Tutorials on Performance

Distributed LLM inference lets large language models run across multiple edge devices like smartphones, IoT sensors, and smart cameras. By splitting the model into smaller parts, each device processes specific sections, reducing the need for cloud-based infrastructure and keeping data local. This approach addresses challenges like limited device resources, privacy concerns, and unreliable connectivity, making it ideal for applications in smart cities, healthcare, industrial IoT , and smart homes. This method balances performance, privacy, and resource constraints, enabling advanced AI on everyday devices. Distributed LLM inference can be implemented using centralized, hybrid, or decentralized architectures, each suited to different enterprise needs.

Explore the transformative impact of dynamic role assignment in multi-agent systems, enhancing efficiency and adaptability in real-time environments.

Explore the differences between Pre-Norm and Post-Norm strategies in transformer models to optimize training stability and performance.

Learn how to effectively simulate large-scale multi-agent systems, from selecting frameworks to optimizing performance for complex environments.

Explore how speculative decoding enhances AI text generation by combining speed and quality through a draft-and-verify model approach.

PagedAttention enhances GPU memory management for large language models, improving efficiency, scalability, and cost during inference.

Explore how vLLM enhances large language model efficiency, optimizing memory and speed for various AI applications in production environments.

Learn essential API integration practices to ensure seamless, secure, and efficient workflows in your coding projects.

Explore how a specialized library enhances the efficiency of large language models with advanced attention mechanisms and resource management.

Explore how a memory-efficient algorithm enhances large language models by accelerating processing and reducing resource demands.

Explore the differences between AutoRound and AWQ quantization methods for large language models, focusing on accuracy, speed, and use cases.

Explore the differences between GPTQ and AWQ quantization methods for optimizing large language models, focusing on efficiency and accuracy.

Explore how GPTQ quantization optimizes large AI models for faster performance and reduced resource usage without sacrificing accuracy.

Explore the differences between two leading AI frameworks for large language models, focusing on their strengths in general tasks versus conversational applications.

Explore the importance of long-term monitoring in LLMs to enhance user experience, comply with regulations, and drive system improvements.

User feedback is essential for improving large language models, bridging the gap between benchmarks and real-world performance.

Explore telemetry strategies for enhancing distributed tracing in AI agents, addressing unique challenges and solutions for effective monitoring.

Explore the differences between MCP and A2A protocols to determine the best fit for your AI workflows, enhancing efficiency and collaboration.

Explore effective strategies for debugging complex multi-agent LLM systems, addressing challenges like non-determinism and communication breakdowns.

Explore the advantages and techniques of fixed-size chunking in retrieval-augmented generation to enhance efficiency and accuracy in data processing.

Explore the balance between model sparsity and accuracy in AI, examining pruning techniques and their implications for deployment and performance.

Explore effective regularization techniques for fine-tuning large language models with limited data, ensuring better generalization and performance.

Learn how to optimize LLM workflows with Python's asyncio, focusing on concurrency patterns, error handling, and performance tuning.

Explore essential tools for evaluating AI prompts in 2025, enhancing performance, reliability, and cost management.

Learn how to identify and fix GPU bottlenecks in large language model pipelines for improved performance and scalability.

Learn how to fine-tune large language models effectively on a budget with cost-saving techniques and strategies for optimal results.

Explore effective strategies for debugging complex multi-agent LLM systems, enhancing reliability and performance in AI applications.

Explore how gradient checkpointing and model partitioning can optimize memory usage for fine-tuning large language models on limited hardware.

Explore effective strategies to analyze and reduce inference latency in large language models, improving performance and user experience.

Explore how Apache Kafka enables real-time event streaming for large language models, enhancing scalability and reliability in AI applications.