Posts

Engineering the Heartbeat of Markets: Designing a Modern Stock Exchange from Scratch Imagine a digital arena where billions of dollars change hands every second, all orchestrated by software that must react faster than a human blink. That’s the stock exchange—a high-stakes symphony of buys, sells, and matches running on razor-thin margins of latency and reliability. In this post, we’ll dive deep into designing a stock exchange system, demystifying its core mechanics, architecture, and the engineering wizardry that keeps markets humming. Whether you’re prepping for system design interviews, scaling fintech apps, or just curious about the tech behind Wall Street, this guide breaks it down step-by-step with fresh insights, real-world parallels, and practical blueprints.[1][2] ...

Introduction The convergence of serverless computing, artificial intelligence, and event‑driven architectures is reshaping how modern applications are built, deployed, and operated. Where traditional monolithic AI pipelines required dedicated VMs, complex orchestration tools, and a lot of manual scaling effort, today developers can compose autonomous agent workflows that spin up on demand, react instantly to events, and scale to millions of concurrent executions—all while paying only for the compute they actually use. ...

Introduction Autonomous agents—software entities that perceive, reason, and act without continuous human guidance—are rapidly moving from isolated prototypes to production‑grade services. From conversational assistants and autonomous vehicles to large‑scale recommendation engines, these agents must process massive streams of data, maintain coherent state across many instances, and adapt in real time. The challenges of scaling such agents are fundamentally different from scaling stateless microservices: Challenge Why It Matters for Agents Stateful Reasoning Agents need to retain context, learn from past interactions, and update internal models. Latency Sensitivity Real‑time decisions (e.g., collision avoidance) cannot tolerate high round‑trip times. Observability Debugging emergent behavior requires visibility into both data flow and internal cognition. Fault Tolerance A single faulty agent should not corrupt the collective intelligence. Two architectural pillars have emerged as decisive enablers: ...

Table of Contents Introduction Semantic Search and Embedding Models: A Quick Recap Why Resource Constraints Matter Model‑Level Optimizations 4.1 Quantization 4.2 Pruning & Structured Sparsity 4.3 Knowledge Distillation 4.4 Low‑Rank Factorization Efficient Indexing & Retrieval Structures 5.1 Flat vs. IVF vs. HNSW 5.2 Product Quantization (PQ) and OPQ 5.3 Hybrid Approaches (FAISS + On‑Device Caches) System‑Level Tactics 6.1 Batching & Dynamic Padding 6.2 Caching Embeddings & Results 6.3 Asynchronous Pipelines & Streaming Practical End‑to‑End Example Monitoring, Evaluation, and Trade‑Offs Conclusion Resources Introduction Semantic search has become the de‑facto method for retrieving information when the exact keyword match is insufficient. By converting queries and documents into dense vector embeddings, similarity metrics (e.g., cosine similarity) can surface relevant content that shares meaning, not just wording. However, the power of modern embedding models—often based on large transformer architectures—comes at a steep computational price. ...

Table of Contents Introduction Why 100‑Billion‑Parameter Models Matter Fundamentals of Model Quantization 3.1 Weight vs. Activation Quantization 3.2 Common Bit‑Widths and Their Trade‑offs Consumer‑Grade Hardware Landscape 4.1 CPU‑Centric Systems 4.2 GPU‑Centric Systems 4.3 Emerging Accelerators (TPU, NPU, AI‑Chiplets) Quantization Techniques for 100B Models 5.1 Post‑Training Quantization (PTQ) 5.2 GPTQ & AWQ: Low‑Rank Approximation Methods 5.3 Mixed‑Precision & Per‑Channel Schemes Toolchains and Frameworks 6.1 llama.cpp 6.2 TensorRT‑LLM 6.3 ONNX Runtime + Quantization 6.4 vLLM & DeepSpeed‑Inference Step‑by‑Step Deployment Pipeline 7.1 Acquiring the Model 7.2 Preparing the Environment 7.3 Running PTQ with GPTQ 7.4 Converting to Runtime‑Friendly Formats 7.5 Launching Inference Performance Tuning Strategies 8.1 KV‑Cache Management 8.2 Batch Size & Sequence Length Trade‑offs 8.3 Thread‑Pinning & NUMA Awareness Real‑World Benchmarks Common Pitfalls & Debugging Tips Future Outlook: From 100B to 1T on the Desktop Conclusion Resources Introduction The AI community has witnessed a rapid escalation in the size of large language models (LLMs), with 100‑billion‑parameter (100B) architectures now considered the sweet spot for high‑quality generation, reasoning, and instruction‑following. Historically, running such models required multi‑GPU clusters or specialised cloud instances, making local inference a luxury reserved for research labs. ...