Full control with local LLM deployment
Take complete control of your AI infrastructure with locally deployed large language models. We help you deploy, optimise and manage LLMs on your own servers for stronger privacy, lower costs and unlimited scalability.
What we offer
Local LLM setup and configuration
End-to-end solutions for on-premise AI deployment:
- Model selection: choosing the right open-source models for your use case.
- Hardware optimisation: configuring servers for optimal LLM performance.
- Quantisation: reducing model size while preserving quality.
- Multi-GPU setup: distributing models across several GPUs.
Custom model fine-tuning
Adapting models to your specific domain:
- Fine-tuning on your proprietary data.
- Integrating domain-specific knowledge.
- Custom instruction tuning.
- Performance optimisation for your use cases.
Infrastructure management
Reliable infrastructure for production deployments:
- Load balancing and scaling.
- High-availability configurations.
- Disaster recovery and backups.
- Resource monitoring and optimisation.
Integration services
Connecting local LLMs to your applications:
- RESTful API development.
- SDKs for multiple languages.
- Authentication and rate limiting.
- Caching and performance optimisation.
Key benefits
Data privacy Keep sensitive data inside your infrastructure. No data goes to external APIs — full control and compliance.
Cost efficiency Eliminate token costs. Pay only for infrastructure and achieve significant savings at scale.
Customisation Fine-tune models on your data without limits. Build genuinely specialised AI for your domain.
Performance control Optimise latency and throughput to your requirements, with no external dependencies.
Independence No vendor lock-in. Full control over model versions, updates and deployment strategies.
Technologies we use
- Models: Llama 3, Mistral, Mixtral, Phi-3, Qwen.
- Inference engines: vLLM, TGI, Ollama, LM Studio.
- Frameworks: PyTorch, Transformers, PEFT, LoRA.
- Quantisation: GPTQ, AWQ, GGUF.
- Deployment: Docker, Kubernetes, Ray Serve.
Use cases
- Healthcare and medicine: HIPAA-compliant patient data analysis, medical documentation processing, clinical decision support and research data analysis.
- Legal services: contract analysis and review, legal document generation, case research assistance and compliance checks.
- Financial services: secure financial analysis, risk scoring, regulatory compliance and internal knowledge management.
- Manufacturing: quality control analysis, production optimisation, technical documentation and supply-chain analytics.
How we work
- Assessment and planning: evaluate your requirements, analyse hardware capabilities, select suitable models and define success metrics.
- Infrastructure setup: configure servers and GPUs, install inference engines, set up monitoring and implement security measures.
- Model deployment: deploy selected models, optimise performance, fine-tune where needed and validate outputs.
- Integration and testing: develop APIs and SDKs, integrate with your applications, run load testing and perform a security audit.
- Training and handover: team training, documentation, ongoing support setup and knowledge transfer.
Hardware guidance
Small scale (< 13B parameters)
- GPU: NVIDIA RTX 4090 or A5000
- VRAM: 24GB+
- RAM: 64GB
- Storage: 500GB NVMe SSD
Medium scale (13B–70B parameters)
- GPU: NVIDIA A100 40GB or multiple RTX 4090
- VRAM: 80GB+ (distributed)
- RAM: 128GB+
- Storage: 1TB NVMe SSD
Large scale (70B+ parameters)
- GPU: Multiple NVIDIA A100 80GB
- VRAM: 160GB+ (distributed)
- RAM: 256GB+
- Storage: 2TB+ NVMe SSD
Ready to deploy your own LLMs? Get in touch to discuss your infrastructure needs and see how local LLM deployment can benefit your organisation.