entirely in their spare time, this project doesn’t just rival production engines like VLLM. It exposes how they work, step-by-step. It’s not just a tool, it’s a cheat code for anyone curious about what really makes large language models tick.
Now, the basic problem that VLLM solves is speed. When you ask a language model for an answer, there’s a lot going on under the hood. The text you type gets chopped into numbered tokens, those tokens run through many layers of math inside the model, and finally the software picks the next word.
Big engines like VLLM do clever scheduling tricks, so a single graphics card, or several working together, never sits idle during that process. The downside is that the VLLM codebase is sprawling. If you open it up to learn how one specific feature works, you can easily get lost in files that call other files, which call still more files written in lower-level languages.
That makes it powerful, but also hard to read. Nano turns that situation upside down. The author wrote every component in plain, modern Python, with clear comments, and arranged the code so you can follow each step from input prompt to final output without jumping through dozens of layers.
Nano VLLM Outpaces VLLM in Benchmarks—On Just 10 Pages of Code
In practice, it feels like a guided tour of a large language model’s brain, but it still runs fast enough to keep up with its heavyweight cousin in many offline tests. When I say offline, I mean single-user jobs where you already have a chunk of text to process — think research experiments, data labeling, or hobby projects, not the busy public chatbots that need to handle streams of questions from lots of people at once. During the first benchmark, the researcher used a laptop graphics card, an RTX 4070 with 8GB of memory, and loaded a mid-size model called Quen 3.6b. To make the test fair, they asked both VLLM and NanoVLM to generate text for 256 different sequences.
Each sequence started with a random prompt between 100 and 1024 words. Then the engines produced another stretch of text of a similar length. In total, both programs generated 133,966 tokens.
The older VLLM finished in 98.37 seconds, while NanoVLM crossed the line at 93.41 seconds. That translates to roughly 1,362 versus 1,434 tokens per second. In other words, the newcomer was 5% quicker on that specific run, even though its code fits neatly on 10 printed pages.
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The Secret Sauce Behind NanoVLLM’s Speed: Smart Tricks, Simple Code
So where does the speed come from if the code is so small? A handful of tidy but powerful ideas do most of the heavy lifting. First, NanoVLLM keeps a prefix cache. Imagine you ask the model to complete similar openings like once upon a time over and over.
The beginning of that sentence produces the same internal values each time, so the program stores them the first go-round and reuses them later, instead of starting from scratch. Next, it supports Tensor Parallelism, which is a way of splitting the model’s layers across several graphics cards, so each card carries part of the workload. There’s also Torch Compile, a feature in PyTorch that bundles small operations together so the graphics card can run them in one shot instead of many tiny bursts.
Finally, it captures CUDA graphs, which work like pre-recorded instructions for the graphics card. Once the graph is captured, the card can replay it without extra chatter from the CPU. Each trick is well-known in big production systems, yet seeing them laid out in straightforward Python makes them feel a lot more approachable.
Now, here’s how NanoVLLM works, in simple terms. When you give it a sentence, it breaks it down into tokens, which are essentially numbers the model can understand, kind of like translating your words into the model’s language. Then it runs those numbers through a streamlined setup that loads the brain of the AI, keeps track of what’s already been said, and figures out what should come next.
It even adds a bit of randomness or filters out weird responses, depending on how you set it up. All of this happens through clean, easy-to-follow code. No messy layers, no mystery tools, just small, focused files that walk through the process one step at a time.
NanoVLLM Embraces Simplicity and Open Learning Over Production Perfection
That’s a big deal for anyone who wants to actually learn how these models work instead of just using them. Of course, keeping things simple means letting go of a few bells and whistles. NanoVLLM isn’t built to handle dozens of users at once, and it won’t type out answers word-by-word like ChatGPT does.
It also skips advanced tricks that make huge models run on tiny devices. But that’s the trade-off. It’s fast, clear, and easy to understand without the noise.
