Private AI Hosting that Keeps Your Data on Your Hardware
Run open-source LLMs, vision models, transcription and embeddings on a single-tenant bare-metal GPU server in our London datacenter. No prompts logged. No outputs scraped. No third party in the request path. Just your application, your model, and a GPU you control.
Why Self-Host Instead of Using a Hosted API
The cost-benefit shifts dramatically once you pass a few hundred pounds a month in API spend or have any data-residency obligation.
Public AI APIs (OpenAI, Anthropic, etc.)
Private AI on a GigaGPU dedicated server
What "Private" Actually Means Here
Everything you need to run production AI workloads on dedicated hardware in the UK.
Bare-metal hardware
You rent the whole physical machine. There is no hypervisor sharing the GPU with another tenant, no neighbour stealing PCIe bandwidth, no noisy-neighbour latency spikes.
Root from day one
Full root SSH from provisioning. Install your own kernel, your own CUDA toolkit, your own audit agent. We do not log into the box for "maintenance" without a written ticket.
UK data residency
Hosted in a Tier-3 London datacenter with 24/7 on-site staff. All data at rest stays inside the EU/UK perimeter unless you intentionally egress it.
No telemetry
We do not ship a vendor agent, we do not run a managed control plane, and we do not collect inference logs. The only out-of-band traffic is hardware health (IPMI) and bandwidth metering.
Encryption at rest
LUKS full-disk encryption is supported on request. BYOK with a remote unlock service of your choice — Vault, Keyper, or your own HSM.
DPA on file
Signed Data Processing Agreement and a one-page subprocessor list (datacenter operator, IP transit). Drop-in attachment for SOC 2 / ISO 27001 audits.
Open-Source Models We See Customers Run Privately
Anything that runs on Hugging Face runs here. The chart below shows the most common deployments and the GPU tier each one fits on.
| Model | Params | FP16 VRAM | INT4 VRAM | Recommended |
|---|---|---|---|---|
| Mistral 7B Instruct | 7B | 14 GB | 4.5 GB | RTX 3090 24 GB |
| LLaMA 3.1 8B Instruct | 8B | 16 GB | 5 GB | RTX 3090 24 GB |
| Qwen 2.5 14B | 14B | 28 GB | 9 GB | RTX 5090 32 GB |
| Gemma 2 27B | 27B | 54 GB | 16 GB | RTX 6000 Pro 96 GB |
| DeepSeek-V2 16B | 16B | 32 GB | 10 GB | RTX 5090 32 GB |
| Mixtral 8x7B | 47B (12.9B active) | 94 GB | 26 GB | RTX 6000 Pro / 2×5090 |
| LLaMA 3.1 70B | 70B | 140 GB | 40 GB | Multi-GPU cluster |
| Whisper Large-v3 | 1.5B | 6 GB | n/a | RTX 3050 6 GB |
| FLUX.1 dev | 12B | 24 GB | 12 GB | RTX 3090 / 5090 |
| nomic-embed-text | 137M | 1 GB | n/a | Any GPU |
Who Buys Private AI Hosting
Real customer workloads we run on this hardware every day.
Legal & Compliance Teams
Privileged contracts, M&A diligence, deposition transcripts. The text never leaves the firm’s perimeter — no "data shared with OpenAI" popup to defend in front of partners.
Healthcare & Pharma
Patient notes, clinical letters, trial data. Process under your existing HIPAA / Caldicott controls without renegotiating a BAA every time a vendor changes terms.
Financial Services
Trading desks running internal copilots, KYC pipelines pulling sensitive PII, FCA/PRA reporting workflows. Run inside your VLAN with no external egress.
Public Sector & Defence
UK OFFICIAL-SENSITIVE workloads, council data, regulated research. UK-resident datacenter, security-cleared on-site staff, hardware-only audit trail.
Universities & Research Labs
Student data under FERPA / DfE rules, unpublished research that mustn’t leak into a foundation-model training set, grant-funded RAG over institutional libraries.
AI Product Companies
Building a vertical SaaS where the differentiator is your fine-tuned model. Hosting it privately means a competitor can’t pull weights from a leaked API key.
How Private Deployment Works
From order to live inference in under 24 hours.
Order
Pick a GPU tier from RTX 3050 to RTX 6000 Pro 96 GB. Pay once, monthly, or annually.
Provision
We rack a clean bare-metal box, image Ubuntu 22.04 LTS, install NVIDIA drivers, hand you the root password.
Harden
Lock down SSH keys, enable LUKS, drop your audit agent, point your VPN at the new IP — all under your control.
Deploy
Pull your model from Hugging Face, start vLLM or Ollama, expose an OpenAI-compatible API on your VPN. Done.
