What does "running AI locally" actually mean?
It means the Large Language Model (LLM, the type of AI behind ChatGPT-style tools) runs on a computer or server you own, on your premises or in your own cloud tenancy, instead of a remote server operated by OpenAI, Anthropic or Google. Your prompts and documents are processed and stored on that machine only.
What does "air-gapped" actually mean?
Air-gapped means the machine has no network connection to the internet at all, not even indirectly. It is the strongest privacy posture available: there is no cable, no Wi-Fi and no cloud API for data to travel through, even by accident. Updates and new models are applied manually via controlled media. It suits the most sensitive environments, for example clinics, law firms and government units handling classified or highly confidential material.
Is local AI actually private, or does VYROX still see our data?
Once a system is delivered, your prompts, documents and outputs never pass through VYROX servers, only through the hardware installed at your premises. VYROX only sees what you choose to share during support or remote monitoring of system health, never document content.
Does local AI really cost less than ChatGPT or Claude in the long run?
Yes, for teams past a certain size and time horizon. Cloud AI is a recurring per-seat and per-token bill that never stops. A local build is a one-time hardware cost that then serves unlimited internal use for the price of electricity. Most VYROX builds break even against the subscriptions they replace within 6 to 14 months, then run for close to nothing.
What happens to our data if the internet goes down?
Nothing changes. A local AI keeps running with no internet connection at all, since the model and your documents already live on the machine in front of you. There is no dependency on an external API being reachable.
Is a local model as capable as GPT or Claude for real work?
For everyday business tasks, drafting, summarising, extraction, classification, internal search and coding help, current open-weight models such as Qwen3.6, Kimi K2.6, GLM-5.1 and DeepSeek V4 run locally at quality very close to the leading cloud models. VYROX also builds hybrid setups that keep private and high-volume work local and only call the cloud for the rare task that genuinely needs it.
How does PDPA compliance work with a local AI?
PDPA (Malaysia's Personal Data Protection Act 2010) expects you to control where personal data is processed and stored. A local or air-gapped deployment keeps that data on hardware you own, inside Malaysia if you choose, with role-based access and audit logging, supporting your PDPA obligations. VYROX aligns the build to these principles; formal compliance sign-off remains with your organisation and its auditor.
Who manages and updates the system after installation?
Every VYROX build ships with remote health monitoring, free model and runtime upgrades, and a same-business-day response SLA. It runs on standard, documented open-source tooling with your own IT team trained on it, so there is no vendor lock-in and no dependence on VYROX staying involved forever.
Can we start small and expand later?
Yes. Most engagements start with a single workstation or department pilot, prove the break-even math and the quality bar on real work, then scale to a team server or additional departments once the numbers are confirmed. Nothing about the local architecture forces an all-or-nothing rollout.
What if our current cloud AI spend is small, is local still worth it?
Not always immediately on cost alone, but privacy and offline reliability are independent of spend size. If your data is highly sensitive (patient records, privileged legal files, financials) the privacy case can justify a local build even before the pure cost math crosses over. The free audit gives you both numbers side by side so you decide with real figures, not guesses.
We already pay for a business cloud AI tier with training turned off. Is that not enough?
It is a real improvement over personal accounts, and for non-confidential work it may be sufficient. But training is a setting, not an architecture. Retention windows, sub-processors in the vendor's supply chain, and the vendor's choice of processing region all remain, and any of them can change with a change of terms. The honest test is what could still reach your data if every promise in the contract disappeared tomorrow. With an on-premise build the answer is nothing, because there is no route out.
How is this different from running a private AI instance in our own cloud tenancy?
A private cloud instance solves data residency and isolation, and it is a good fit if you already run cloud infrastructure well and your demand is spiky. What it does not do is remove the recurring bill, which becomes per hour the GPU instance runs rather than per seat, and it does not work offline, because it is still a network service. It also hands your team a full platform to operate. On-premise trades that recurring cost and dependency for a one-time build.
How much work is this for our IT team once it is installed?
Your team owns the physical basics, user accounts through your existing directory and single sign-on, and putting the documents and vector database into your normal backup routine. VYROX owns sizing, build and commissioning, remote health monitoring, and free model and runtime upgrades, with a same-business-day response SLA for faults. If someone can look after a file server, a switch and a directory, they have the skills, because it is standard documented open-source tooling rather than a proprietary appliance.
Does the remote health monitoring mean VYROX can see our documents?
No. Health monitoring reports whether the machine is up and healthy: uptime, disk, temperature and service status. It does not read prompts, documents or outputs. If diagnosing a problem would require seeing content, you decide what to share, case by case.
When would VYROX tell us not to buy a local build?
When the arithmetic does not work or the conditions are not there. A small team with light usage will stay cheaper on a subscription. Work that genuinely lives at the frontier of hard reasoning is better served by a hybrid that keeps confidential tasks local and calls the cloud for the rare exception. And if nobody can commit space, power and ownership of the hardware, a single workstation build is the better starting point over a team server nobody looks after.
