Will this open model run on my NVIDIA GPU? I tested the math so you don't have to guess!
Open AI models are free to download and use. The hard part is knowing, before you spend an evening on it, whether a model will actually run on the computer you have.
I'm a product manager, not a machine-learning engineer. I built a small free tool called LaunchReady to answer that question, then checked its answers on real NVIDIA GPUs. This post covers what it does, what held up, and what broke.
- Try it (free, no login): https://huggingface.co/spaces/tanmaypathak4/launchready
- Code and results: https://github.com/Tanmaypathak18/launchready
- Project page: https://tanmaypathak18.github.io/launchready/
The question everyone asks first
Every time a new open model comes out, people ask the same thing: "Will it run on my machine?"
Think of a graphics card (GPU) as a desk. The model is a stack of papers that has to fit on the desk. On top of that, every conversation you have with the model needs extra desk space for its working notes, and longer conversations need more.
If the stack doesn't fit, nothing works. Often you only find out after a long download.
What LaunchReady does
You give it the name of a model on Hugging Face. It reads the model's published description and file sizes, and does not download the model itself. In seconds it tells you:
- how much memory the model needs at different sizes (open models come in compressed versions that trade a little quality for much less memory)
- how many simultaneous conversations fit on a given GPU
- which NVIDIA GPUs can run it
- whether the popular tools for running models (vLLM, SGLang, llama.cpp, Transformers) support it yet
It can also write a one-page brief on any model, covering what it is, what hardware it needs and who it is for.
Every number it shows is labeled projected (calculated) or measured (observed on a real machine). I wanted nobody to have to guess which is which.
Did the math hold up? Mostly.
I tested the projections on free cloud GPUs (two NVIDIA Tesla T4 cards) using three kinds of models.
“Projected vs measured memory on two Tesla T4 GPUs”
Standard models: close.
- Qwen3-4B: the tool projected room for 7.12 conversations at 4,096 tokens of context. I measured 7.41. That's 3.9% off, on the cautious side.
- OLMoE-1B-7B (a "mixture of experts" model): projected 20.22, measured 21.47. That's 5.8% off.
- The memory taken up by the models themselves matched within 1%.
A newer design: badly off.
- granite-4.0-h-tiny uses a newer architecture called a hybrid Mamba design. The tool projected room for 118.08 conversations. The real number was 50.58. That's 133% too optimistic.
- Two things combined to cause it. The engine reserves more overhead memory than my formula assumed (confirmed from its log). Each conversation also costs more memory than predicted (the size is measured, but I haven't confirmed why).
- I did not quietly tune the formula to hide this. The tool now warns when it sees this kind of model.
This matters because NVIDIA's own Nemotron 3.5 Lightning is also a hybrid Mamba model, so projections for it carry the same risk.
Three "getting started" guides that didn't work
I also acted as a first-time user and ran the model cards' own instructions word for word.
- Qwen3-4B, vLLM command: it failed within 30 seconds because of a command option the current version doesn't recognize.
- Qwen3-4B, Python example on a 16 GB Mac: it quietly spilled onto the hard drive and produced no answer after 45 minutes. The same example finished in 115 seconds on the GPU machine.
- Nemotron 3.5 Lightning, llama.cpp instructions: they ask for a file named Q4_K_M, which doesn't exist in the official repository. It failed in 6 seconds.
None of these are anyone's fault. They are exactly what a first-time user hits, and the model builders can fix them quickly if they know.
Labels don't always mean what you think
Files labeled "Q4_K_M" sound like 4-bit. The three Nemotron 3.5 Lightning files I checked actually store about 6.1 bits per weight, so they need more memory than the name suggests. I measured this from the files' own headers, with no GPU involved.
What I haven't done
I have not run Nemotron 3.5 Lightning on an RTX 5090 yet. Every Lightning number in the project is projected, not measured. My readiness checklist for it currently has 1 item ready, 5 not ready and 21 unknown, because that checking hasn't happened and will be done in the coming phases of the project.
The tool is also not a replacement for testing. It tells you where to expect trouble.
Who this is for
- If you're curious about open models but aren't an engineer: paste a model name into the free tool and see whether it fits your GPU before you download anything.
- If you run or support models: the repo has a launch checklist, recipes and the full list of what broke, with the evidence for each result.
I'd welcome corrections, especially from people who have run these models on other GPUs. Issues and suggestions are open on the GitHub repo.
Measured results: 2× Tesla T4 on Kaggle, vLLM 0.32.0, run on 2026-11-03. All raw data is in the repo.
