Lemura AI Labs
Talk to us

Enterprise

Run it where your data already is.

Open weights, compressed to the hardware you own, evaluated with the numbers that could embarrass us. Tell us the constraint and we will tell you what fits.

Talk to us See the models
46
open-weight models
506k
all-time downloads
Apache & MIT
on most releases

What you get

Four things, and they are all checkable

None of this asks you to take our word for it — the weights are public and the evaluations come with their own counter-evidence.

Deployment

The model comes to the data

Everything we publish is open weights, so it runs inside your account, your datacentre, or an air-gapped room with no call home. Nothing about our work assumes your data can leave the building.

Compression

Sized to the hardware you own

Expert pruning and quantization take frontier models down to something a single GPU — or a laptop — can serve. If you tell us the hardware, the question becomes what fits on it rather than what you have to buy.

Evidence

Numbers, including the awkward ones

Refusal rate with the KL divergence beside it. Word error rate with the parameter count beside it. You get the evaluation that could embarrass us, not the half that flatters the model.

Direction

A path onto your own silicon

Compact models are the near term. Compiling detection models onto reconfigurable chips is the work after that, which is what makes inspecting every request affordable rather than theoretical.

Where this lands

Who tends to need it

The common thread is a constraint that rules out an API — data that cannot move, hardware that cannot change, or a language nobody else has bothered with.

Healthcare & national genomics

Programmes that cannot export a single record, and no foundation model built for their population.

Financial services

Transaction-adjacent AI where the audit trail matters as much as the answer.

Telecom, MENA and the Gulf

Voice-channel analytics in Arabic dialects the incumbent systems handle badly.

Defence & government

Air-gapped, auditable, and hardware-rooted from the start rather than retrofitted.

Manufacturing & industrial

Edge deployments where the power budget is fixed and a cloud round trip is too slow.

AI-native software

Teams shipping agents who have inherited the attack surface that comes with them.

How it goes

Three steps, no procurement theatre

  1. 01

    You tell us the constraint

    The hardware, the language, the rule about where data may sit. The constraint is more useful to us than the wish list.

  2. 02

    We come back with what fits

    Within two business days, from someone who works on that thread — with the numbers we already have and an honest note on what we have not measured.

  3. 03

    You run it yourself

    Open weights, on your infrastructure, evaluated against your data before any commitment.

Enquiry

Tell us the constraint

Eight fields, five of them required. The more specific you are about the hardware and where the data sits, the more useful the reply.
A work address tells us which organisation we are talking to.
This one shapes the answer more than anything else you can tell us.

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