local llm setups
Your own AI. On your own hardware. Behind your own walls.
DigiMount designs, builds, and manages on-premise AI infrastructure that runs powerful open models 24/7, completely private, completely under your control. Your prompts, your documents, and your intellectual property never leave your environment and are never shared with a third party.
No cloud dependency. No per-token billing. No data leaving your premises.
Why run AI locally
Cloud AI is convenient, until the data you're feeding it is the data you can't afford to expose. A local LLM keeps the entire pipeline inside your perimeter.
Privacy by design.
Sensitive inputs and outputs stay on hardware you own. Nothing is sent to an external API, logged elsewhere, or used to train someone else's model.
IP protection.
Source code, contracts, research, customer records, and trade secrets never leave your network.
Compliance-friendly.
On-premise processing supports strict data-residency, confidentiality, and regulatory requirements where third-party data sharing isn't an option.
Always available.
Models run 24/7 with no rate limits, no usage caps, and no surprise per-token costs.
Predictable cost.
You own the hardware. Heavy, ongoing usage becomes a fixed asset instead of a metered bill that grows with adoption.
What we handle, end to end
You don't need an in-house ML team. We deliver a working, hardened, supported system.
Setup and deployment.
We specify and assemble the right hardware for your workload, install and tune the model-serving stack, and integrate it with your tools and internal applications.
Security hardening.
We lock down the deployment: network isolation, access controls, encryption at rest and in transit, audit logging, and a configuration reviewed against your compliance needs.
Ongoing management.
Optional managed service covers monitoring, updates, model swaps as better open models are released, performance tuning, and support, so the system keeps improving without burdening your team.
Want the same private-AI capability without managing infrastructure yourself? Our AI Operating Systems can run on top of a local LLM you own.
Hardware tiers
Three build classes, sized to the models you need to run and the number of people who'll use them. The model families below (Llama-, Mistral-, and Qwen-class open weights) are examples of what each tier comfortably runs, your exact models are chosen with you.
We don't publish list prices for hardware: GPU configuration, model size and support level move the number too much. Tell us your use case and you get a firm figure, quoted in USD or ZAR.
Essential
Private AI for a small team
Model scale~8–13B
Your first step into fully private AI. Ideal for individuals, founders, and small teams that want a confidential assistant and document intelligence without sending anything to the cloud.
- Hardware
- Single workstation-grade GPU (~24 GB VRAM class), quiet enough for an office.
- Models
- Llama-, Mistral-, and Qwen-class 7B–14B open models (quantised where useful).
- Typical use
- Private chat assistant, Q&A and search over your internal documents (RAG), drafting and summarising, day-to-day coding help for a small team.
Professional
Company-wide capability
Model scale~70B
Serious capability for a growing company or a busy department. Runs large, high-quality models and supports many users at once.
- Hardware
- Multi-GPU server (~96–160 GB total VRAM class), rack- or pedestal-mounted.
- Models
- Llama-, Mistral-, and Qwen-class ~70B open models at good speed, or several mid-size models served concurrently.
- Typical use
- Company-wide assistant, heavier document and knowledge retrieval, code generation, internal agents and automations, moderate concurrent usage across teams.
Enterprise
Maximum models, maximum scale
Model scale100B+
For organisations with strict compliance demands, high throughput, or a need to run the largest open models, and to run several of them at once.
- Hardware
- Rack-mounted multi-GPU node(s) (~320 GB+ total VRAM class, scalable across nodes).
- Models
- Flagship Llama-, Mistral-, and Qwen-class open weights, served to many users simultaneously, with capacity for fine-tuning.
- Typical use
- Regulated industries, high-concurrency deployment, multiple models and agents running side by side, custom fine-tuned models on proprietary data.
Not sure which tier fits? It comes down to two things: how large a model you need and how many people will use it at once. Tell us both and we'll recommend a build and confirm real pricing.
Privacy and security, by default
- Sensitive data never leaves your environment and is never shared with any third party.
- No external API calls required for inference: the model lives where you put it.
- Hardened configuration: network isolation, role-based access, encryption, and audit logging.
- You own the hardware, the models, and the data, with no vendor lock-in.
Bring your AI in-house: privately, securely, on your terms.
Tell us what you want to run and who needs to use it. We'll recommend the right build, harden it, and keep it running.