Run capable LLMs on your own hardware — nothing leaves the building
Private LLM Deployment
Private LLM deployment is the engineering of self-hosted large language models on infrastructure you own and control — bare-metal or in-VPC — so prompts, documents and outputs never leave your perimeter. AI Pinnacle sizes the hardware, deploys open-weight models (Gemma, Llama-class) via Ollama and vLLM, and hands over a private, authenticated inference endpoint for air-gapped and data-residency-bound environments.
What you get
- Hardware sizing spec for your target model, concurrency, and latency
- Bare-metal Ubuntu (or in-VPC) provisioning and hardening
- Model serving via Ollama / vLLM with quantization tuned to your GPUs
- Private inference gateway (FastAPI) with auth, rate limits, and logging
- Air-gapped / offline install where required (staged weights + dependencies)
- Observability, runbooks, and a model-upgrade path your team can operate
Why deploy LLMs on your own hardware instead of an API?
Because for some data, the cloud is not an option — legal, defence, healthcare and government workloads where confidentiality, data residency or air-gap rules forbid sending data to a hosted API. Self-hosting keeps every prompt and output inside your network, and once the hardware is bought, inference runs at electricity cost instead of per-token metering.
What does self-hosted LLM infrastructure cost in 2026?
Two line items: hardware (passed through at cost) and engineering. An 8B-class model serves well on a single modern GPU workstation (hardware roughly USD 3K–9K); 70B-class serving typically needs multi-GPU or a data-centre card and runs USD 15K–45K+ in hardware. Our engineering to spec, deploy, secure and hand over the stack runs USD 12K–35K depending on air-gap and integration requirements.
Self-hosted vs API: when does on-prem pay back?
Below roughly a few million tokens a day, hosted APIs are usually cheaper all-in. Above that, or under any hard privacy/residency constraint, on-prem wins — you stop paying per token and you stop exporting data. We model the break-even for your actual volume before you commit to hardware.
Which models can run locally, and how good are they?
Open-weight models — Gemma, Llama-class, Qwen, Mistral — now cover most enterprise tasks (RAG, extraction, drafting, classification) at quality that clears production bars, especially fine-tuned on your domain. We benchmark candidate models on your real prompts and pick per-task, rather than assuming the biggest model is required.
Engagement tiers & pricing
Hardware is passed through at cost — you own it. Engineering is fixed-price against a written spec, with the self-hosted-vs-API break-even modeled for your volume before you buy a single GPU.
On-Prem Pilot
USD 12K–20K + hardware
3–5 weeks
- Single-node deployment
- One 8B–13B-class model
- Private inference endpoint
- Auth + basic observability
Production Private LLM
USD 22K–35K + hardware
6–10 weeks
- Multi-GPU / 70B-class serving via vLLM
- RAG over your private data
- Air-gapped install option
- Monitoring + runbooks
- Model-upgrade path
Sovereign / Air-Gapped
Custom
8–14 weeks
- Fully offline deployment
- HA + failover
- SSO + audit logging
- Compliance documentation (GDPR/HIPAA)
- On-site handover + training
Hosted API vs self-hosted LLM for enterprise
| Criterion | Self-hosted (on-prem/VPC) | Hosted API |
|---|---|---|
| Data egress | None — stays in your perimeter | Prompts + outputs leave your network |
| Air-gap / sovereignty | Supported | Not possible |
| Cost model | Fixed hardware + electricity | Per token, scales with usage |
| Best fit | Regulated data, high volume, residency rules | Low volume, non-sensitive, fastest start |
| Model choice | Any open-weight model, fine-tunable | Vendor's catalogue |
Frequently asked questions
Can the deployment run fully air-gapped?
Yes. Weights, dependencies and the serving stack are staged for offline install, and the inference gateway runs with no outbound internet dependency — suitable for defence, government and confidentiality-bound environments.
What hardware do we need to run a local LLM?
It depends on the target model and concurrency. An 8B-class model runs on a single modern GPU; 70B-class serving needs more VRAM and usually vLLM for throughput. We produce the exact spec from your quality, latency and volume targets before anything is purchased.
Which models do you deploy?
Open-weight models — Gemma, Llama-class, Qwen, Mistral — served via Ollama or vLLM. We benchmark candidates on your real prompts and pick per task rather than defaulting to the largest model.
Do you also handle RAG and applications on top of the local model?
Yes. The private endpoint behaves like a cloud API to your applications, so we can build RAG, agents and integrations against it — all inside your perimeter. This pairs with our LLM Integration and AI Agent Development services.
Who owns the infrastructure after handover?
You do — the hardware, the models, and the deployment. We hand over runbooks, an upgrade path, and training so your team operates it, with optional retainers for maintenance and model upgrades.
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