# Sovereign GPU Compute: dedicated, in-region GPUs for private AI > Dedicated, single-tenant GPU capacity in the region you choose (EU, UK, North America, Middle East, APAC or on-premises) to host, fine-tune and train your own models without public AI clouds. Source: https://aibyos.com/gpu Sovereign GPU Compute # Your models. Your region. _Your GPUs_. 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. [Request capacity](https://aibyos.com/contact) [Size your workload →](#sizing) **Single-tenant**no shared GPUs or storage **In-region**data never leaves your jurisdiction **H100 · H200 · B200**plus L40S and A100 classes **Managed**we set up and operate the stack 01 · Why sovereign compute ## When public AI clouds are not the right answer. Hosted LLM APIs are a great start. These are the four reasons our clients move their AI onto dedicated compute. ◈ ### Security & confidentiality Trade secrets, source code, patient or client data processed on hardware no one else shares, with a complete audit trail. ⚑ ### Data residency & regulation Keep compute, storage, logs and backups inside the EU, a specific country or your own building, for GDPR, sector rules and local data laws. € ### Cost at scale Per-token pricing grows with every request. Fixed capacity gets cheaper per request as usage grows, often the tipping point for high-volume workloads. ⚡ ### Performance & control Choose any open or licensed model, your own fine-tunes, and tune quantisation, batching and hardware for your latency targets. 02 · Regions ## Pick your jurisdiction. Capacity is provisioned in the region you choose. Exact locations and GPU availability are confirmed per request. ### European Union e.g. Poland, Germany, Netherlands, Nordics - Data processed and stored under EU jurisdiction (GDPR) - Supports EU AI Act documentation and financial / public-sector requirements - Nordic sites offer low-carbon power for long training runs Need a different country or location? [Ask us →](https://aibyos.com/contact) 03 · What you can run ## Host, fine-tune, train, research. ### Host models privately Serve open models (Llama, Qwen, Mistral, Gemma, DeepSeek and others) or your own fine-tunes behind an OpenAI-compatible API inside your network. ### Fine-tune on sensitive data LoRA, full fine-tuning and preference tuning on data that cannot leave your jurisdiction, with encrypted storage and verified wipe after the run. ### Train your own models Multi-node clusters with high-speed interconnect for training domain, speech and vision models from scratch. ### Research & experiments Burst capacity for distillation, evaluation and ablation studies, with experiment tracking and cost reporting per project. 04 · Deployment models ## Four ways to get capacity. Choose based on how steady your workload is and how strict your isolation requirements are. ### Dedicated private cluster ##### Choose it when - Steady, long-running inference or continuous training - Single-tenant hardware: no shared GPUs or storage - Best cost per GPU-hour at high, predictable utilisation ##### Watch out for - Monthly or longer commitment - Needs a capacity plan to avoid idle GPUs ### On-demand & burst capacity ##### Choose it when - Fine-tuning runs, experiments and training bursts - Days or weeks of capacity without long contracts - Scale up for a project, then release it ##### Watch out for - Top-tier GPUs are best reserved ahead of time - Plan data transfer in and out of the environment ### Managed private endpoint ##### Choose it when - Teams who want an API, not infrastructure - Your chosen open model, served privately with an OpenAI-compatible API - We handle scaling, updates and monitoring ##### Watch out for - Model choice limited to what you are licensed to run - Throughput sized to your agreed peak ### On-premises / air-gapped ##### Choose it when - Classified, defence, critical-infrastructure or strict-bank workloads - Data and models never leave your building - Reuse existing data-centre investment ##### Watch out for - Longest lead time: hardware procurement - Requires suitable power and cooling 05 · Sizing helper ## What will your workload need? Pick a workload for an indicative starting configuration. We size precisely after reviewing your model, traffic and latency targets. Hosting · indicative configuration ### 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. 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 →](https://aibyos.com/contact) All indicative configurations at a glance 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 ## Security controls, by default. Every environment is built to the same baseline. We document it for your security review, DPIA and auditors. **Single-tenant isolation** Dedicated nodes, networks and storage; no GPU sharing with other customers. **Data residency by design** Compute, storage, backups and logs stay in the region you choose. **Private connectivity** Site-to-site VPN or private interconnect; no public endpoints unless you want them. **Encryption everywhere** Encrypted disks and transport, with customer-managed keys available. **Identity & least privilege** SSO / MFA, role-based access and just-in-time admin access. **Full audit trail** Every access, job and model deployment logged and exportable to your SIEM. **Secure data lifecycle** Verified wipe of data and model weights at the end of an engagement. **Certified facilities** We select data-centre partners with recognised certifications such as ISO/IEC 27001. 07 · Comparison ## Hyperscaler AI APIs vs. sovereign GPU compute. 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 ## GPU compute questions. Do we need our own ML team to use this? 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. Which models can we run? 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. How fast can capacity be available? 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. Can we combine this with hosted APIs? 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. What happens to our data at the end? Data, model weights and logs are returned to you or securely wiped, and we confirm the wipe in writing.