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Inference Engineering: Inside the Engine
What holds one model server back — arithmetic, memory bandwidth or memory capacity — and every serving technique as a way to move one of them.
0/31
31 lessons
- 1. What Inference Engineering Is6 min
- 2. From Text to Tokens to a Forward Pass5 min
- 3. The KV Cache and the Two Phases It Creates5 min
- 4. The Clocks: TTFT, ITL, TPOT and Throughput5 min
- 5. Goodput, Cost per Token and the Workload5 min
- 6. Embeddings, Attention and Q, K, V5 min
- 7. Why K and V Are Cached and Q Is Not: MQA and GQA5 min
- 8. Inside the Accelerator: Compute, Memory and the Bus Between5 min
- 9. Compute-Bound or Memory-Bound: The Roofline5 min
- 10. The Memory Budget: Weights, Cache and Headroom5 min
- 11. Kernels and FlashAttention: The Same Arithmetic, Less Traffic7 min
- 12. Quantization: Fewer Bits per Weight5 min
- 13. Quantizing and Shrinking the KV Cache5 min
- 14. Continuous Batching: Never Leave a Slot Empty5 min
- 15. Paged Attention: Memory Handed Out a Block at a Time5 min
- 16. Chunked Prefill and the Scheduler's Budgets5 min
- 17. Prefix Caching: Never Prefill the Same Tokens Twice5 min
- 18. Why Temperature Zero Is Not Deterministic5 min
- 19. Speculative Decoding: Guess Ahead, Then Check6 min
- 20. The Speedup Equation: How Far Ahead to Guess5 min
- 21. Where Drafts Come From, and When Speculation Loses6 min
- 22. Constraining and Marking Output8 min
- 23. Tensor Parallelism: One Model Across Several GPUs6 min
- 24. Pipeline, Data and Expert Parallelism5 min
- 25. Anatomy of an Engine: One Loop, Every Technique5 min
- 26. Datacenter Engines: vLLM, SGLang and TensorRT-LLM5 min
- 27. llama.cpp and Local Inference5 min
- 28. Load Testing and Tuning One Server6 min
- 29. Encoders, Vision Inputs and Speech5 min
- 30. Diffusion Models: A Different Loop5 min
- 31. Capstone: Tuning the Running Service5 min
- Key takeaways4 min
- How to get certified3 min
- Your certificate
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