A practical guide to the infrastructure changes required when moving from dense LLMs to sparse MoE architectures: expert parallelism, memory planning, network bandwidth, and vLLM optimizations for DeepSeek, Llama 4, and Qwen 3 MoE in production.
AWS S3 Vectors brings native vector indexing directly into object storage. Here's what it actually means for RAG architectures, when it beats purpose-built vector databases, and the cost numbers that should change your AI budget planning.
Reasoning models like o3 and Claude Extended Thinking change everything about LLM infrastructure. Here is how to route, cache, monitor, and not bankrupt yourself deploying them in production.
A practitioner's guide to AWS Trainium 3, Google Ironwood TPU, and Microsoft Maia: when hyperscaler custom AI silicon beats NVIDIA GPUs on cost and performance, and what it actually takes to migrate.
Prompt caching is the single highest-ROI optimization for LLM API costs. Here's how KV cache prefix reuse works, how to structure prompts for maximum cache hits, and what kills your hit rate without you realizing it.
GPU bills are the new egress tax. Learn how to build a real AI FinOps practice: from cost anatomy to model routing to chargeback, before your CFO starts asking questions.
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