Why On-Premise Matters for Government AI
When an AI model processes citizen data, classified documents or national security information, that data must not leave the country's borders or pass through foreign infrastructure. Cloud AI services — even from reputable providers — involve data leaving your jurisdiction, being processed on foreign servers and potentially being subject to foreign law.
SynaptxCloud's On-Premise AI Platform deploys the full agentic AI stack — models, orchestration, memory, tool access, governance controls — entirely within your own data centres or government cloud accounts. Not a single token of citizen data ever reaches an external API.
Deployment Modes
| Mode | Data stays in-country | External connectivity | Model options | Suitable for |
|---|---|---|---|---|
| Cloud (gov. account) | ✓ | Limited | Open-weight | Most government agencies |
| On-Premise | ✓ | Internal only | Open-weight | Sensitive data / high-classification |
| Air-Gapped | ✓ | None | Open-weight | Classified / critical infrastructure |
Supported Open-Weight Models
Llama (Meta)
8B, 70B, 405B parameter variants. Strong all-round performance, widely deployed and thoroughly documented.
Mistral / Mixtral
7B–47B. European-origin models with strong multilingual support and efficient inference — a natural fit where EU data-protection alignment matters.
Falcon (TII UAE)
7B–180B. UAE-origin sovereign AI model. Particularly relevant for Gulf government deployments.
Custom Fine-Tuned
Deploy models fine-tuned on your government's own data and terminology — for domain-specific accuracy and compliance.
Frequently Asked Questions
SynaptxCloud's On-Premise AI Platform supports any open-weight model that can be self-hosted, including: Llama (Meta), Mistral and Mixtral (Mistral AI), Falcon (TII UAE) and DeepSeek. The platform is model-agnostic — you can swap models without changing your agent workflows. For classified deployments, we can also work with government fine-tuned proprietary models that have been cleared for on-premise use. Licence terms vary by model and are confirmed during deployment scoping.
Hardware requirements depend on model size and throughput needed: (1) Entry-level (7B–13B parameter models): 2–4 NVIDIA A100 or H100 GPUs — suitable for 10–50 concurrent users; (2) Mid-range (70B parameter models): 8–16 NVIDIA A100 or H100 GPUs — suitable for 50–200 concurrent users; (3) Large-scale (multiple 70B+ models): Custom GPU cluster — suitable for thousands of concurrent users. SynaptxCloud provides hardware sizing guidance as part of the deployment scoping process.
Yes. SynaptxCloud's On-Premise AI Platform can be deployed in fully air-gapped environments with no external network connectivity. In this mode: model weights are transferred physically (on encrypted storage media) and verified with cryptographic hashes; the platform operates entirely offline; updates are applied via verified patch packages distributed physically; and all integrations use internal APIs only. Air-gapped deployment is used by defence agencies, intelligence services and critical infrastructure operators.
Related Solutions & Terms
Sovereign AI that never leaves your borders
Talk to our team about deploying SynaptxCloud's agentic AI platform on your government's own infrastructure — cloud, on-premise or air-gapped.