AI Services
Local LLM Deployment: Private AI on Your Own Infrastructure
We deploy open models such as Llama, DeepSeek and Mistral on your servers or private cloud, so you get capable AI while your data never leaves your infrastructure.
Overview
Why Local LLM Deployment matters
Many organizations want the productivity of generative AI but cannot send contracts, patient records, source code or financial data to a public API. Local LLM deployment solves that. We install and operate open-weight models such as Llama, DeepSeek and Mistral inside your data center, on dedicated servers or in a private cloud account you control. Prompts, documents and outputs stay within your network, and the model can keep working even without an internet connection.
We handle the full journey: choosing a model that matches your tasks and hardware, sizing GPUs, serving through Ollama or a production inference server, connecting it to your applications and fine-tuning it on your domain language when needed.
What we build
Local LLM Deployment: capabilities
Every feature is built on our standardized stack, so it is secure, tested and maintainable by any engineer in our pool.
On-premise and private cloud deployment
Run models on your own hardware, in a colocation rack or in a private cloud tenancy, with networking locked down to your environment.
Data stays inside your infrastructure
Prompts, retrieved documents and responses are processed and stored on systems you control, which simplifies data residency and confidentiality reviews.
Open model selection
Benchmark Llama, DeepSeek, Mistral and other open models on your real tasks to find the best balance of quality, speed and hardware cost.
Offline capability
Keep AI available in air-gapped sites, factories or field locations where internet access is restricted or unreliable.
Custom fine-tuning
Adapt a base model to your terminology, document formats and response style with parameter-efficient fine-tuning on your own data.
Private retrieval (RAG)
Index internal documents in a self-hosted vector store so the model answers from your knowledge without any of it leaving the network.
Access control and logging
Put the model behind authenticated APIs with role-based access, rate limits and audit logs, just like any other internal service.
Monitoring and upgrades
Track latency, throughput and GPU usage, and roll out newer model versions safely after testing them against your evaluation set.
Key benefits
Outcomes your business can count on
Confidential data stays confidential
Sensitive material is never sent to an external model provider, which makes AI adoption easier to approve for legal and security teams.
Predictable running costs
Instead of per-token fees that grow with usage, you pay for hardware and operations, which can be more economical at high volumes.
Independence from API providers
Price changes, rate limits or service outages at a third-party provider do not stop your internal AI tools from working.
Tailored to your domain
Fine-tuning and private retrieval let a smaller model perform well on your specific vocabulary and document types.
Use cases
Where this delivers value
Hospitals and clinics
Search clinical guidelines, summarize discharge notes and answer staff questions while patient information remains on hospital servers.
Banks and financial firms
Analyze loan files, policies and internal reports with an assistant that runs inside the regulated environment.
Manufacturing plants
Give shop-floor teams an offline assistant over machine manuals, SOPs and maintenance history at sites with limited connectivity.
Legal and compliance teams
Review contracts and regulatory documents without exposing privileged material to third-party services.
Software companies
Provide developers with a code assistant that works on proprietary repositories without sending source code outside the company.
Technology used
Built on one proven stack
We serve models with Ollama for simpler setups and production inference servers for higher throughput, packaged in Docker and orchestrated with Kubernetes where scale demands it. Applications reach the model through a secured internal API built on our standard backend stack.
- Ollama
- Llama
- DeepSeek
- Mistral
- LlamaIndex and LangChain
- Self-hosted Weaviate
- Python and FastAPI
- NestJS
- Docker and Kubernetes
- NVIDIA GPU servers
Our process
From first call to confident launch
- 01
Discovery
We list the tasks the model must handle, the data it will touch and your security and residency rules, then review existing hardware or hosting options.
- 02
Design
We shortlist models, size GPU and memory requirements, design network isolation and access control, and decide whether fine-tuning or retrieval is needed.
- 03
Agile Build
We stand up the inference stack, benchmark candidate models on your evaluation set, build the retrieval pipeline and connect the first internal application.
- 04
Production + 90-day hypercare
After load testing and a security review we go live, then monitor latency and quality, tune serving settings and plan model upgrades during 90 days of hypercare.
Private LLM on-premise versus cloud APIs
Hosted models from major providers remain the most capable option for some complex reasoning tasks, and they require no hardware. A private LLM on-premise trades a little of that peak capability for control: you decide where data goes, which version runs and how much each query costs. For many business tasks, such as summarizing, classifying, extracting fields and answering from internal documents, current open models perform very well.
Many clients choose a hybrid approach. Confidential workloads run on the local model, while non-sensitive tasks use a cloud API. We design the routing so applications do not need to know which model is answering, and you can shift the balance as open models improve.
Planning a local LLM deployment that performs
Hardware sizing is where most private AI projects go wrong. Model size, quantization, context length and the number of concurrent users all affect how many GPUs you need. We measure these on your actual workload before recommending a configuration, so you avoid both under-powered servers and expensive idle capacity.
Hosting can sit in your own data center or with infrastructure partners such as our group company Raidlayer, and our cloud hosting and DevOps team manages patching, backups and monitoring after launch.
- Model benchmarking on your own evaluation set
- GPU sizing based on measured throughput and concurrency
- Quantization to fit capable models on modest hardware
- Documented upgrade path as new open models are released
What you can build on a private model
Once the model is running, it becomes a shared internal service. Teams can build a private AI chatbot for staff, an AI knowledge base over policies and manuals, or document processing pipelines for contracts and forms, all reusing the same secure endpoint and access controls.
Industries served
Proven across industries
FAQ
Local LLM Deployment: frequently asked questions
Run AI on your own terms
Tell us about your data rules and the tasks you have in mind, and we will recommend models, hardware and a deployment plan for a private LLM.