Service S.06 · Edge AI & SLMs · ON-PREMISE · AIR-GAPPED
We deploy open-weight small language models (Llama, Gemma, and similar) on your hardware: on-premise, at the edge, or fully air-gapped. Inference runs inside your network. Nothing goes out.
30 minutes · an engineer, not a salesperson
The problem · 01
Patient records, transaction histories, classified material, trade secrets. Sending them to a cloud API is a non-starter in security review, and a network round-trip is too slow for real-time work on a factory floor or in a vehicle.
The alternative is to bring the model to the data. Open-weight small language models now handle focused tasks such as document processing, extraction, and domain Q&A well enough to run production workloads on hardware you already own.
All inference happens inside your network boundary. There is no third-party API in the loop: nothing to redact, nothing to audit in transit.
No cloud round-trip. Response time is set by your hardware, not your internet connection, which is what real-time applications actually need.
No external dependencies once deployed. The system runs fully offline; updates arrive as packages you apply on your own schedule.
Quantized small models run on server CPUs and single GPUs. Most deployments fit the racks you already have.
No per-token fees. Once deployed, inference volume costs what your electricity and hardware cost: a fixed line item, not a variable one.
Data residency is a structural fact, not a vendor promise. That simplifies GDPR, HIPAA, and internal review conversations considerably.
Where it fits · 02
Regulated, latency-critical, or disconnected: these are the settings where on-premise models are the only serious option.
Process clinical notes, patient records, and imaging metadata without PHI ever leaving the covered environment.
Analyze transactions and sensitive customer data inside your secure perimeter, where your regulators expect it to stay.
Run models directly on the factory floor, where milliseconds matter and connectivity is never guaranteed.
Deploy in classified and disconnected environments where external network access is prohibited by policy.
Power in-store experiences and back-of-house tools without shipping customer data to the cloud.
Run compact models on edge devices and embedded systems for local processing where no backhaul exists.
Model selection · 03
We select from the current open-weight families, quantize for your compute, and benchmark against your real tasks before anything ships.
Model families evolve quickly. We re-evaluate the field at the start of every engagement, not once a year.
How it works · 04 STEPS
We handle model selection, optimization, and deployment engineering. You end up with a system your team operates.
We inventory your hardware, network topology, and compute headroom, then size the model and architecture to what you actually have.
We benchmark candidate open-weight models against your real tasks, then quantize and optimize the winner for your hardware.
Containerized install in your data center, private cloud, or edge locations, with orchestration, monitoring, and runbooks included.
Optional domain fine-tuning on your private data, performed in your environment, plus ongoing evaluation and offline update packages.
FAQ · 05
Get started · 30 MIN
A free 30-minute technical consultation: your goals, your constraints, and a straight answer on whether AI is worth it for your case.
Prefer email? info@euforic.io
No commitment. No deck. Just engineering.