[ Idea ]Found 13 Nov · 1 Product Hunt · 1 GitHub

Headroom

Measure which AI models your audience's computers can run.

Should you build this?Checking

This idea is still going through the six checks.

0 products · 7 sourcesPending

The problem

Tools that say whether you can run a local AI model guess from spec lists, so people download large files only to find them too slow.

Developers shipping local-AI apps and hackathon organisers have no measured data on what share of their users' machines can run a model well. Users lose time and disk space, often on a 20 GB download, before finding out the model is slow or will not fit.

Seen in 1 Product Hunt · 1 GitHub

The idea

Headroom runs a quick speed test in the browser on each visitor's machine and reports what share of the audience can run each model.

1Visit a page with the Headroom widget and see a verdict for each model.
2A short speed test runs on your own machine, with no personal details collected.
3Creators see what share of visitors can run their models at a usable speed.

Price: The widget and public leaderboard are free. Creator Pro is $19/month per product, with a custom model catalog, no branding and a weekly audience-reach dashboard. Event Census is $49 per hackathon or cohort. A one-off 'Launch Reach Report' for a model release is $99. A UPI/Razorpay option is available for Indian buyers.

[ The six checks ]0 of 6 done

1Not built in alreadyNot checked

2An AI agent can't do itNot checked

3Nothing similar existsNot checked

4People pay for thisNot checked

5Strong scoresNot checked

6Real sourcesNot checked

1 · Not built in alreadyNot checked

2 · An AI agent can't do itNot checked

Who paysIndie developers and small teams shipping local-AI desktop apps, open-model fine-tuners and small labs who need audience reach data, and hackathon organisers or DevRel people who set model and compute rules for participants. Each pays from a tools or event budget.

Who uses itDevelopers and hobbyists running local LLMs on personal laptops and desktops. They are the data source and the free users. The paying side is the people who ship local models to them.

First 10 users1) Build a free 'Hackathon Rig Census' link. Offer it to 5 Indian hackathon organisers and DevRel contacts from his network, such as GDG on Campus and Takshila clubs, and Superteam India events. Participants run a 30-second check, and the organiser gets a distribution of VRAM, RAM and measured tok/s for the cohort. 2) DM 10-15 indie builders on X who have shipped local-AI apps or fine-tunes, and offer a free 'reach report' for their model. 3) Post the first public dataset on X and LinkedIn, for example 'what an Indian student laptop can really run'. That is original data, and it will pull in more embeds and a few paying creators.

Why not just AIThe value is measured data from many strangers' machines, not reasoning. A buyer's own AI agent can only read spec sheets or run a benchmark on the buyer's own rig. It cannot tell a model maker that 38% of their visitors have at least 8 GB of VRAM and reach at least 15 tok/s on a given quant. It also cannot give a hackathon organiser the hardware distribution of 300 participants. Only an embedded widget with real visitors can produce that, and it improves as more sites embed it.

[ Inspired by ]What it borrows, and what's different

Steam Hardware & Software Survey
Opt-in monthly census of real gamer hardware that developers use to set minimum specs. It records what hardware people own, not how it performs. Headroom measures actual local-LLM speed per model and quant, and shows it per creator's own audience, not one global survey.
CanIRun.ai
Browser WebGPU detection that grades open models against your GPU, with no install. It is a free, estimate-based consumer checker. Headroom adds a short in-browser measured test and sells the aggregated results to the model and app makers, not to the end user.
LocalScore (Mozilla Builders)
Open benchmark with a public results database for local LLM speed. It is a CLI with fixed reference models and a global leaderboard. Headroom is a zero-install embeddable widget with per-creator audience dashboards and event cohorts.

[ Sources ]7 cited

RigSpark is a new, active tool for 'which local LLMs can my computer run?'. Its README says unknown figures stay unknown and estimates are not benchmarks. That confirms demand for the problem and that the tool's speed numbers are estimates, not measurements.github.com ↗
CanIRun.ai is popular but its tokens/sec figures are estimated. WebGPU hides true VRAM, so it infers hardware from the GPU name and a spec database, then uses a bandwidth formula for speed. This shows a gap in measured data.runaihome.com ↗
Another review says CanIRun.ai grades are estimates, not guarantees, and that it handles Apple Silicon unified memory poorly.toolcenter.ai ↗
Measured crowdsourced data exists (LocalScore) but is CLI-only and uses a few fixed Llama models. The Micro Center guide says it is not for everyone because it is text-based. It is not embedded in the places where model and app makers meet users.microcenter.com ↗
Hardware mix is shifting fast. 16 GB VRAM GPUs passed 8 GB GPUs on Steam in July 2026, so reach data goes stale quickly and is valuable to refresh.tech-insider.org ↗

[ Idea ]Found 13 Nov · 1 Product Hunt · 1 GitHub

Headroom

Measure which AI models your audience's computers can run.

Should you build this?Checking

This idea is still going through the six checks.

0 products · 7 sourcesPending

The problem

Tools that say whether you can run a local AI model guess from spec lists, so people download large files only to find them too slow.

Seen in 1 Product Hunt · 1 GitHub

The idea

Headroom runs a quick speed test in the browser on each visitor's machine and reports what share of the audience can run each model.

1Visit a page with the Headroom widget and see a verdict for each model.
2A short speed test runs on your own machine, with no personal details collected.
3Creators see what share of visitors can run their models at a usable speed.

[ The six checks ]0 of 6 done

1Not built in alreadyNot checked

2An AI agent can't do itNot checked

3Nothing similar existsNot checked

4People pay for thisNot checked

5Strong scoresNot checked

6Real sourcesNot checked

1 · Not built in alreadyNot checked

2 · An AI agent can't do itNot checked

Who uses itDevelopers and hobbyists running local LLMs on personal laptops and desktops. They are the data source and the free users. The paying side is the people who ship local models to them.

[ Inspired by ]What it borrows, and what's different

Steam Hardware & Software Survey
Opt-in monthly census of real gamer hardware that developers use to set minimum specs. It records what hardware people own, not how it performs. Headroom measures actual local-LLM speed per model and quant, and shows it per creator's own audience, not one global survey.
CanIRun.ai
Browser WebGPU detection that grades open models against your GPU, with no install. It is a free, estimate-based consumer checker. Headroom adds a short in-browser measured test and sells the aggregated results to the model and app makers, not to the end user.
LocalScore (Mozilla Builders)
Open benchmark with a public results database for local LLM speed. It is a CLI with fixed reference models and a global leaderboard. Headroom is a zero-install embeddable widget with per-creator audience dashboards and event cohorts.

[ Sources ]7 cited

RigSpark is a new, active tool for 'which local LLMs can my computer run?'. Its README says unknown figures stay unknown and estimates are not benchmarks. That confirms demand for the problem and that the tool's speed numbers are estimates, not measurements.github.com ↗
CanIRun.ai is popular but its tokens/sec figures are estimated. WebGPU hides true VRAM, so it infers hardware from the GPU name and a spec database, then uses a bandwidth formula for speed. This shows a gap in measured data.runaihome.com ↗
Another review says CanIRun.ai grades are estimates, not guarantees, and that it handles Apple Silicon unified memory poorly.toolcenter.ai ↗
Measured crowdsourced data exists (LocalScore) but is CLI-only and uses a few fixed Llama models. The Micro Center guide says it is not for everyone because it is text-based. It is not embedded in the places where model and app makers meet users.microcenter.com ↗
Hardware mix is shifting fast. 16 GB VRAM GPUs passed 8 GB GPUs on Steam in July 2026, so reach data goes stale quickly and is valuable to refresh.tech-insider.org ↗