Deep Dive
Why your prompts are not as private as you think
Every commercial AI provider — OpenAI, Anthropic, Google, Microsoft — operates a Trust & Safety pipeline. Even if their privacy policy says "we don’t train on your data", prompts and completions are buffered, scanned, and stored for 7–30 days for abuse review. That buffer is a US-resident copy of your business data, accessible to a small subset of vendor staff, vulnerable to legal compulsion in a jurisdiction outside the EU.
For most workloads, that’s an acceptable trade-off. For a contract-review tool processing privileged communications, a clinical scribe processing patient notes, or a defense contractor processing OFFICIAL-SENSITIVE briefings — it’s not. Dedicated GPU hosting removes that buffer entirely: your prompts hit a vLLM process running on a server you rent, generate a completion, and the completion goes back to your application. Nothing is logged. Nothing is shipped to a third party. The hardware is in a UK datacenter with a documented chain of custody.
The threat model we actually defend against
"Private" means different things to different buyers. We design against the four threats that show up in real procurement diligence:
- Vendor data exfiltration. No managed control plane, no agent calling home, no hidden "model improvement" flag. Your application talks to your GPU and stops.
- Multi-tenant leakage. Bare-metal physical isolation eliminates Spectre/Meltdown-class side channels and PCIe contention. The GPU’s HBM/GDDR is reset between tenants by reflash, not just a soft reboot.
- Lawful interception abroad. All inference happens in a single UK jurisdiction. No CLOUD Act compulsion, no Section 702 surveillance.
- Supply-chain & insider risk. On-site engineers are SC-clearable; remote access requires named-user MFA and is logged with timestamps you can audit.
For workloads that need an explicit answer to a security questionnaire, our private AI infrastructure guide spells out each control and the corresponding evidence we can produce.
Network & identity — the boring half of privacy
The interesting bit of private AI is the model. The expensive bit is the plumbing. By default we drop you on a public IPv4 with 1 Gbps unmetered transit, but most private deployments connect over WireGuard or IPsec back to your office or VPC. We can:
- Build a site-to-site VPN against your AWS / Azure / on-prem firewall
- Provision a private cross-connect into LON1 / LON2 if you have rack presence with us
- Disable the public IP entirely and serve the API over your VPN only
- Front the OpenAI-compatible endpoint with mTLS or Cloudflare Access
For identity, we expect you to bring your own. The server has no SSO, no SaaS dashboard, no managed user database. You SSH in with a key, you mount your secrets manager, you ship audit logs to your SIEM. We are a hardware provider — your application stack stays yours.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Is this actually private, or is there a vendor backdoor?
There is no managed agent, no remote-access daemon, no telemetry beacon. The only software we install is the OS, the NVIDIA driver, and whatever you ask for. After provisioning, we don’t SSH into the box without a written change request from you. We can produce a SHA-256 of every binary in the base image on request.
Where is the hardware physically?
Tier-3 datacenter in central London, with a hot-standby site in Slough. Both are inside the UK regulatory perimeter. We do not operate any datacenter outside the UK and EU.
Will you sign a DPA / BAA / security questionnaire?
Yes — standard DPA with SCCs is on file and ready to attach to your MSA. For healthcare, our reference architecture maps to the NHS DSP Toolkit. For US regulated workloads we sign a BAA on a per-tenant basis.
How do I migrate from an OpenAI-compatible API?
Run vLLM with –served-model-name set to whatever your client expects. Point your existing SDK at the new base URL. Most teams flip over in one afternoon. See our self-host LLM guide for the full walkthrough.
What about the GPU’s firmware and drivers?
We pin a specific NVIDIA driver version per server image, signed by NVIDIA. You can lock to that version with apt-mark hold. Firmware updates only happen on your maintenance window, never silently.
Can I run multiple models on one box?
Yes. A single RTX 5090 (32 GB) comfortably runs Mistral 7B + Whisper + a small embedding model concurrently. For larger fleets you can rent multi-GPU clusters and partition cards across services.
What is the maximum throughput?
Workload-dependent. As a rough anchor: 1× RTX 5090 serves around 1,200 tokens/sec aggregate on Mistral 7B FP16; 4× RTX 5090 in tensor parallel serves LLaMA 3 70B at ~150 tokens/sec. Our cost per million tokens breakdown has the full table.
How fast can you deploy?
Same-day for in-stock SKUs, <72 hours for out-of-stock cards. New cluster builds (4× or 8× GPU) typically ship in 5 working days.
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Bring your data home.
Most private AI deployments are live within 24 hours of order. Talk to a GPU specialist about your privacy and compliance requirements.
