# navaneethkrishnansuresh/Inference-Engineering

> A hands-on course on LLM inference engineering covering what happens when a model serves a request: tokenization, prefill, decode, KV cache, scheduling and batching. Aimed at developers who want to understand the runtime side of model serving.

- Magnitude: 0.0 out of 10 — Quiet
- Stars: 118 total · +0 stars on 2026-09-30 (UTC)
- Star trust: star growth looks organic
- Category: Language models · Language: Jupyter Notebook · Created: 2026-09-14 · Last push: 2026-09-25
- GitHub: https://github.com/navaneethkrishnansuresh/Inference-Engineering · Page: https://gitnova.dev/en/r/navaneethkrishnansuresh/Inference-Engineering

## Useful for

- Work through Chapter 1 to manually implement LLM token generation from first principles
- Study how KV cache grows and where GPU memory goes during inference
- Learn request scheduling and continuous batching to improve throughput

## Why it’s here

- The repository is 16 days old and already has 118 stars.

## Star trust

Star growth looks organic.

Star-trust labels are heuristics based on the repository’s behavior, not a check of every stargazer.

## Numbers

- Forks: 10
- Issues and pull requests: 4
- Watchers: 3
- Average over the last week: 1 per day
- Usual pace: 6 per day

## Stars per day, last 30 days (oldest → newest, today is partial)

2026-10-04 … 2026-11-02: None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None, None

## Magnitude by day, last 14 days

09-17 1.7, 09-18 1.1, 09-19 1.3, 09-20 0.6, 09-21 0.8, 09-22 1.0, 09-23 0.8, 09-24 0.5, 09-25 0.5, 09-26 0.7, 09-27 0.7, 09-28 0.4, 09-29 0.0, 09-30 0.0

---

Magnitude (0–10) measures how fast and how unusually interest in a repository is growing right now. It is not a quality score.
Days are UTC. “So far today” is a fact; “expected by the end of the day” is a forecast.
Summaries and use cases are written by an LLM (DeepSeek V4.1 Flash) from the README and may be inaccurate: verify specific claims (benchmarks, speed, hardware) in the repository itself.
Data as of 2026-09-30 23:58 UTC, updated every 30 minutes.