LLM Inference Optimization Explained | Quantization, KV Cache, Batching & GPU Performance

Описание: Want to optimize Large Language Model (LLM) inference for maximum throughput and minimum latency? In this deep-dive tutorial, you'll learn the full-stack techniques used by AI infrastructure teams to accelerate model serving, reduce costs, and improve production performance.

This video explores how modern AI systems optimize GPU utilization through advanced batching strategies, KV cache management, quantization, scheduling algorithms, and performance profiling across the entire inference stack.

📚 Topics Covered:
✅ LLM Inference Fundamentals
✅ GPU Throughput vs Latency Trade-offs
✅ Prefill and Decode Phases Explained
✅ Disaggregated Prefill & Decode Architecture
✅ Continuous Batching
✅ Dynamic Batching Techniques
✅ Chunked Prefill Optimization
✅ KV Cache Management
✅ Weight-Only Quantization
✅ GPTQ & AWQ Quantization Methods
✅ Memory Bandwidth Optimization
✅ GPU Utilization Strategies
✅ Tail Latency Reduction
✅ Model Serving Architectures
✅ Prometheus & Grafana Monitoring
✅ NVIDIA Nsight Profiling
✅ Bottleneck Identification & Analysis
✅ Production AI Infrastructure Best Practices

You'll discover how leading AI platforms optimize inference pipelines to serve more users with lower hardware costs while maintaining high responsiveness and reliability.

🚀 Perfect For:
• AI Engineers
• ML Engineers
• LLM Infrastructure Engineers
• Platform Engineers
• MLOps Engineers
• Cloud Architects
• Software Architects
• Generative AI Developers

🔥 Key Learning Outcomes:
✔ Optimize GPU utilization for LLM serving
✔ Reduce inference latency and operational costs
✔ Implement advanced batching strategies
✔ Understand KV cache optimization techniques
✔ Profile and troubleshoot inference bottlenecks
✔ Build scalable AI infrastructure

Whether you're deploying open-source models, operating enterprise AI platforms, or designing next-generation LLM systems, this tutorial provides practical insights into real-world inference optimization.

📈 Advanced Topics Included:
• Continuous Batching
• Chunked Prefill
• Tensor Parallelism
• Model Quantization
• GPU Scheduling
• Memory Optimization
• AI Infrastructure Monitoring
• High-Performance LLM Serving

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LLM Engineering, AI Infrastructure, MLOps, Agentic AI, Distributed Systems, GPU Optimization, AI Architecture, Generative AI, and Production Machine Learning.

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