Private and On-Device AI Development
Private and on-device AI: self-hosted open-weight LLMs, private RAG and models optimized for edge and embedded hardware, for data that cannot leave your environment.
Private AI keeps the model, the data and the logs inside an environment you control: your cloud account, your servers or the device itself.
It suits regulated data, data-residency rules, offline use and products where an API call per request would cost too much or take too long. The trade-off is that open-weight models are usually less capable than the largest hosted models, so every project starts with an evaluation set that shows whether a private model is good enough for the task.
Where Lightrains deploys private AI
Models in your cloud account
Open-weight models such as Llama, Mistral and Gemma served inside your AWS, Google Cloud or Azure account, behind your own network controls.
LLM integrationOn your own servers
Models, vector database and application deployed on your hardware, with no calls to external model APIs.
AI infrastructure and optimizationPrivate RAG
Retrieval over internal documents with a self-hosted vector database such as Qdrant or Milvus, with permissions carried through from your source systems.
RAG development servicesOn-device and edge
Models compressed to run on phones, wearables and embedded hardware, for offline use, real-time response and data that never leaves the device.
TinyML for wearablesChoosing where the model runs
Start with the least restrictive option your data allows. Each step towards self-hosting adds operating work, so it should be justified by a requirement.
| If | Consider |
|---|---|
| Data may go to a vendor under an enterprise agreement | A hosted API that does not train on your data |
| Data must stay in your cloud account or region | Open-weight models served in your cloud |
| Answers must come from internal documents | Private RAG with a self-hosted vector database |
| The device is offline or needs a real-time response | An on-device model |
| A stable domain, a strict format or tight latency | A smaller fine-tuned model |
What a private deployment needs
These are part of every private or on-device project. The linked pages explain how the Lightrains team implements them.
Model selection on your data
Open-weight candidates are compared with a hosted baseline on your own evaluation set, so the accuracy trade-off is known before you commit.
Quantization and distillation
Models are compressed to fit your GPUs, CPUs or device memory, and kept only if they still pass the evaluation set.
Fine-tuning where it helps
For a stable domain, a strict output format or tight latency, a smaller fine-tuned model can do the job of a larger general one.
Data isolation and access
Per-user or per-record scoping, encryption in transit and at rest, audit trails and configurable retention.
Serving and updates
Model serving sized to your hardware, with a tested path to roll out new model versions and re-run the evaluation set.
Documentation for security review
Where data goes, what is logged and who can access it, written down for your security and compliance teams.
Sensitive data, handled in isolation
HelixGuide: an assistant for genomic reports
Each report is processed in isolation, with encryption in transit and at rest, consent-aware workflows, per-session permissions, audit trails and configurable retention. Answers come only from the report and the lab’s approved content, with no web search and no memory across users.
Read the case studyHow a project runs
- 01
Step 1
Requirements and evaluation set
Agree where data may go, the hardware available and the quality bar, and collect real examples to test against.
- 02
Step 2
Model trials
Compare open-weight candidates, compressed variants and a hosted baseline on the evaluation set, with latency and hardware cost for each.
- 03
Step 3
Deploy and operate
Deploy into your environment with monitoring and audit logs, then keep running it or hand it to your team with documentation.
How much accuracy do we lose with a private model?
It depends on the task. Open-weight models are usually less capable than the largest hosted models, but on narrow tasks the gap is often small. The evaluation set measures it on your data before you commit.
Is a hosted API with an enterprise agreement private enough?
Often, yes. Enterprise API tiers from the major providers do not train on customer data by default. Self-hosting is for cases where data residency, regulation, offline use or per-call cost rule that out.
Which open-weight models do you use?
Llama, Mistral, Gemma and other open-weight families, chosen per task. The serving layer is built so the model can be replaced as better ones are released.
What hardware do we need?
That comes out of the model trials. You get the hardware requirement alongside the evaluation results, so the cost of running the model is known before deployment.
Can models run on phones and wearables?
Yes, for suitably small models. Quantization, pruning and distillation make models fit device memory and battery limits. Optimizing AI models for fitness trackers covers the constraints.
Who owns the code?
You do. All IP belongs to you, access to your systems is VPN-only under NDA, and payments are tied to milestones.
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