01
Why AI Infrastructure Is a Career Move Right Now
Learn why reliable model serving sits at the intersection of software, distributed systems, performance engineering, and business outcomes.
8 minPractical field guide
Field notes
Models to production serving
LLM Infrastructure From First Principles
A practical path from models, tokens, and GPU sizing to production inference, intelligent routing, and Kubernetes.
- Built for
- AI engineers, platform teams, and technical leaders
- Path length
- 9 chapters · 79 minutes
02
How ChatGPT and Large Language Models Work
Follow a request from text through tokens, transformer computation, probability sampling, and the production systems around an LLM.
9 minPractical field guide
03
How AI Models Are Stored and Why Hardware Limits Matter
Understand checkpoints, precision, runtime memory, model provenance, and why many large models exceed a typical laptop without oversimplifying the tradeoffs.
8 minPractical field guide
04
GPU Sizing for LLM Inference Without Guesswork
Size inference capacity using weight memory, active-token demand, and service objectives while accounting for GPU compute, VRAM, and bandwidth.
9 minPractical field guide
05
From PyTorch Weights to a vLLM Model Server
Understand the boundary between model code and a production inference server, then design a controlled path from artifact to API.
9 minPractical field guide
06
Prefill, Decode, TTFT, and TPOT Explained
Connect the two phases of language-model inference to user-visible latency, capacity tests, and practical performance diagnosis.
8 minPractical field guide
07
KV Cache, Prefix Caching, and Continuous Batching
Learn how LLM servers reuse attention state, combine active requests, protect tenant boundaries, and balance latency against throughput.
9 minPractical field guide
08
Distributed LLM Inference, Sharding, and Routing
Choose the right parallelism and route requests using real workload cost, cache locality, and failure boundaries instead of replica counts alone.
9 minPractical field guide
09
Running llm-d on Kubernetes in Production
Turn the series into a deployment plan using llm-d routing, Well-Lit Paths, Kubernetes workload choices, observability, and scoped hands-on labs.
10 minPractical field guide