Scaling on AI VOID

Inside LLMs: Inference Fundamentals and Key Concepts

Fri, 20 Mar 2026 00:00:00 +0000

Inside LLMs: Inference Fundamentals and Key Concepts

Welcome back, future LLM architect! In our previous chapter, we set the stage for LLMOps, understanding its importance in bringing Large Language Models from research to reliable production. Now, it’s time to peek behind the curtain and truly understand what happens when an LLM is asked a question – a process we call inference.

This chapter is your deep dive into the core mechanics of LLM inference, focusing on the unique challenges these powerful models present and the fundamental concepts needed to deploy them effectively. We’ll uncover why GPUs are indispensable, how we can make them work harder and smarter, and clever strategies like caching that can dramatically improve performance and reduce costs. By the end, you’ll have a solid conceptual foundation for building robust, scalable, and cost-efficient LLM production systems.

Scaling LLM Deployments: From Single Instances to Clusters

Fri, 20 Mar 2026 00:00:00 +0000

Scaling LLM Deployments: From Single Instances to Clusters

Welcome back, MLOps engineers, data scientists, and developers! In previous chapters, we’ve explored the foundational elements of LLM inference pipelines, model routing, and critical optimization techniques like caching and GPU usage. You’ve likely started to appreciate the sheer resource demands of Large Language Models.

Now, imagine your incredible LLM application goes viral overnight! Suddenly, a single GPU instance just won’t cut it. Requests flood in, latency skyrockets, and your users are unhappy. This is where the magic of scaling comes into play.

Scaling Netflix: Elasticity, Load Balancing, and Autoscaling

Thu, 19 Mar 2026 00:00:00 +0000

Introduction

Welcome to Chapter 9 of our deep dive into “How Netflix Works Internally.” In previous chapters, we laid the groundwork by discussing Netflix’s microservices architecture and principles of fault tolerance. Now, we confront a fundamental challenge for any global streaming service: how to handle massive, fluctuating user demand while maintaining high performance and availability. This is where the concepts of elasticity, load balancing, and autoscaling become paramount.

In this chapter, we will explore the core strategies Netflix employs to scale its infrastructure. You’ll learn how Netflix leverages cloud elasticity to dynamically adjust resources, distributes incoming traffic efficiently using various load balancing mechanisms, and automates resource provisioning and de-provisioning through sophisticated autoscaling solutions. Understanding these mechanisms is crucial for appreciating how Netflix can serve millions of concurrent users worldwide without skipping a beat.

Chapter 9: Distributed Training and Scaling with Tunix

Fri, 30 Jan 2026 00:00:00 +0000

Chapter 9: Distributed Training and Scaling with Tunix

Welcome back, intrepid Tunix explorer! So far, we’ve mastered the fundamentals of Tunix, understood its core concepts, and even applied it to fine-tune smaller language models. But what happens when our models grow to billions or even trillions of parameters? What happens when our datasets are so massive that a single GPU or even a single machine can’t handle them?

That’s where distributed training comes in! In this chapter, we’re going to dive into the exciting world of scaling our LLM post-training efforts. We’ll learn how Tunix, powered by JAX, allows us to harness the power of multiple devices – whether they’re GPUs or TPUs – to train larger models faster and more efficiently.

Chapter 9: Advanced Kubernetes - Scaling, Configuration & Secrets

Mon, 12 Jan 2026 00:00:00 +0000

Chapter 9: Advanced Kubernetes - Scaling, Configuration & Secrets

Welcome back, future DevOps maestro! In our previous Kubernetes adventures, you mastered the fundamentals: deploying applications with Pods, making them accessible with Services, and managing their lifecycle with Deployments. You’ve got a solid foundation, but real-world applications demand more – they need to be dynamic, adaptable, and secure.

This chapter is your gateway to making your Kubernetes applications truly production-ready. We’ll explore how to automatically scale your applications to handle varying loads, how to manage application configurations cleanly and efficiently, and critically, how to protect sensitive information like API keys and database credentials. By the end of this chapter, you’ll be able to build more resilient, flexible, and secure applications on Kubernetes.

Chapter 11: Scaling Your SpaceTimeDB Application: Distributed Architectures and Deployment

Sat, 14 Mar 2026 00:00:00 +0000

Chapter 11: Scaling Your SpaceTimeDB Application: Distributed Architectures and Deployment

Welcome back, intrepid SpaceTimeDB adventurer! Up until now, we’ve focused on building fantastic real-time applications on a single SpaceTimeDB instance. But what happens when your game explodes in popularity, your collaborative app goes viral, or your real-time dashboard needs to handle millions of data points per second? That’s when you need to think about scaling.

In this chapter, we’re going to tackle one of the most exciting and critical aspects of building production-ready systems: making them scale. We’ll explore how SpaceTimeDB’s unique architecture lends itself to distributed deployments, dive into concepts like sharding and replication, and then discuss modern deployment strategies using tools like Docker and Kubernetes. Get ready to design systems that can handle immense loads and stay resilient!

Chapter 17: Distributed Training & Scaling Deep Learning

Sat, 17 Jan 2026 00:00:00 +0000

Chapter 17: Distributed Training & Scaling Deep Learning

Welcome back, future AI architect! In our journey so far, we’ve built a strong foundation in deep learning, mastering neural network architectures, understanding training workflows, and optimizing models. We’ve even considered how powerful hardware like GPUs accelerate our tasks. But what happens when your model becomes so massive it won’t fit on a single GPU? Or when your dataset is so enormous that training takes weeks, even on the most powerful single machine?

Chapter 20: Deployment and Scaling HTMX Applications

Thu, 04 Dec 2025 00:00:00 +0000

Chapter 20: Deployment and Scaling HTMX Applications

Welcome back, fellow web adventurer! You’ve come a long way, mastering the magic of HTMX to create dynamic, engaging user interfaces with minimal JavaScript. So far, we’ve focused on building fantastic features locally. But what good is a masterpiece if it’s only admired in your workshop?

In this chapter, we’re going to tackle the exciting, and sometimes daunting, world of taking your HTMX applications from your development machine to the vast, open internet. We’ll explore the core concepts behind deploying and scaling HTMX-powered web applications, ensuring they are robust, performant, and ready for real-world traffic. Get ready to think about how your server-side rendering strategy impacts everything from caching to load balancing!

Build a Production Docker Stack Guide

Fri, 22 May 2026 00:00:00 +0000

Welcome to this comprehensive guide on designing and building a production-ready Docker stack. Across 13 detailed steps, you will learn essential best practices for deploying, scaling, and securing modern applications using Docker Compose. Prepare to transform your development setup into a robust, production-grade environment.

AI Infrastructure and LLMOps Guide

Fri, 20 Mar 2026 00:00:00 +0000

This comprehensive guide demystifies AI infrastructure and LLMOps, providing essential knowledge for deploying and managing AI systems effectively in production. Explore critical topics such as model routing, inference pipelines, caching strategies, GPU utilization, and robust monitoring. Discover real-world architectures and best practices to optimize performance, cost, and scalability for your AI applications.