LLM Optimization Techniques You MUST Know for Faster, Cheaper AI [TOP 10 TECHNIQUES]

Описание: 🎯 Want to land a top ML role at FAANG companies like Meta or Google?
This ultimate system design guide covers everything you need to ace your machine learning system design interview — from deploying large language models to optimizing inference and cutting real-world costs.

⏳ TIMESTAMPS:
[00:00] Introduction 🚀
[01:00] Inference Inefficiencies 🤖
[01:30] How LLMs Work 🏗️
[03:35] Attention Mechanism 📊
[04:40] Optimization Techniques ⚙️
[12:10] Extra Techniques 🌍
[12:45] Wrap-Up 🎯

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🚀 *What You'll Learn in This Video:*

✅ *Top 10 LLM Optimization Techniques* for 2025
✅ Reduce inference costs by up to *90%*
✅ Accelerate LLM deployment using production-ready tools
✅ Build systems like OpenAI, Meta, and Google DeepMind

💡 Techniques covered:

Quantization (4-bit & 8-bit LLMs)
Pruning to remove unnecessary model weights
Knowledge Distillation to compress large models
TensorRT & GPU acceleration
Mixture of Experts (MoE) for scalable inference
LoRA & PEFT for efficient fine-tuning
FlashAttention and optimized attention mechanisms

Whether you're building **real-time apps**, **mobile AI**, or **cloud-scale inference**, these strategies are essential.

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🌐 **SOURCES USED**:
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#LLMOptimization #TensorRT #MLSystemDesign #FAANG #MachineLearning #Quantization #MoE #LoRA #AI2025 #DeepLearning #MLDeployment #Google #Meta