Content Wave 128: AI Infrastructure & Deployment (June 2026)
Reviewed: June 4, 2026
Published: May 28, 2026 | Category: AI Infrastructure
Wave 128 covers the infrastructure layer of production AI: from model serving frameworks and edge deployment to cost optimization and multi-cloud strategy. These four articles provide a comprehensive guide to running AI workloads efficiently in 2026.
Articles in This Wave
The definitive comparison of the four major serving frameworks. Covers PagedAttention, RadixAttention, FlashAttention-3, and TensorRT-LLM with real-world benchmarks on A100 and H100 hardware. Includes a decision framework for choosing the right tool for your workload.
Reading time: 12 min | Key topics: vLLM, TGI, SGLang, TensorRT-LLM, PagedAttention, speculative decoding
From Mac Minis to Raspberry Pis — how to run LLMs on consumer hardware. Covers GGUF quantization, llama.cpp, Ollama, and real-world benchmarks on Apple Silicon, NVIDIA gaming GPUs, and ARM devices. Includes deployment patterns for local-first and hybrid architectures.
Reading time: 11 min | Key topics: GGUF, llama.cpp, Ollama, Apple Silicon, Raspberry Pi, quantization
A systematic framework for cutting AI inference costs. Covers quantization, semantic caching, model cascading, spot instances, and provider arbitrage. Includes a real-world case study showing 93% cost reduction from $25K to $1.8K/month.
Reading time: 10 min | Key topics: Quantization, caching, model cascading, spot GPUs, cost monitoring
Architecture and tooling for running AI workloads across AWS, GCP, Azure, and bare-metal. Covers Kubernetes federation, Terraform patterns, cross-cloud load balancing, and portable data pipelines. Includes decision criteria for when multi-cloud is (and isn’t) worth the complexity.
Reading time: 10 min | Key topics: Kubernetes, Terraform, KubeAI, multi-cloud, cost optimization
Wave Summary
| Article |
Key Takeaway |
| Model Serving |
vLLM for general workloads, SGLang for agents, TensorRT-LLM for max NVIDIA perf |
| Edge AI |
Q4_K_M quantization on consumer hardware delivers usable inference for 7B–14B models |
| Cost Optimization |
Systematic optimization achieves 80–93% cost reduction without quality loss |
| Multi-Cloud |
Kubernetes + Terraform + KubeAI provides a portable, provider-agnostic foundation |
Previous wave: Wave 127 — Embodied AI & Robotics
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