Infrastructure & Runtime
23 articles exploring Infrastructure & Runtime. Expert analysis and insights from our editorial team.
Where AI models run determines everything about latency, cost, privacy, and operational risk. This cluster covers the runtime and serving layer: inference optimization, hardware tradeoffs, RAG architectures, vector search at scale, and the growing ecosystem of edge and on-device deployment.
Serving-side architecture has undergone a genuine paradigm shift with prefill-decode disaggregation. The insight—that prefill is compute-bound while decode is memory-bandwidth-bound, and routing them to different hardware pools eliminates the phase interference that inflates P99 latency—is now being productized by NVIDIA Dynamo, vLLM’s disaggregated serving, and Mooncake at ByteDance scale. If you’re running inference at volume, this architectural decision is no longer academic.
The local and edge stack has consolidated around a shorter list of serious runtimes. MLX performs significantly better than llama.cpp on Apple Silicon for sub-14B models while llama.cpp remains the right call for cross-platform deployments and long contexts. Google’s LiteRT (successor to TensorFlow Lite) anchors the Android/embedded side. The tradeoffs are measurable; Groundy publishes benchmark results rather than vendor summaries.
RAG production architecture is where theory consistently meets deployment reality. The gap between a notebook demo and a system that handles document poisoning, retrieval precision degradation under index growth, and embedding drift over time is where most RAG projects stall. Groundy covers the failure modes—not just the happy-path architecture diagrams.
Hardware selection is increasingly a first-class decision. Microsoft’s BitNet 1-bit quantization, NVIDIA’s open-source quantum-calibration models, and Alibaba’s ZVEC vector database each represent architectural bets on where the cost curves are heading. This cluster tracks those bets.
Serving infrastructure also intersects directly with security posture. Container deployments inherit every existing container vulnerability alongside a new class of AI-specific threats: model-weight theft, prompt injection through sidecar services, and supply-chain attacks targeting the Python dependencies that wrap inference engines. Infrastructure coverage at Groundy treats the operational and security dimensions as a single problem, not separate lanes.
Featured in this cluster
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.
CornerstoneEdge 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.
CornerstoneThe Complete Guide to Local LLMs in 2026
Why running AI on your own hardware is becoming the default choice for privacy-conscious developers and enterprises alike
CornerstoneMLX 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.
CornerstoneVector 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.
Latest in Infrastructure & Runtime
KV Cache Is Becoming a Distributed Infrastructure Layer: What KV Packet and llm-d Mean for Self-Hosted LLM Teams
KV Packet eliminates cross-request recomputation; llm-d brings cache-aware routing to Kubernetes. Here's what both mean for vLLM capacity planning.
Google Cloud Is Doubling Peering Egress Costs on May 1 — Here's What to Audit Before Then
GCP doubles North America CDN Interconnect and Direct Peering rates May 1. Here's how to find your exposure in 10 minutes and rank your mitigation options.
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.
WebAssembly AI: Running Models in the Browser
WebAssembly enables production-ready AI inference directly in the browser—no server required. Learn how WASM, WebGPU, and modern frameworks make client-side ML practical, what the performance trade-offs actually look like, and when to use it.
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.
Microsoft's Data Storage That Lasts Millennia
Microsoft's Project Silica has demonstrated a way to encode terabytes of data into ordinary borosilicate glass using femtosecond lasers, with accelerated aging tests projecting data integrity for at least 10,000 years—at a fraction of previous costs.
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.
Pebble Is Back: Inside the Community-Driven Smartwatch Revival
After nine years in stasis, Pebble—the iconic smartwatch that pioneered wearable computing—is returning through a grassroots revival led by its original founder and a passionate community of developers.
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.