Why This Talk Mattered
I recently had the opportunity to present my thoughts on LLMs, SLMs, and VLMs at the Microsoft office during a community event. This wasn’t just another AI talk filled with buzzwords and hype. The goal was simple but powerful: help students and professionals understand why not all AI models are built the same—and why that’s actually a good thing.
This blog is a written walkthrough of that presentation. I’ll be embedding the same slides I used and expanding on the thinking behind them—what I wanted the audience to feel, question, and take back with them.
Slide 1: Not All AI Models Are Built the Same — And That’s the Point
Key idea: AI diversity is a feature, not a flaw.
Most AI conversations start with the wrong question:
Which model is the best?
I wanted to flip that narrative early and replace it with a better one:
Which model fits the problem we are actually trying to solve?
That framing sets the foundation for everything that follows.
Slide 2: Who Am I and Why This Perspective Matters
Before diving into models, I briefly introduced myself and my background—working as an AI Data Scientist, building SaaS products, publishing research, and deploying production-grade AI systems.
This mattered because the talk wasn’t theoretical. It was grounded in real-world AI, where cost, latency, privacy, and infrastructure constraints are non-negotiable.
Slide 3: What Even Are LLMs?
Large Language Models (LLMs) are neural networks trained on massive datasets. They represent the most powerful and versatile AI systems available today.
In simple terms, LLMs:
- Contain billions to trillions of parameters
- Use transformer architectures with attention mechanisms
- Can reason, generate text, write code, translate languages, and analyze data
Examples include GPT-style models, Claude, Gemini, and LLaMA-based systems.
Slide 4: Why the AI Landscape Is Not One-Size-Fits-All
I used a smartphone analogy to make this intuitive.
Just like we have flagship phones, budget phones, and specialized devices, AI models exist for different needs.
- LLMs are the heavyweights
- SLMs are the efficiency experts
- VLMs are the multimodal specialists
Different tools exist because different problems demand different trade-offs.
Slide 5–6: What LLMs Can Do Really Well
LLMs are incredibly versatile. They can:
- Generate long-form content
- Perform complex reasoning
- Write and debug code across multiple languages
- Translate between dozens of languages
- Hold natural, human-like conversations
- Assist with deep research and analysis
This is where most of the AI hype comes from—and rightly so.
Slide 7: The LLM Trade-Offs No One Talks About Enough
All that power comes at a cost.
Running LLMs is like driving a Ferrari. It’s impressive, but not always practical.
Real-world limitations include:
- High computational requirements
- Expensive inference costs
- Higher latency
- Heavy cloud dependency
- Significant energy consumption
This is where many production systems start to struggle.
Slide 8: Enter SLMs — The Efficiency Experts
Small Language Models (SLMs) are often underestimated, but they are having their moment.
SLMs are:
- Smaller and more focused
- Optimized for specific tasks
- Fast and cost-efficient
- Capable of running on phones, laptops, and edge devices
They are designed for practicality, not bragging rights.
Slide 9: SLMs Are Not Weak, They Are Strategic
I highlighted several modern SLMs to make this concrete:
- Phi-3 (Microsoft)
- Gemini Nano
- Mistral 7B
- TinyLLaMA
These models prove that intelligence is not just about size—it’s about optimization.
Slide 10: When Should You Use SLMs?
SLMs shine in scenarios where:
- Speed matters
- Privacy is critical
- Offline capability is required
- Budgets are limited
- Edge deployment is necessary
- Tasks are domain-specific
SLMs are not budget LLMs—they are the right choice for the right job.
Slide 11: VLMs — When AI Learns to See
Vision-Language Models (VLMs) take things a step further.
They don’t just read text—they understand images as well. This is where AI becomes truly multimodal.
VLMs can:
- Process images and text together
- Understand visual context
- Answer questions about photos
- Generate descriptions from images
Slide 12: How VLMs Actually Work
Under the hood, VLMs combine:
- A vision encoder for images
- A language model for text
- A fusion layer to connect meaning across modalities
This allows AI systems to see and reason at the same time.
Slide 13: VLMs in the Real World
VLMs are already transforming industries such as:
- Medical imaging and diagnostics
- Autonomous vehicles
- Accessibility tools
- Visual search engines
- Content moderation
- AR and VR experiences
Multimodal AI is no longer optional—it’s becoming standard.
Slide 14: Comparing LLMs, SLMs, and VLMs
There is no single best model.
- LLMs excel at reasoning and versatility
- SLMs excel at efficiency and speed
- VLMs excel at multimodal understanding
The right choice depends entirely on context.
Slide 15: Speed and Latency Reality Check
Response time matters more than ever.
Approximate latency expectations:
- LLMs: 1–5 seconds
- SLMs: under 0.5 seconds
- VLMs: 2–8 seconds depending on image complexity
For real-time applications, speed is not optional.
Slide 16: Choosing the Right Tool
Choosing a model is like choosing a vehicle. A monster truck is overkill for city driving.
- Need complex reasoning → LLM
- Need speed and efficiency → SLM
- Need visual understanding → VLM
- Need offline capability → SLM
Final Takeaways
The most important lessons from this talk:
- There is no universally best AI model
- Context beats capability
- Efficiency matters as much as intelligence
- Hybrid systems often outperform single-model setups
Choose models like an engineer, not like a fan.
Closing Thoughts
Presenting this at the Microsoft office was special—not because of the venue, but because the audience asked implementation-focused questions, not hype-driven ones.
If you’re building AI systems today, understanding LLMs, SLMs, and VLMs isn’t optional—it’s foundational.
Let’s Continue the Conversation
If this resonated with you, feel free to connect with me on LinkedIn or reach out directly. I’d love to hear how you’re thinking about model selection in your own AI stack.
https://www.linkedin.com/in/jaskiratai
