Deploy and Run Large Language Models (LLMs) on Your Own Network

Organizations may want to deploy and run Large Language Models (LLMs) within their own secure network, without depending exclusively on public GenAI providers. 

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Why M247 Global?

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With M247 Global, it could be possible to leverage enterprise-class AI servers, preconfigured data center infrastructure, and modern orchestration frameworks (such as Retrieval-Augmented Generation – RAG, Ollama, and Model Context Protocol – MCP).

This may allow companies to build, deploy, and run LLMs in a private, secure, and scalable environment that meets their exact operational and compliance needs.

Who is this AI Infrastructure solution for?

Industries

  • Companies that want to train and use their own large language models.

  • Organizations with strict data privacy and regulatory requirements (finance, healthcare, public sector).

  • Enterprises needing high-performance AI infrastructure for NLP, generative AI, predictive analytics, or real-time decision-making.

  • Businesses looking to reduce costs by avoiding ongoing cloud API charges while maintaining control over model execution.

Roles

  • CTOs, CIO

  • IT Directors,

  • AI Engineers 

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Current business problems

 

Most companies face challenges when attempting to deploy LLMs internally:

  • Lack of specialized hardware (GPUs, TPUs) to handle AI workloads.
  • Absence of a software architecture tailored for LLM deployment (RAG, MCP, Ollama).
  • Integration knowledge gaps when connecting LLMs to business systems and external data sources.
  • Insufficient data center infrastructure for high availability and fault tolerance.
  • Bandwidth limitations for handling large-scale data traffic.

Without these capabilities, AI initiatives may remain at the prototype stage, unable to scale into production.

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How M247 Addresses the Challenge

Enterprise AI Servers

Preconfigured with NVIDIA GPU nodes and optimized for TensorFlow, PyTorch, and other ML libraries.

Custom Architecture Design

Based on RAG, Ollama, MCP, or other frameworks, tailored to each client’s workflows.

Deployment & Administration Services

Full support for server setup, software configuration, and ongoing monitoring.

Colocation & Data Center Hosting

Tier 3 compliant infrastructure ensuring redundancy, low-latency connectivity, and 24/7 expert support.

Key Features & Capabilities

NVIDIA GPUs

High-performance NVIDIA GPU AI servers (up to 10 GPUs per node).

Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) to integrate external knowledge into LLM responses.

Ollama

Ollama for simplified, local-first LLM deployment and management.

Model Context Protocol

Model Context Protocol (MCP) for seamless integration with APIs, databases, and enterprise applications.

Scalable compute clusters

Scalable compute clusters for training and inference at any scale.

Secure colocation services

Secure colocation services, with compliance-ready environments.

Expert engineering support

Expert engineering support for architecture design, deployment, and troubleshooting.

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How It Works (Implementation & Workflow)

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  1.  Provisioning: M247 deploys dedicated AI servers within its Tier 3 complaint data centers (+55 POPs globally)

  2. Configuration: The environment is preconfigured with ML frameworks (TensorFlow, PyTorch, MXNet) and optimized for GPU acceleration.

  3. Architecture Setup:
    • RAG enables the LLM to access and query knowledge bases.
    • Ollama manages local execution of LLMs, reducing dependency on cloud APIs.
    • MCP connects external systems (documents, APIs, databases) to enrich the LLM’s context.

  2. Integration: The LLM is connected to customer applications via APIs or direct integration.

  3. Operations: M247 engineers may provide 24/7 monitoring, scaling adjustments, and system administration.

Business benefits & outcomes

Running advanced AI applications often requires infrastructure far beyond traditional IT resources. With M247 Global’s AI servers, colocation services, and architectural expertise, it may be possible for enterprises to deploy, run, and scale their own LLMs—securely, cost-effectively, and without the limitations of cloud-only solutions


  • Cost Efficiency: Avoid ongoing cloud API expenses; optimize resources with subscription-based infrastructure.

  • Enhanced Privacy & Compliance: Keep sensitive data fully on-premises.

  • Performance & Scalability: GPU-powered AI servers accelerate training and inference for large-scale models.

  • Reduced Latency: Local execution ensures real-time responses for mission-critical applications.

  • Customization: Fine-tune and adapt LLMs for unique business needs without vendor lock-in.

  • Faster Time-to-Market: Preconfigured environments accelerate development and deployment cycles.

  • Operational Reliability: Complaint Tier 3 data center infrastructure ensures redundancy and 24/7 uptime

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