Generative AI vs Agentic AI Understanding the Future of Private AI Workloads

The AI world is evolving at breakneck speed, and if you've been following the conversation lately, you've probably heard terms like Generative AI and Agentic AI thrown around a lot. These aren't just buzzwords—they represent two fundamental shifts in how artificial intelligence is being developed and applied.

In this blog, I want to unpack what these terms really mean, how they differ, and more importantly, what it takes to run them on-premises as a private AI stack. This is especially important for organizations dealing with sensitive data, regulatory environments, or those simply looking to reduce cloud dependency and take full control of their AI infrastructure.

Generative AI: The Creative Mind

Generative AI refers to models that can generate new content—text, images, code, videos, even music—based on patterns they’ve learned from large datasets. Think ChatGPT writing an article, DALL· E creating original artwork, or GitHub Copilot suggesting code snippets.

TAt its core, Generative AI relies on foundation models like:

• GPT (text generation)
• Stable Diffusion (image generation)
• LLaMA, Mistral, Falcon, etc. (open-source LLMs)
• MusicGen, CodeGen, and others for niche tasks

These models work through deep learning techniques, especially transformers, which are trained on massive volumes of data and require significant computational power—both during training and even during inference (when generating results).

Agentic AI: The Autonomous Executor

Agentic AI is a step beyond generative AI. It doesn’t just generate outputs—it takes action.

• Imagine an AI agent that can:
• Browse a website, analyze information, and summarize it.
• Take a business goal, break it into sub-tasks, and use APIs or tools to execute them.
• Act as your virtual employee, managing emails, preparing reports, or coordinating tasks.
  
  

Agentic AI combines:

• A reasoning engine (usually a large language model)
• Memory (to keep track of context)
• Tool usage (code execution, web browsing, database queries)
• Planning modules (to decide what to do next)
  
  

In simple terms, Generative AI is your intelligent writer or designer. Agentic AI is your virtual executive assistant.

Why Enterprises Are Prioritizing Private AI

Having spoken with numerous CIOs, IT leaders, and CTOs across public sector organizations, large Indian enterprises, and BFSI players, a common pattern has emerged. There's growing enthusiasm for AI, but also an equally strong concern around data governance and sovereignty.

Many organizations want to harness the power of GenAI but:

• Cannot send sensitive datasets to the cloud.
• Need full control over how models behave.
• Want to fine-tune models on proprietary knowledge without risking IP leakage.

This is leading to a serious push for on-premises AI deployments, often bundled under what I call “Private AI”—a secure, scalable, and compliant approach to GenAI and agent-based workflows, built within your own infrastructure.

What Kind of Infrastructure is Needed?

Let’s talk hardware. Running powerful AI models locally is not the same as spinning up a basic server. Here's what you need to consider:

1. Compute: GPU is King

For both training (if you plan to) and inference (model execution), GPUs are essential.

• NVIDIA A100 / H100 / L40S / RTX 6000 Ada – Ideal for high-performance inferencing and fine-tuning.
• AMD Instinct MI300 series – An emerging alternative with solid AI performance.
• For lighter workloads or small models, even NVIDIA A10 or A30 can work.
  
  

2. Memory

• Generative models like LLaMA 2 (7B) need 16–32 GB VRAM per instance.
• Larger models (13B, 65B) need upwards of 80–120 GB VRAM, or distributed GPUs.
• System RAM should be in the 256 GB+ range for agentic pipelines with persistent memory and context handling.
  
  

3. Storage

AI workloads need fast, scalable storage:

• NVMe SSDs for hot data (models, tokens)
• SAS/SATA HDDs or storage arrays (like HexaData!) for long-term storage

Depending on usage, plan for multiple TBs of capacity.

4. Networking

For multi-GPU or cluster setups, high-speed interconnects are a must:

• NVIDIA NVLink, InfiniBand, or at least 25/40/100GbE

AI models often serve apps via REST APIs—so reliable Layer 3/4 networking is critical.

5. Orchestration & Software

Running AI stacks isn’t just about hardware. You’ll need:

• Docker or Kubernetes for containerized model serving.
• LLM serving frameworks: Text Generation WebUI, vLLM, Hugging Face Inference, FastChat
• Agent frameworks: LangChain, AutoGen, CrewAI
• Fine-tuning tools: LoRA, QLoRA, DeepSpeed, Hugging Face PEFT
  

We’ve already seen several enterprises experimenting with pilot use cases using internal infrastructure—especially using open-source models they can fine-tune securely. Our own work at Esconet, building high-performance, GPU-rich servers and storage under the HexaData brand, is already aligned to support these shifts.

Use Cases We’re Seeing

• Internal Chatbots for HR, IT Helpdesk, and Knowledge Management
• AI Copilots for software teams (code suggestions, bug fixes)
• Autonomous Agents to handle tickets, CRM updates, or data entry
• AI Document Search across private file systems (RAG - Retrieval Augmented Generation)
• Private Art/Media Generation Studios for branding teams
  

Each of these can be powered either by a generative model alone or an agentic setup with planning, task execution, and feedback loops.

Final Thoughts: AI is Not Just a Tool - It’s an Ecosystem

Both generative and agentic AI are transforming the way we work, automate, and create. But to truly unlock their potential - especially in a secure, customizable, enterprise-ready manner - organizations need to look beyond SaaS tools and think on-premises, private AI infrastructure.

After engaging with multiple stakeholders across industries from CIOs in government organizations to datacentre architects in private enterprises - one thing is clear: the future of enterprise AI is private, performant, and purpose-built.

If you're considering building your own AI stack, think of it as building your own data brain trained on your knowledge, run on your hardware, and aligned to your mission. And that’s where we at Esconet and our HexaData platform are uniquely positioned to help with GPGPU servers, high-throughput storage, and AI consulting tailored for real-world workloads.

Let’s not just consume the future, let’s build it, intelligently and privately.