Sovereign GPU Compute
Dedicated, single-tenant GPU capacity in the jurisdiction you choose, to host, fine-tune or train your own models when hyperscaler-hosted AI isn’t an option for security, compliance, cost or performance reasons.
01 · Why sovereign compute
Hosted LLM APIs are a great start. These are the four reasons our clients move their AI onto dedicated compute.
Trade secrets, source code, patient or client data processed on hardware no one else shares, with a complete audit trail.
Keep compute, storage, logs and backups inside the EU, a specific country or your own building, for GDPR, sector rules and local data laws.
Per-token pricing grows with every request. Fixed capacity gets cheaper per request as usage grows, often the tipping point for high-volume workloads.
Choose any open or licensed model, your own fine-tunes, and tune quantisation, batching and hardware for your latency targets.
02 · Regions
Capacity is provisioned in the region you choose. Exact locations and GPU availability are confirmed per request.
e.g. Poland, Germany, Netherlands, Nordics
Need a different country or location? Ask us →
03 · What you can run
Serve open models (Llama, Qwen, Mistral, Gemma, DeepSeek and others) or your own fine-tunes behind an OpenAI-compatible API inside your network.
LoRA, full fine-tuning and preference tuning on data that cannot leave your jurisdiction, with encrypted storage and verified wipe after the run.
Multi-node clusters with high-speed interconnect for training domain, speech and vision models from scratch.
Burst capacity for distillation, evaluation and ablation studies, with experiment tracking and cost reporting per project.
04 · Deployment models
Choose based on how steady your workload is and how strict your isolation requirements are.
05 · Sizing helper
Pick a workload for an indicative starting configuration. We size precisely after reviewing your model, traffic and latency targets.
Hosting · indicative configuration
▸ 1× L40S or 1× A100 / H100 80GB
Chat, RAG, extraction and classification. Quantised (FP8 / INT4) for higher throughput; add replicas to scale.
Indicative only. Exact sizing depends on model, context length, concurrency, latency targets and dataset size.
Workload not listed? Describe it and we will size it →
| Workload | Indicative GPUs | Notes |
|---|---|---|
| Serve a 7–14B model | 1× L40S or 1× A100 / H100 80GB | Chat, RAG, extraction and classification. Quantised (FP8 / INT4) for higher throughput; add replicas to scale. |
| Serve a 70B-class model | 2–4× H100 / H200 | Tensor-parallel serving with vLLM or TensorRT-LLM. FP8 keeps quality high while halving memory. |
| Serve a very large / MoE model (400B+) | 1–2 nodes of 8× H200 or B200 | For frontier-class open models. High-bandwidth interconnect needed between GPUs. |
| LoRA / QLoRA fine-tune (7–14B) | 1–2× A100 / H100 80GB | Most task-specific fine-tunes. Typically hours to a day per run, depending on dataset size. |
| Full fine-tune of a 70B model | 2–8 nodes of 8× H100 / H200 with InfiniBand | Full-parameter or preference tuning at scale, with sharded training (FSDP / DeepSpeed). |
| Train a small model from scratch (≤1B) | 1–4 nodes of 8× H100 | Domain language models, speech or time-series models. Days to weeks depending on data. |
| Speech: ASR / TTS training & serving | 1–4× L40S or A100 | Whisper-class fine-tuning, custom voices and real-time streaming inference. |
| Vision / diffusion fine-tuning | 1–2× L40S or H100 | LoRA fine-tunes for image generation, VLM and detection models. |
06 · Information security
Every environment is built to the same baseline. We document it for your security review, DPIA and auditors.
Dedicated nodes, networks and storage; no GPU sharing with other customers.
Compute, storage, backups and logs stay in the region you choose.
Site-to-site VPN or private interconnect; no public endpoints unless you want them.
Encrypted disks and transport, with customer-managed keys available.
SSO / MFA, role-based access and just-in-time admin access.
Every access, job and model deployment logged and exportable to your SIEM.
Verified wipe of data and model weights at the end of an engagement.
We select data-centre partners with recognised certifications such as ISO/IEC 27001.
07 · Comparison
Both have their place; many clients use both, routed through one AI gateway.
| Hyperscaler-hosted AI APIs | Sovereign GPU with AIBYOS | |
|---|---|---|
| Where data is processed | Provider’s shared platform, region options vary | Dedicated hardware in the region or building you choose |
| Model choice | Provider’s catalogue | Any open or licensed model, including your fine-tunes |
| Cost at high, steady volume | Pay per token; grows linearly | Fixed capacity; cost per request falls as usage grows |
| Performance tuning | Limited to provider settings | Full control: quantisation, batching, caching, hardware |
| Isolation | Logical, multi-tenant | Physical, single-tenant |
| Best for | Fast start, variable or low volume | Sensitive data, high volume, custom models |
08 · FAQ
No. We set up the serving or training stack, deploy your chosen models and operate them. Your team gets an API or a ready-to-use training environment.
Any open-weight model whose licence fits your use (for example Llama, Qwen, Mistral, Gemma, DeepSeek), your own fine-tunes, and speech and vision models. We help you check licence terms.
On-demand capacity for common GPU types can often be arranged within days; large dedicated clusters and on-premises builds take longer. We confirm lead times per region when you request capacity.
Yes. A common pattern is an AI gateway that sends sensitive or high-volume traffic to your private models and everything else to hosted APIs.
Data, model weights and logs are returned to you or securely wiped, and we confirm the wipe in writing.
Next step
A free 30-minute call with an Engagement Lead or AI Architect. You'll leave with a clearer view of options, risks and cost, whether or not we work together. Your case doesn't need to fit any box on this site; just tell us what you're facing.
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