Local AI Inference: Taking Back Control of Your Data

Every prompt you send to a cloud AI API leaves your perimeter. For a quick prototype that may be fine. For customer records, contracts, source code, or patient data, it is a compliance problem waiting to happen.

Local inference changes the equation: the model runs on hardware you control, inside your own network, and no prompt or response crosses a third-party boundary.

The hidden costs of cloud AI

The sticker price of a cloud API is only part of the picture.

• Data sovereignty. Sending personal data to an external processor has to be justified under Singapore's PDPA — and equivalent regimes elsewhere.
• Cost that scales with usage. API bills grow with every token, including the ones your users waste.
• Latency and availability. You inherit the provider's rate limits, outages, and roadmap.
• Lock-in. Prompts, fine-tunes, and workflows end up shaped around one vendor's API.

What local inference actually requires

Running a capable model on-premises is no longer exotic — but it is an engineering problem, not a shopping problem. The variables that matter are:

• Model size and quantisation — a quantised model can run in a fraction of the memory of its full-precision equivalent.
• Concurrency — how many users hit the model at once, and how long each response may take.
• Throughput vs. latency — batch serving and interactive chat are tuned differently.
• Total cost of ownership — the hardware is a fixed cost; there is no per-token meter.

The stack we deploy

Modern open-source inference engines make local deployment practical at enterprise scale:

• vLLM and SGLang for high-throughput, optimised large language model serving.
• Llama.cpp for highly efficient quantised CPU/GPU inference.
• MLX for optimised deployments on Apple Silicon.

Because the stack is open source, you are not tied to a single vendor — and a model can move in or out without renegotiating a contract.

Cost predictability

A properly sized AI rig is a one-time capital cost with a known performance envelope. Compare that with an API bill that grows every time usage grows. For steady, predictable workloads — internal assistants, document processing, code assistance — the economics usually favour owning the hardware.

Where Genworx fits

Genworx designs, sizes, and deploys local AI infrastructure end to end: choosing the right hardware for your models and budget, configuring the serving stack, and integrating it with your existing systems and identity. Talk to us to scope a deployment.