LLM Inference Performance Engineering-Optimization, Scaling & GPU Performance -Live Training
(LLM Inference Performance Engineering – Optimization, Scaling & GPU Performance.)
LLM Inference Performance Engineering is an advanced technical course designed to teach professionals how to optimize the performance, speed, scalability, and resource utilization of Large Language Models (LLMs) in real-world production environments.
When users interact with AI applications such as chatbots, AI assistants, Generative AI platforms, and RAG-based systems, the underlying LLM must process requests and generate responses efficiently. As the number of users and requests increases, these systems can experience high response times, GPU memory bottlenecks, increased infrastructure costs, and reduced throughput.
This course focuses on understanding and solving these performance challenges through practical optimization techniques, benchmarking tools, inference frameworks, and GPU resource management.
Participants will gain hands-on experience in optimizing LLM inference pipelines, improving Time to First Token (TTFT), reducing Time Per Output Token (TPOT), increasing tokens-per-second throughput, implementing continuous batching, optimizing KV cache, and applying model quantization techniques.
Why Choose This Course?
- Industry-Relevant Skills
Learn practical techniques used to optimize LLM inference in real-world AI applications. - High-Demand Specialization
Build expertise in Generative AI performance engineering and production-scale LLM optimization. - 100% Hands-On Learning
Get practical experience with inference frameworks, benchmarking tools, and GPU optimization techniques. - Performance Optimization Expertise
Learn how to reduce latency, improve throughput, and speed up token generation. - Modern Tools & Technologies
Work with popular tools such as vLLM, Hugging Face TGI, and NVIDIA TensorRT-LLM. - GPU Resource Management
Understand GPU memory usage, KV cache optimization, quantization, and efficient resource allocation.
About the Instructor:
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Kelvin brings 14+ years of industry experience in software engineering, performance engineering, and advanced technology solutions. He has worked on diverse enterprise-level projects involving AI/ML technologies, performance optimization, system scalability, infrastructure engineering, and application performance management. He has strong experience in analyzing complex system performance challenges, identifying infrastructure bottlenecks, and implementing optimization strategies for enterprise applications. Kelvin emphasizes practical, real-world learning through technical demonstrations, project-based examples, and industry use cases. His training approach helps participants understand how to optimize LLM inference pipelines, GPU utilization, response latency, throughput, and scalable AI deployments. He also focuses on helping learners understand the relationship between model performance, infrastructure efficiency, and operational costs. Through practical scenarios, participants gain insights into evaluating AI workloads, selecting suitable optimization approaches, and improving application responsiveness under varying workloads. His sessions are designed to bridge the gap between theoretical concepts and real-world implementation, enabling professionals to develop the technical confidence required to work with modern LLM serving architectures and production-scale Generative AI systems |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 196 USD 160 118 USD Or USD17000 INR 14000 INR 11,000 Rupees.
OR
Free Demo Session Timings:
Indian Timings: December 15th @ 9 PM – 10 PM (IST)
U.S Timings: December 15th@ 11:30 AM – 12:30 PM (EST)
UK Timings: December 15th @ 4:30 PM – 5:30 PM (BST)
Class Schedule:
For Participants in India: Monday to Friday @ 9 PM – 10 PM (IST)
For Participants in US: Monday to Friday @ 11:30 AM – 12:30 PM (EST)
For Participants in UK: Monday to Friday @ 4:30 PM – 5:30 PM (BST)
What student’s have to say about Trainer :
| 👨 Ayla
“Excellent training experience with Kelvin. The sessions provided a clear understanding of LLM inference, performance optimization, GPU utilization, and latency reduction. The practical explanations made complex concepts easier to understand.” 👩Esther “Kelvin has strong technical knowledge and explains concepts in a structured way. I particularly liked learning about vLLM, KV cache optimization, and inference performance benchmarking. Very useful course for AI professionals.” 👨Advait “One of the most valuable technical courses I have attended. The real-world examples helped me understand how to improve LLM response times, optimize throughput, and manage GPU resources efficiently.” 👨Rayyan Zayd “Great learning experience! The trainer explained advanced topics like model quantization, continuous batching, and inference optimization with practical examples. The sessions were interactive and informative.” 👩Caleb Matthew “Highly recommended for professionals interested in Generative AI and performance engineering. Kelvin shared valuable industry insights and practical approaches to building scalable, high-performance LLM applications.” |
What Will I Learn by the End of This Course?
- Understand the complete LLM inference lifecycle.
- Measure and analyze LLM performance using industry-relevant metrics.
- Optimize inference speed and reduce response latency.
- Improve GPU memory utilization and token generation throughput.
- Implement efficient model serving using modern inference frameworks.
- Perform LLM load testing and performance benchmarking.
- Optimize infrastructure costs while maintaining performance.
- Design scalable inference architectures for production AI applications.
Salient Features:
- 15 Hours of Live Training along with recorded videos
- 1-year access to all recorded sessions
- Course Completion Certificate provided
Who Can Enroll in This Course?This course is suitable for:
- AI/ML Engineers – Optimize Large Language Models for better performance.
- Generative AI Engineers – Improve the speed and efficiency of LLM-based applications.
- Machine Learning Engineers – Focus on model performance and scalability.
- MLOps Engineers – Manage AI model deployment and infrastructure more efficiently.
- Performance Engineers – Learn LLM performance testing and optimization.
- Cloud & DevOps Engineers – Work with AI infrastructure and GPU resources.
- LLM Application Developers – Build faster chatbots, AI assistants, and RAG-based systems.
- Software Engineers – Specialize in AI inference performance engineering.
- Technical Architects & Engineering Leads – Design and manage scalable AI solutions.
- IT Professionals – Advance your career in Generative AI and AI Infrastructure.
Course Features
- Lecture 0
- Quiz 0
- Duration 20 hours
- Skill level All levels
- Language English
- Students 0
- Assessments Yes