When news of the release landed on the local Llama subreddit, the top comment quickly pointed out that this is strictly a personal project, not an official deep-seek product. That distinction matters because companies often need extensive testing, legal checks, and customer commitments before they bless anything as official. A personal repository can move faster, try risky ideas, and let others learn from rough edges.
In fact, many people in the thread cheered the hobbyist spirit. They compared NanoVLLM to NanoGPT, which is another famous tiny project. It shows how to train, not just run, a model in as few lines of code as possible.
Installing NanoVLLM is surprisingly simple. You just paste one line into your terminal and the whole thing downloads. Once it’s set up, you load your model folder, adjust a few settings like how long the response should be or how creative it sounds, and run your prompts.
NanoVLLM Sparks a Wave of Experimentation, Education, and Efficient AI
That’s it. It even works almost exactly like VLLM, so if you’re already using that, switching over takes just a couple of small changes. What really stood out in the tests wasn’t just the speed.
It was how efficient the whole thing is. Both NanoVLLM and VLLM produced the exact same amount of text, but NanoVLLM did it faster, using only 8 gigabytes of graphics card memory. That means it’s doing more with less, simply by cutting out all the unnecessary background noise.
And that’s why people are now trying it on more powerful setups with bigger models and multi-graphics card systems to see just how far it can go. Now, it doesn’t support everything yet. There are more complex types of models, like Mixture of Experts, where different parts of the model take turns, depending on the question.
NanoVLLM doesn’t handle those yet. But because the code is so small and well-organized, it wouldn’t be hard for someone to add it. It’s the kind of project that makes developers want to tinker, test ideas, and build on top of it.
That’s also why educators are getting excited. In a classroom, this is the perfect starting point. Students can actually read through the code, understand what’s happening, and then try adding new features themselves.
And because the basics are all here, how to handle input, store memory, and generate text, those lessons carry over to the bigger, more complex frameworks used in real-world applications. There’s also a neat little feature that makes it easier to learn from, a setting called Enforce Eager. When it’s on, the program runs step-by-step, which makes it easy to test, explore, or debug.
NanoVLLM Proves What One Developer Can Do with Clean Code and Smart Design
And once you’ve got it working the way you want, you can flip the switch and let PyTorch optimize everything behind the scenes for better performance. On top of that, it uses CudaGraph to speed things up by recording a full path once and replaying it, which saves time every time it runs. If you’re running a big production system with lots of users and real-time traffic, Nano VLLM probably isn’t what you’ll rely on.
VLLM is still the tool for that. It’s been battle-tested, tuned, and scaled to handle serious workloads. But if you’re learning, experimenting, or building smaller tools that don’t need to support thousands of users at once, Nano VLLM gives you almost the same power with way less complexity.
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What’s really inspiring about this project, though, is how much one person was able to do with clear, focused code. No clutter, no over-engineering, just a smart design that works. Developers on Reddit were actually excited to open the files and see exactly what each piece was doing without having to dig through hundreds of extra lines.
That kind of simplicity opens the door for more people to get involved, especially those who have ideas but get overwhelmed by giant codebases. Of course, how well it performs depends on your setup. How big your model is, how long your prompts are, how much memory you have, and whether you need responses to stream in real time.
The benchmark on a laptop shows what’s possible, but results will vary depending on what you throw at it. Still, it proves that you don’t need a massive system to get impressive speed. And now that it’s out in the open, the project will evolve the way all great open source tools do, through community contributions.
Anyone can jump in, add new features like dynamic batching or a mixture of expert support, and help shape where it goes next. It’s exactly how projects like PyTorch and TensorFlow got started, small, powerful ideas that grew with help from people who cared. What do you think? Could something this small really shift how we build and run AI models? Drop your thoughts below.
If you found this interesting, make sure to stick around. There’s a lot more like this coming. Thanks for reading.
Catch you in the next one.
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
- DeepSeek Dev Drops NANO and Internet Is Going WILD Over This
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