x
loader
Contact UsContact Us
    ESS ENN Associates
    USA: +1 661 727 3766
    India: +91 97817 16363
    kc@essenn.associates
    Contact UsContact Us

    On-Premise AI Deployment

    Private AI Infrastructure for Regulated Industries

    shape
    On-Premise AI Deployment
    SINCE 1993
    Delivering intelligent software solutions globally
    ON-PREMISE AI

    Deploy AI Models on Your Own Infrastructure
    — Fully Private, Fully Controlled

    Not every organisation can send sensitive data to cloud AI APIs. Healthcare providers, legal firms, defence contractors, financial institutions, and enterprises in regulated industries need AI that runs entirely within their own infrastructure — with zero data egress, full compliance, and predictable costs.


    ESS ENN Associates designs and deploys on-premise AI systems using Ollama, vLLM, llama.cpp, LocalAI, and LM Studio. We handle hardware selection, model quantisation, inference optimisation, API gateway setup, and integration with your existing systems — delivering the power of state-of-the-art LLMs on your own servers, air-gapped networks, or edge devices.

    Request a QuoteRequest a Quote
    +1 661 727 3766
    KEY CAPABILITIES

    On-Premise AI Deployment
    Services We Deliver

    Local LLM Deployment with Ollama & vLLM

    Set up production-grade local inference servers using Ollama for ease of management and vLLM for high-throughput OpenAI-compatible APIs. Deploy Llama 3, Mistral, Gemma, Qwen, Phi-3, and DeepSeek models on your servers with automatic model management and GPU optimisation.

    Hardware Optimisation & Model Quantisation

    Run large models on available hardware through intelligent quantisation (GGUF, GPTQ, AWQ, EXL2). A 70B parameter model can run efficiently on dual-GPU servers. We optimise KV-cache, batch sizes, context lengths, and speculative decoding to maximise throughput-per-dollar on your hardware.

    Private AI API Gateway & Access Control

    Deploy an OpenAI-compatible API gateway (LiteLLM, LocalAI) on your infrastructure — so your existing applications connect to local models without code changes. Role-based access, usage monitoring, rate limiting, and audit logging for enterprise compliance.

    Air-Gapped & Secure Enclave Deployment

    For defence, intelligence, and maximum-security environments, we architect fully air-gapped AI deployments — models, weights, inference engines, and application stacks packaged for completely offline operation.

    Edge AI & Embedded Deployment

    Deploy compact, quantised models on edge hardware — NVIDIA Jetson, industrial PCs, Raspberry Pi clusters, and ruggedised devices — for real-time AI inference without cloud connectivity.

    Hybrid Cloud-Local AI Architecture

    Route AI requests intelligently — sensitive data to local models, non-sensitive workloads to cloud APIs for cost optimisation. Design tiered AI architectures with intelligent routing, caching, and fallback strategies.

    WHY ON-PREMISE AI

    The Business Case for
    Private AI Infrastructure

    Complete Data Sovereignty — No Cloud Vendor Risk
    HIPAA, GDPR, SOC 2 Compliance Without API Data-Sharing
    Zero Per-Token API Costs at High Usage Volumes
    Sub-100ms Inference Latency on Local Network
    Fine-Tuned Models That Never Leave Your Infrastructure
    No Service Interruption from Cloud Provider Outages
    Full Control Over Model Versions and Updates
    Proprietary AI Capabilities Competitors Cannot Access
    shape
    shape
    FAQ

    Frequently Asked Questions

    Everything you need to know about on-premise AI deployment.

    • Q: What hardware do I need to run LLMs on-premise?
      A: Hardware requirements depend on the model size and performance requirements. A single NVIDIA RTX 4090 (24GB VRAM) can run quantised 7B–13B models at good speeds for low to moderate throughput. For 70B models, two RTX 4090s or an A100 40GB are more appropriate. Enterprise-grade deployments typically use NVIDIA A10G, A100, or H100 GPUs. We also support CPU-only deployment (using llama.cpp) for organisations without GPU infrastructure — though this is slower and best for lighter workloads. We provide a hardware sizing consultation as part of our scoping process to match your use case, concurrency requirements, and budget.
    • Q: Which open-source models perform closest to GPT-4?
      A: The open-source LLM landscape has advanced dramatically. As of 2025, models like Llama 3.3 70B, DeepSeek-V3, Qwen 2.5 72B, and Mistral Large approach GPT-4-class performance on many benchmarks, particularly for code generation, reasoning, and instruction following. For specialised tasks, fine-tuned variants often outperform general-purpose commercial models. The right model depends on your specific tasks — we conduct benchmark testing on representative samples of your actual workload to identify the best model before deployment, rather than relying on generic leaderboard rankings.
    • Q: How do you handle model updates and security patches?
      A: We design on-premise deployments with versioned model management from the start. Using tools like Ollama's model library or a private model registry, updates can be pulled and tested in a staging environment before production rollout. For air-gapped deployments, we provide model packages delivered via approved transfer mechanisms. Security patching covers the inference engine (Ollama, vLLM), API gateway (LiteLLM, LocalAI), operating system, and GPU drivers. We can provide managed update services or training for your team to handle updates independently.
    • Q: Can on-premise AI integrate with our existing software systems?
      A: Yes — we deploy OpenAI-compatible API endpoints, which means any application built to use the OpenAI SDK, LangChain, LlamaIndex, or similar frameworks can switch to your local model with a single configuration change. This covers CRMs, document management systems, chatbot platforms, custom business applications, and development tools. We also build custom integrations for proprietary internal systems, legacy software, and enterprise platforms (SharePoint, ServiceNow, SAP, Salesforce) that require bespoke connectors. Your team will be fully briefed on the integration architecture.
    • Q: What is the total cost comparison between on-premise and cloud AI APIs?
      A: The break-even point between on-premise and cloud API costs typically occurs at moderate to high usage volumes. At low usage (under a few million tokens/month), cloud APIs are more economical due to low upfront costs. As usage grows, on-premise becomes dramatically cheaper — a single GPU server costing $10,000–$20,000 can pay for itself in 3–6 months compared to equivalent cloud API costs at high volumes. We provide a detailed ROI analysis during scoping, comparing your projected token consumption against hardware capex, opex (power, cooling, maintenance), and engineering setup costs, giving you a clear break-even timeline.

    Your Data. Your Models. Your Infrastructure.

    Stop sending sensitive business data to cloud AI providers. ESS ENN Associates will design and deploy a private AI infrastructure that meets your compliance requirements, performance targets, and budget.

    Request a QuoteRequest a Quote
    career promotion
    career
    growth
    innovation
    work-life-balance