Local LLMs

Local LLM Deployment

Keep your data private with on-premise language models. We help organizations deploy and optimize open-source LLMs for production use cases without sending data to external providers.

Why Local LLMs?

Architecture Overview

flowchart TB
    subgraph Apps["Your Applications"]
        A[Web App]
        B[Mobile App]
        C[Internal Tools]
    end

    subgraph API["API Layer"]
        D[Load Balancer]
        E[OpenAI-Compatible API]
    end

    subgraph Inference["Inference Servers"]
        F[vLLM / TGI]
        G[Ollama]
        H[llama.cpp]
    end

    subgraph Models["Model Storage"]
        I[Model Registry]
        J[Quantized Models]
    end

    subgraph Hardware["Hardware"]
        K[NVIDIA GPUs]
        L[AMD GPUs]
        M[CPU Fallback]
    end

    A & B & C --> D
    D --> E
    E --> F & G & H
    F & G & H --> I & J
    F & G & H --> K & L & M

    style Apps fill:#e0f2fe,stroke:#0284c7
    style API fill:#fef3c7,stroke:#d97706
    style Inference fill:#dcfce7,stroke:#16a34a
    style Hardware fill:#f3e8ff,stroke:#9333ea

    

Supported Models

We deploy and optimize various open-source models, including the latest edge-optimized and reasoning models:

Deployment Options

On-Premise Servers

Datacenter GPUs

AI Workstations & Consumer GPUs

Private Cloud

• AWS EC2 instances (p4d, p5, g5)
• Azure NC-series VMs
• GCP Compute Engine with GPUs
• Air-gapped deployments for maximum security

Edge Deployment

• NVIDIA Jetson Orin for edge inference
• Quantized models for limited resources
• Mobile deployment with llama.cpp

Optimization Techniques

We optimize models for your hardware and requirements:

Inference Servers

Intelligent Model Routing

For production deployments with multiple models, consider adding an intelligent routing layer:

Fine-Tuning Services

Customize models for your domain:

Training Approaches

• LoRA: Low-rank adaptation for efficient fine-tuning
• QLoRA: Quantized LoRA for memory efficiency
• Full Fine-tuning: Maximum customization for large datasets
• Instruction Tuning: Improve instruction following
• Domain Adaptation: Specialize for your industry

What You Need

1. Training Data: Examples of desired behavior (100-10,000+ samples)
2. Evaluation Data: Test set for measuring improvement
3. Hardware: GPU cluster for training (we provide or use yours)
4. Iteration: Multiple rounds of training and evaluation

Implementation Process

1. Assessment: Evaluate your use cases, data sensitivity, and hardware options
2. Model Selection: Choose appropriate model(s) based on requirements
3. Infrastructure: Set up GPU servers and inference infrastructure
4. Optimization: Quantize and optimize for your hardware
5. Integration: Deploy API-compatible endpoint for your applications
6. Fine-tuning: Optional domain adaptation if needed
7. Monitoring: Implement logging, metrics, and alerting

Need private AI capabilities? Discuss your local LLM deployment with us.