Performance

Table of Contents Introduction Why Run LLMs Locally? WebAssembly as the Execution Engine for Local LLMs 3.1 Wasm’s Core Advantages 3.2 Current Limitations for AI Workloads Garbage Collection in WebAssembly: A Brief History The New GC Proposal and Its Implications 5.1 Typed References and Runtime Type Information 5.2 Deterministic Memory Management 5.3 Interoperability with Existing Languages Performance Bottlenecks in Local LLM Inference 6.1 Memory Allocation Overhead 6.2 Cache Misses & Fragmentation 6.3 Threading and Parallelism Constraints Practical Optimization Techniques Using Wasm GC 7.1 Zero‑Copy Tensor Buffers 7.2 Arena Allocation for Transient Objects 7.3 Pinned Memory for GPU/Accelerator Offload 7.4 Static vs Dynamic Dispatch in Model Layers Case Study: Running a 7B Transformer with Wasm‑GC on a Raspberry Pi 5 8.1 Setup Overview 8.2 Benchmarks Before GC Optimizations 8.3 Applying the Optimizations 8.4 Results & Analysis Best Practices for Developers Future Directions: Beyond GC – SIMD, Threads, and Custom Memory Allocators Conclusion Resources Introduction Large language models (LLMs) have moved from cloud‑only research curiosities to everyday developer tools. Yet, the same cloud‑centric mindset that powers ChatGPT or Claude also creates latency, privacy, and cost concerns for many real‑world use cases. Running LLM inference locally—whether on a laptop, edge device, or an on‑premise server—offers immediate responsiveness, data sovereignty, and the possibility of fine‑grained control over model behavior. ...

Introduction Retrieval‑Augmented Generation (RAG) has quickly become the de‑facto architecture for building LLM‑powered applications that need up‑to‑date, factual, or domain‑specific knowledge. By coupling a large language model (LLM) with a vector store that holds embedded representations of documents, RAG lets the model “look up” relevant passages before it generates an answer. While the conceptual pipeline is simple—embed → store → retrieve → generate—real‑world deployments quickly expose performance bottlenecks. Two of the most potent levers for scaling RAG are metadata‑based filtering and vector database indexing strategies. Properly harnessed, they can: ...

Introduction Vector databases have moved from research prototypes to core components of modern data pipelines. Whether you’re powering a recommendation engine, a semantic search service, or an anomaly‑detection system, you’re often dealing with high‑dimensional embeddings that must be stored, indexed, and queried at scale. In production environments, the stakes are higher: latency budgets are measured in milliseconds, throughput can reach hundreds of thousands of queries per second, and any performance regression can directly affect user experience and revenue. ...

Introduction Large language models (LLMs) such as GPT‑4, Claude, or LLaMA have transformed how we approach natural language understanding, generation, and reasoning. While the raw generative capability of these models is impressive, many production‑grade applications rely on retrieval‑augmented generation (RAG), where the model is supplied with relevant context drawn from a massive corpus of documents, embeddings, or other structured data. At the heart of RAG pipelines lies a vector database (also called a similarity search engine). It stores high‑dimensional embeddings, indexes them for fast nearest‑neighbor (K‑NN) lookup, and serves queries at scale. In high‑throughput scenarios—think chat‑bots handling thousands of concurrent users, real‑time recommendation engines, or search‑as‑you‑type interfaces—latency, throughput, and cost become critical success factors. ...

Introduction Rust has earned a reputation as the language that delivers C‑level performance while offering memory safety guarantees that most systems languages lack. At the heart of this promise lies Rust’s unique approach to memory management: a static ownership model enforced by the compiler, combined with the ability to drop down to raw pointers and unsafe blocks when absolute control is required. This article is a comprehensive, deep‑dive into how Rust manages memory—from the high‑level concepts of ownership and borrowing down to low‑level optimizations that touch the metal. We’ll explore: ...