AI Infrastructure
16 articles exploring AI Infrastructure. Expert analysis and insights from our editorial team.
Latest in AI Infrastructure
IonRouter (YC W26): The Custom NVIDIA GH200 Runtime Targeting the LLM Inference Cost Crisis
IonRouter (YC W26) built IonAttention, a custom GH200 inference runtime claiming 50% cost cuts and 2x VLM throughput. Here's what the technology actually does.
OpenRAG: The Open-Source RAG Platform Challenging Pinecone
OpenRAG combines Langflow, OpenSearch, and Docling into a single deployable RAG platform. Here's how it compares to managed services like Pinecone.
MLX vs llama.cpp on Apple Silicon: Which Runtime to Use for Local LLM Inference
MLX delivers 20-87% faster generation on Apple Silicon for models under 14B parameters. llama.cpp wins for cross-platform use and long contexts.
Prefill-Decode Disaggregation: The Architecture Shift Redefining LLM Serving at Scale
Prefill-decode disaggregation separates compute-bound prefill from memory-bound decode onto dedicated hardware, eliminating phase interference.
Google LiteRT: Running LLMs on Your Phone Without the Cloud
Google's LiteRT (formerly TensorFlow Lite) is now the production backbone for on-device GenAI across Android, Chrome, and Pixel devices. Here's what it means for developers building AI apps that run privately, without the cloud.
Microsoft's BitNet: How 1-Bit LLMs Could Make GPU Farms Obsolete
Microsoft's BitNet inference framework runs billion-parameter LLMs on ordinary CPUs using ternary weights, delivering up to 6x faster inference and 82% lower energy consumption—potentially upending the assumption that AI inference requires expensive GPU hardware.
The MCP Registry: GitHub's Play to Become the App Store for AI Tools
GitHub's MCP Registry centralizes discovery of Model Context Protocol servers, positioning GitHub as the primary distribution layer for AI agent tooling and addressing the fragmentation that emerged as MCP's ecosystem exploded past 5,000 servers in under a year.
MCP Is Everywhere: The Protocol That Connected AI to Everything
How the Model Context Protocol became the universal standard connecting AI assistants to data sources, tools, and enterprise systems—transforming isolated models into truly connected agents.
Nvidia's Deal With Meta Signals a New Era in AI Computing Power
Meta and Nvidia announced a multi-year strategic partnership in February 2026 that will see Meta deploy Nvidia's Vera Rubin platform across gigawatt-scale data centers, representing one of the largest single commitments of AI computing resources in history.
Alibaba's zvec: A Lightning-Fast Vector Database That Fits In-Process
Zvec is Alibaba's open-source, in-process vector database built on the battle-tested Proxima engine. It enables millisecond semantic search across billions of vectors without requiring external servers or infrastructure, making it ideal for edge AI and embedded applications.
Edge AI Deployment: Running Models Where the Data Lives
Edge AI deploys machine learning models directly on local devices, reducing latency to milliseconds while keeping sensitive data private. This comprehensive guide covers deployment strategies, optimization techniques, and key frameworks for running AI from smartphones to IoT sensors.
GitHub Agentic Workflows: AI That Commits Code For You
GitHub's agentic workflows bring autonomous AI agents directly into the developer workflow, enabling AI to write code, create pull requests, and respond to feedback—transforming the PR process from manual coding to AI-assisted systems thinking.
Vector Search at Scale: Architectures That Handle Billions of Embeddings
Vector search at scale requires distributed architectures, approximate nearest neighbor algorithms like HNSW and IVF, and intelligent sharding strategies. Leading implementations can query billions of embeddings in milliseconds with 95%+ recall.
Perplexity API: Adding Real-Time Search to Your Apps in Minutes
A comprehensive guide to implementing Perplexity's Search API, featuring pricing, code examples, use cases, and comparisons with alternatives.
RAG in Production: Retrieval Augmented Generation That Actually Works
RAG combines large language models with external knowledge retrieval to reduce hallucinations and ground AI outputs in factual data. While the concept is straightforward, production deployment reveals critical challenges around chunking strategies, latency optimization, and retrieval accuracy that separate working systems from prototypes.