Search for a command to run...
No comments yet. Be the first to comment.
Privacy isn't dead. It's just being rebuilt. The old internet promised us "move fast and break things." We moved fast. We broke things. Now we're realizing what we broke: privacy. Every LLM you use wa
Coding is changing faster than you think. Not just "AI helps with autocomplete." AI is becoming the primary developer. You become the reviewer. The Skill Inversion Old model (2020): Human writes code.
Single AI agents are limited. They make mistakes. They miss context. They take wrong paths. Multi-agent systems don't have these problems. They have different problems. But better ones. The Single Age
You've learned basic RAG. You embed documents. You retrieve them. You feed them to an LLM. It generates an answer. Then you deploy it. And it breaks. Because the real world isn't simple retrieval. Adv
Muhammad Zulqarnain | Full Stack AI Engineer & Geospatial Developer
16 posts
A blog by Muhammad Zulqarnain — Full Stack AI Engineer & Geospatial Developer based in Turku, Finland. I write about RAG systems, LLMs, Prompt Engineering, Next.js, TypeScript, and geospatial development. Practical insights, deep dives, and real-world AI solutions.
The biggest AI models aren't winning anymore.
GPT-4: 1.8 trillion parameters Claude 3 Opus: 200+ billion parameters Claude 3.5 Sonnet: Unknown but smaller Llama 3.1: 405 billion parameters PhiMoe (open source): 4 billion parameters
Guess which ones are being deployed at scale?
The small ones.
Large Model (GPT-4 level)
Training cost: $50-100M
Inference per 1M tokens: $5-15
Server hardware: $100K+ per GPU
Energy per inference: 10-50W
User needs to solve complex problems
Cost per user: High Deployment: API only (OpenAI's problem, not yours)
Small Model (4-7B parameters)
Training cost: $1-5M
Inference per 1M tokens: $0.01-0.10
Server hardware: $10-20K per GPU
Energy per inference: 0.5-2W
User needs 90% of the functionality
Cost per user: 100-1000x cheaper Deployment: Self-hosted, embedded, local
The math is obvious.
1. Fine-Tuning Works
Take Llama 3.1 8B parameter model. Fine-tune it on 1000 examples of your task. Cost: $100-500 in API calls. Performance: 95% as good as GPT-4 for YOUR specific task.
Fine-tune GPT-4? Can't. It's an API.
2. You Can Run It Locally
Small model on your laptop: 8GB GPU memory, runs at 50 tokens/second. Small model on your server: Free inference, full control, zero API costs.
GPT-4 on your laptop? Not possible.
3. Privacy That Matters
For healthcare, finance, legal: Data never leaves your infrastructure. Not possible with APIs.
4. Speed and Latency
Small model: 100ms latency Large model API: 500ms-2s latency (including network)
For real-time applications (chatbots, agents), latency kills the API approach.
5. Cost Scales Differently
1M requests with GPT-4: $15,000 1M requests with Llama 3.1 8B locally: $200
75x cheaper. For the same quality (for many tasks).
1. Quantization
Models normally: 32-bit floating point (expensive) Quantized: 4-bit integer (10x smaller, 5% accuracy loss)
Llama 3.1 70B normally: 140GB Quantized: 14GB (fits on single consumer GPU)
Tool: GPTQ, GGUF, BitsAndBytes
2. Pruning
Many neural network weights don't matter. Remove them: Model 20-30% smaller, 2-5% accuracy loss.
Example: Remove 30% of attention heads in transformer. Model still works.
3. Distillation
Small model learns from large model:
Large model (teacher) processes 100K examples
Small model (student) learns to predict teacher's outputs
Student gets 85-95% of teacher's performance
Student is 5-10x smaller
OpenAI, Anthropic, Meta all do this internally.
4. LoRA (Low-Rank Adaptation)
Don't fine-tune the whole model. Add small adapters (0.1% of parameters). Fine-tune only the adapters.
Full fine-tune: 40GB GPU memory LoRA fine-tune: 8GB GPU memory
Open Source (Small Model Champions)
Meta (Llama 3.1)
Mistral (Mixtral)
Microsoft (Phi)
Anthropic (Soon: smaller models)
Closed Source (Starting to Focus on Small)
OpenAI (Mini models)
Anthropic (Building Haiku variant)
Google (Gemini 2 Flash)
Why the shift?
They realized: Most users don't need GPT-4. They need 90% of GPT-4's capability for 10% of the cost.
2023 Model "Build the biggest model. Sell API access. Profit." Works for OpenAI because they have first-mover advantage.
2025 Model "Build diverse models:
Large (GPT-4 competitor): For complex reasoning
Medium (13B params): For good tasks at low cost
Small (7B params): For optimization, fine-tuning
Tiny (3B params): For embedding, classification
Let users pick and deploy however they want."
This is what Meta is doing with Llama 3.1. This is what Mistral is doing. This is what Anthropic will do.
Disappearing: "I wait for GPT-4 API responses" jobs
Exploding:
Fine-tune custom models for your domain
Deploy locally and at scale
Compress and optimize models
Integrate models into products
Build with model APIs + small models hybrid
1. 1B Parameter Models
Tiny models that do specific things really well. Run on phones. Run on IoT devices. Run on edges. Cost: Pennies per million tokens.
2. Mixture of Experts (MoE)
Model has 100 specialized experts. For your query, activate only 10. Get expert-level performance at generalist cost.
3. Custom Models As a Service
"Upload your data. We compress and fine-tune. Deploy instantly."
Like GitHub Copilot but for custom models.
4. Edge Models
Run Llama 7B on your phone. Run Phi 3B on your smartwatch.
Internet optional.
If you're building:
Start with small models
Fine-tune for your task
Deploy locally
Only use large model APIs if you hit the wall
99% of the time you won't need to.
If you're investing:
Small model infrastructure is the bet:
Quantization tools
Fine-tuning platforms
Deployment infrastructure
Optimization tools
If you're hiring:
Look for engineers who understand:
How to fine-tune small models
Quantization and compression
Local deployment
When to use which model size
For 50 years, bigger = better in computing.
"More compute, more memory, more data."
With AI, we're discovering: Smarter > bigger
A 7B parameter model, fine-tuned on your data, running locally, beats a 1.8T parameter model accessed via API.
For your use case.
That's the revolution.
Not in model scale. In model fit and efficiency.
The companies that realize this first win.ls Are Winning
