Please find the detailed article publushed @ github:
https://github.com/mazsola2k/genaiprompt/wiki/AI-Platforms-journey-in-2025-LLM%E2%80%90s—GenAI
What’s This About?
This article provides a practical, up-to-date overview of the rapidly evolving landscape of Generative AI (GenAI) and Large Language Models (LLMs) as of 2025. It explains the core concepts, technical bottlenecks, and hands-on approaches to leveraging leading AI platforms—both in the cloud and on-premises.
Key Takeaways for Readers
1. Clear Foundations
- GenAI & LLMs Explained:
Understand how GenAI (generative artificial intelligence) fits within machine learning, and how LLMs (like GPT-4, Llama-3/4, Mistral, Mixtral) are built and trained to generate human-like text, code, and content. - Model Sizes & Capabilities:
Bigger models (more parameters) handle complex tasks but demand more computing power.
2. Platform Landscape in 2025
- Cloud vs. On-Prem:
- Cloud APIs (OpenAI GPT, Google Gemini, Amazon Bedrock) offer easy access, scalability, and cutting-edge models but limit user fine-tuning and local control.
- On-Prem/Open Source (Llama, Mistral, Mixtral, Hugging Face, llama.cpp, Ollama) allow full user control, custom training, and privacy—if you have the hardware.
3. Technical Insights
- Resource Bottlenecks:
- On-Prem: GPU VRAM and system RAM limit the size of models you can run locally.
- Cloud: Users typically cannot fine-tune or retrain proprietary models—only providers can.
- Model Quantization & Formats:
Techniques like quantization and formats such as GGUF make it feasible to run advanced models on regular laptops and desktops, not just expensive servers.
4. Licensing & Usage
- Open vs. Proprietary:
- Open models have varying licenses, from highly permissive (MIT, Apache-2.0) to restrictive (Meta, DeepSeek).
- Proprietary models are only accessible as cloud services.
5. Hands-On Examples
- Practical How-To:
Get step-by-step, real-world scripts for running LLMs locally (Ollama, llama.cpp, Hugging Face) and sample chatbot code for Python. - Example Task:
See how to prompt an LLM to generate an Ansible script for deploying an Nginx container via Podman, demonstrating real utility.
6. Actionable Guidance
- Choosing a Platform:
- For easy access and production scaling: use cloud APIs.
- For customization, privacy, or cost-savings: run open models locally with tools like Ollama or llama.cpp.
- Next Steps:
Try open-source models, follow practical setup guides, and explore more on the referenced GitHub repository for deeper learning and sample code.
Read this article to:
- Demystify GenAI and LLMs in plain language.
- Compare major AI platforms and their trade-offs.
- Learn how to actually run and use modern GenAI models—whether on the cloud or your own laptop.
- Get inspired to experiment hands-on with open LLMs using the latest community tools.
https://github.com/mazsola2k/genaiprompt/wiki/AI-Platforms-journey-in-2025-LLM%E2%80%90s—GenAI
https://github.com/mazsola2k/genaiprompt
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