Ray Serve is a cutting-edge model serving library built on the Ray framework, designed to simplify and scale AI model deployment. Whether you’re chaining models in sequence, running them in parallel, or dynamically routing requests, Ray Serve excels at handling complex, distributed inference pipelines. Unlike Ollama or FastAPI, it combines ease of use with powerful scaling, multi-model management, and Pythonic APIs. In this post, we’ll explore how Ray Serve compares to other solutions and why it stands out for large-scale, multi-node AI serving. Before Introducing Ray Serve, We Need to Understand Ray What is Ray? Ray is an open-source distributed computing framework that provides the core tools and components for building and running distributed applications. Its goal is to enable developers to easily scale single-machine programs to distributed environments, supporting high-performance tasks such as distributed model training, large-scale data processing, and distributed inference. Core Modules of Ray Ray Core The foundation of Ray, providing distributed scheduling, task execution, and resource management. Allows Python functions to be seamlessly transformed into distributed tasks using the @ray.remote decorator. Ideal for distributed data processing and computation-intensive workloads. Ray Libraries Built on top of Ray Core, these are specialized tools designed for specific tasks. Examples include: Ray Tune: For hyperparameter search and experiment optimization. Ray Train: For distributed model training. Ray Serve: For distributed model serving. Ray Data: For large-scale data and stream processing. In simpler terms, Ray Core is the underlying engine, while the various tools (like Ray Serve) are specific modules built on top of it to handle specific functionalities. Now Let’s Talk…

Quantization is a transformative AI optimization technique that compresses models by reducing precision from high-bit floating-point numbers (e.g., FP32) to low-bit integers (e.g., INT8). This process significantly decreases storage requirements, speeds up inference, and enables deployment on resource-constrained devices like mobile phones or IoT systems—all while retaining close-to-original performance. Let’s explore why it is essential, how it works, and its real-world applications. Why Do AI Models Need to Be Slimmed Down? AI models are growing exponentially in size, with models like GPT-4 containing hundreds of billions of parameters. While their performance is impressive, this scale brings challenges: High Computational Costs: Large models require expensive hardware like GPUs or TPUs, with significant power consumption. Slow Inference Speed: Real-time applications, such as voice assistants or autonomous driving, demand fast responses that large models struggle to provide. Deployment Constraints: Limited memory and compute power on mobile or IoT devices make running large models impractical. The Problem How can we preserve the capabilities of large models while making them lightweight and efficient? The Solution Quantization. This optimization method compresses models to improve efficiency without sacrificing much performance. What Is It? It reduces the precision of AI model parameters (weights) and intermediate results (activations) from high-precision formats like FP32 to lower-precision formats like FP16 or INT8. Simplified Analogy It is like compressing an image: Original Image (High Precision): High resolution, large file size, slow to load. Compressed Image (Low Precision): Smaller file size with slightly lower quality but faster and more efficient. How Does It Work? The key is representing parameters and activations using fewer…

Introduction: Why It Matters In the rapidly evolving field of AI, the distinction between foundation models and task models is critical for understanding how modern AI systems work. Foundation models, like GPT-4 or BERT, provide the backbone of AI development, offering general-purpose capabilities. Task models, on the other hand, are fine-tuned or custom-built for specific applications. Understanding their differences helps businesses and developers leverage the right model for the right task, optimizing both performance and cost. Let’s dive into how these two types of models differ and why both are essential. 1. What Are Foundation Models? Foundation models are general-purpose AI models trained on vast amounts of data to understand and generate language across a wide range of contexts. Their primary goal is to act as a universal knowledge base, capable of supporting a multitude of applications with minimal additional training. Examples of foundation models include GPT-4, BERT, and PaLM. These models are not designed for any one task but are built to be flexible, with a deep understanding of grammar, structure, and semantics. Key Features: Massive Scale: Often involve billions or even trillions of parameters (What does parameters mean? You can refer to my previous blog What Are Parameters?). Multi-Purpose: Can be adapted for numerous tasks through fine-tuning or prompt engineering (Please refer to my previous blog What Is Prompt Engineering and What Is Fine-Tuning). Pretraining-Driven: Trained on vast datasets (e.g., Wikipedia, news, books) to understand general language structures (Please refer to ). Think of a foundation model as a jack-of-all-trades—broadly knowledgeable but not specialized in any one field.…

1. What Are Parameters? This was covered in a previous issue: What Are Parameters? Why Are “Bigger” Models Often “Smarter”? 2. The Relationship Between Parameter Count and Inference Speed As the number of parameters in a model increases, it requires more computational resources to perform inference (i.e., generate results). This directly impacts inference speed. However, the relationship between parameters and speed is not a straightforward inverse correlation. Several factors influence inference speed: (1) Computational Load (FLOPs) The number of floating-point operations (FLOPs) required by a model directly impacts inference time. However, FLOPs are not the sole determinant since different types of operations may execute with varying efficiency on hardware. (2) Memory Access Cost During inference, the model frequently accesses memory. The volume of memory access (or memory bandwidth requirements) can affect speed. For instance, both the computational load and memory access demands of deep learning models significantly impact deployment and inference performance. (3) Model Architecture The design of the model, including its parallelism and branching structure, influences efficiency. For example, branched architectures may introduce synchronization overhead, causing some compute units to idle and slowing inference. (4) Hardware Architecture Different hardware setups handle models differently. A device’s computational power, memory bandwidth, and overall architecture all affect inference speed. Efficient neural network designs must balance computational load and memory demands for optimal performance across various hardware environments. Thus, while parameter count is one factor affecting inference time, it’s not a simple inverse relationship. Optimizing inference speed requires consideration of computational load, memory access patterns, model architecture, and hardware capabilities. 3. Why Are…

The Problem: Too Much Dust on Old Photos, Too Aggressive a Cleanup Schedule Imagine sifting through tens of thousands of photos—manually. I mounted the NAS SMB partition on my MacBook, only to discover it was excruciatingly slow. After two days of copying files to my MacBook, my manual review session turned into a blur. My eyes hurt, my patience wore thin, and I knew there had to be a better way. When I turned to existing tools, I hit a wall. Most were paid, overly complex, or simply didn’t fit my needs. Even the so-called free solutions required learning arcane commands like find. I needed something powerful, flexible, and fast. And when all else fails, what’s a tech enthusiast to do? Write their own solution—with a "little" help from ChatGPT. The Power of ChatGPT I’d dabbled with the same task scripting years ago but quickly gave up because of the time it required. Enter ChatGPT (no marketing here... I am a paid user though...), the real hero of this story. With its assistance, I wrote the majority of the script in less than a day before i gave up ! But anyway, of course, I still have to thank the emergence of Large Language Models! Based on the current code volume and quality, without 10 to 15 days, a single person would absolutely not be able to achieve the current results! So, I believe LLMs have helped me improve my efficiency by at least 10 times! And they've helped me avoid all sorts of unnecessary detours! So now, I've create…