# AI use cases for LLM Inference Engineering (amitshekhariitbhu/llm-inference-engineering) Repo: [amitshekhariitbhu/llm-inference-engineering](https://github.com/amitshekhariitbhu/llm-inference-engineering) | Use cases: 4 | Page: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/ A free, step-by-step learning series of blog posts and videos on LLM inference engineering, covering topics such as KV cache, PagedAttention, continuous batching, and speculative decoding. This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL. - Learn how LLMs generate text one token at a time: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/learn-how-llm-text-generation-works/ - Pick a KV cache memory technique for long prompts: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/pick-a-memory-optimization-for-kv-cache/ - Choose a faster decoding method for slow responses: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/choose-a-decoding-speedup-technique/ - Understand how LLM servers handle many users at once: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/understand-serving-many-users-at-once/ ## How to learn how LLMs generate text one token at a time with LLM Inference Engineering Repo: [amitshekhariitbhu/llm-inference-engineering](https://github.com/amitshekhariitbhu/llm-inference-engineering) | Difficulty: Beginner | Time: 30 minutes | Basis: From the README Work through the Part 1 basics on autoregressive generation and the prefill and decode phases. **Who it is for:** A developer new to LLM serving who needs the core generation loop before reading about optimizations. **The problem:** Terms like prefill, decode, and autoregressive appear everywhere in inference docs, but they are hard to follow without the underlying loop. **The outcome:** A clear mental model of the generation loop, the two inference phases, and the metrics TTFT and TPOT. ### Steps 1. Open the Autoregressive Models post at https://outcomeschool.com/blog/autoregressive-models and read the generation loop and the numeric example. 2. Open the Prefill vs Decode post at https://outcomeschool.com/blog/prefill-vs-decode-llm-inference-optimization and read the comparison table. 3. Read the key metrics section on TTFT, TPOT, throughput, and end-to-end latency in the same post. 4. Write a short summary of the difference between prefill and decode in your own words. ### Prompt to paste into your agent ```text Using the amitshekhariitbhu/llm-inference-engineering repo, read the Part 1 section on and produce a one-page summary of its key terms. Verify that each term in the summary is defined in the linked Outcome School post. ``` ### Caveat The repo is a reading series that links to external blog posts; the README itself contains no code or runnable setup. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/learn-how-llm-text-generation-works/ ## How to pick a KV cache memory technique for long prompts with LLM Inference Engineering Repo: [amitshekhariitbhu/llm-inference-engineering](https://github.com/amitshekhariitbhu/llm-inference-engineering) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README Compare KV cache compression, PagedAttention, GQA, and Flash Attention to choose a memory approach. **Who it is for:** An ML engineer sizing GPU memory for a model that serves long conversations or many concurrent users. **The problem:** The KV cache grows with the conversation, and memory waste limits how many users one GPU can serve. **The outcome:** A short list of which memory technique fits a given serving problem, with the trade-offs for each. ### Steps 1. Open the KV Cache in LLMs post at https://outcomeschool.com/blog/kv-cache-in-llms to understand why the cache exists. 2. Open the KV Cache Compression post at https://outcomeschool.com/blog/kv-cache-compression and read the 'When to use which one' section. 3. Open the Paged Attention post at https://outcomeschool.com/blog/paged-attention-in-llms to see how it addresses memory waste. 4. Open the Grouped Query Attention post at https://outcomeschool.com/blog/grouped-query-attention and compare it with MHA and MQA. 5. Write a table with one row per technique listing the problem it solves and its main trade-off. ### Prompt to paste into your agent ```text Using the amitshekhariitbhu/llm-inference-engineering repo, compare the KV cache techniques for the workload and output a table with one row per technique. Verify that each row matches the 'Comparison of the approaches' and 'When to use which one' sections of the KV Cache Compression post. ``` ### Caveat The README gives explanations and links only; it does not show how to apply any of these techniques to a specific model or server. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/pick-a-memory-optimization-for-kv-cache/ ## How to choose a faster decoding method for slow responses with LLM Inference Engineering Repo: [amitshekhariitbhu/llm-inference-engineering](https://github.com/amitshekhariitbhu/llm-inference-engineering) | Difficulty: Intermediate | Time: 45 minutes | Basis: From the README Compare speculative decoding, Medusa, EAGLE, and n-gram speculation to pick one to study. **Who it is for:** An engineer whose LLM responses feel slow and who wants to know which speedup approach to investigate first. **The problem:** Several speculative decoding methods promise faster generation, and each needs different extra parts such as a draft model or extra heads. **The outcome:** A ranked shortlist of speedup methods with the main requirement and trade-off noted for each. ### Steps 1. Open the Speculative Decoding post at https://outcomeschool.com/blog/speculative-decoding and read the trade-offs section. 2. Open the N-gram Speculation post at https://outcomeschool.com/blog/n-gram-speculation-in-llms and read 'The cost of a draft model' and 'Where it works well and where it fails'. 3. Open the Decoding Medusa post at https://outcomeschool.com/blog/decoding-medusa to see the multiple-heads approach. 4. Open the Decoding EAGLE post at https://outcomeschool.com/blog/decoding-eagle to see drafting at the feature level. 5. Write a ranked list with one line each on the requirement and the main trade-off. ### Prompt to paste into your agent ```text Using the amitshekhariitbhu/llm-inference-engineering repo, compare the speculative decoding methods for the case and return a ranked list with requirement and trade-off for each. Verify that every claim about speedup in the list is taken from the linked blog posts. ``` ### Caveat The README reports that methods can give speedups but does not state a guaranteed gain for any particular model or hardware. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/choose-a-decoding-speedup-technique/ ## How to understand how LLM servers handle many users at once with LLM Inference Engineering Repo: [amitshekhariitbhu/llm-inference-engineering](https://github.com/amitshekhariitbhu/llm-inference-engineering) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README Read about continuous batching, streaming, and disaggregated serving to plan an LLM API. **Who it is for:** A backend engineer designing an API that streams LLM answers to many users at the same time. **The problem:** Static batching wastes GPU time, and streaming and separating prefill from decode each add design choices. **The outcome:** An understanding of how continuous batching, token streaming, and prefill-decode disaggregation fit together in a serving design. ### Steps 1. Open the Continuous Batching in LLMs post at https://outcomeschool.com/blog/continuous-batching-in-llms and read 'The Problem with Static Batching'. 2. Open the How does Token Streaming work? post at https://outcomeschool.com/blog/how-does-token-streaming-work and read 'SSE vs WebSockets'. 3. Open the Prefill-Decode Disaggregation post at https://outcomeschool.com/blog/prefill-decode-disaggregation and read 'Where it works well and where it is overkill'. 4. Write a one-page design note that says which of the three ideas your API needs and which it can skip. ### Prompt to paste into your agent ```text Using the amitshekhariitbhu/llm-inference-engineering repo, draft a one-page design note for an LLM API serving concurrent users. Verify that the note explains continuous batching, streaming, and disaggregation using only the linked posts. ``` ### Caveat The README mentions vLLM, SGLang, and GPUs only in its description and does not explain how to deploy any of them. Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/ URL: https://dedupedai.com/use-cases/amitshekhariitbhu-llm-inference-engineering/understand-serving-many-users-at-once/