Infrastructure & Runtime
33 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](/articles/vllm-block-level-preemption-and-flexkv-shift-the-long-context-bottleneck-from/) 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
Crawshaw's 'I Am Building a Cloud': What a Tailscale Co-Founder's Solo Stack Implies for Platform Teams
David Crawshaw's exe.dev launched with $35M, giving platform teams a concrete alternative to the Kubernetes default that forces TCO justification for cloud-native overhead.
Azure NAT Gateway Blocks [Tailscale Direct Connect](/articles/crawshaws-i-am-building-a-cloud-what-a-tailscale-co-founders-solo-stack-implies/); v1.96.2 Fixes Container Relay Scaling for AKS
Azure NAT Gateway's Hard NAT forces Tailscale onto DERP; a public-subnet Peer Relay bypasses it. v1.96.2 fixes container GOMAXPROCS socket scaling for AKS relay instances.
K-Token Merging Compresses Sequences in Latent Space, Lowering KV Cache Floors for 24GB and 48GB Cards
K-Token Merging compresses prompts in latent space before attention, cutting prefill KV cache 75% on 0.5B models and extending feasible context on 24GB and 48GB consumer GPUs.
KServe + llm-d Claims 57× P90 TTFT. RC1 Ships with a Routing Deadlock and No Migration Guide
Red Hat's KServe + llm-d integration claims 57× P90 TTFT gains against an unoptimized vLLM baseline, but RC1 ships with a known routing deadlock, a prematurely merged WIP.
UCCL-Zip Adds Lossless Compression to NCCL Collectives: 47.5% Faster RL Weight Sync, No API Changes
UCCL-Zip fuses lossless compression into NCCL collectives at the kernel level, cutting cross-node wire bytes without accuracy tradeoffs or application changes. Peak gains:.
UCCL-Zip: Lossless Compression for NCCL, 47.5% Faster RL Sync, 10% Lower vLLM Latency
UCCL-Zip fuses lossless compression into NCCL and GPU P2P transfers, cutting RL weight sync by 47.5% and vLLM latency by 10% with no API changes and bit-identical outputs.
CoCoDiff Exposes the All-to-All Bottleneck That Caps Distributed Diffusion Transformer Inference Well Below Theoretical GPU Count
Ulysses parallelism caps distributed DiT inference scaling on heterogeneous interconnects. CoCoDiff delivers 3.6x average speedups on Aurora via topology-aware scheduling.
Ingress-Nginx Is Dead, Not Deprecated: The Final CVE Patches Shipped, But [Platform Teams](/articles/crawshaws-i-am-building-a-cloud-what-a-tailscale-co-founders-solo-stack-implies/) Still Need a Migration Plan
ingress-nginx was retired March 24, 2026. CVE-2026-4342 patches shipped March 19, but no future fixes are coming. How platform teams should pick a migration path.
Tailscale Peer Relays Behind Azure NAT Gateway: Why the DERP Fallback Hides a Throughput Cliff
Azure NAT Gateway silently forces Tailscale into DERP relay fallback, capping throughput. A Peer Relay in a public subnet with a static UDP endpoint restores direct-path.
vLLM Block-Level Preemption and FlexKV Shift the Long-Context Bottleneck From GPU Memory to PCIe
vLLM v0.19 block preemption and v0.18 FlexKV shift the long-context bottleneck from GPU memory to PCIe and CPU cache, but require experimental flags and carry unresolved.
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.