Footer

Your go-to destination for discovering trending open-source projects and uncovering the insights that matter

Legal

  • Terms and conditions
  • Privacy policy

More

  • Pricing
  • Advertise
  • onedump
  • gosnakego
  • Contact us

Social

  • X (Twitter)
  • GitHub
Trendshift owl
What they build speaks louder than words
© 2026 Trendshift, created by Julian Li with ❤️
SignalAdvertise
Trendshift
SignalAdvertise
Sign in
Trendshift owl

Discover trends that matter

SignalAdvertise
DailyWeeklyMonthlyYearly
Live mentionsTopics
RepositoriesDevelopers
InsightsStats

Bifrost
Bifrost

Prasannajaga/inference-book

everything you need to know about inference engineering

#Learning resources#Curated list
Visit GitHub
Jupyter Notebook
44
3
1 contributors
AchievementsView all →
FeaturedAdvertise to 180K+ monthly visitors
Bifrost

Fastest Enterprise AI Gateway for Routing, Governing, and Securing all your AI Traffic

Momentum

How fast Prasannajaga/inference-book is gaining stars, and where it lands on Trendshift and GitHub Trending

Activity and trending

Stars, forks and merged pull requests each day or month, lined up with the repository's rank on Trendshift Trending

Sep 20 – Nov 4, 2026

Stars +5 in 46 days

01
1

Forks +0 in 46 days

01

Merged PRs 0 in 46 days

01

Trendshift Trendingwhere the repository ranked each day it made the top 25

Not in the Trendshift top 25 in this period

Sep 20Sep 26Oct 2Oct 8Oct 14Oct 20Oct 26Nov 1
The left and right arrow keys step from day to day; Home and End go to the first and the last

Stars, forks, merged pull requests and Trendshift Trending rank, by day
DayStars gainedForks gainedMerged PRs
Sun, Sep 20, 2026+1+00
Mon, Sep 21, 2026+1+00
Tue, Sep 22, 2026+0+00
Wed, Sep 23, 2026+0+00
Thu, Sep 24, 2026+0+00
Fri, Sep 25, 2026+0+00
Sat, Sep 26, 2026+0+00
Sun, Sep 27, 2026+0+00
Mon, Sep 28, 2026+0+00
Tue, Sep 29, 2026+0+00
Wed, Sep 30, 2026+0+00
Thu, Oct 1, 2026+0+00
Fri, Oct 2, 2026+0+00
Sat, Oct 3, 2026+1+00
Sun, Oct 4, 2026+0+00
Mon, Oct 5, 2026+0+00
Tue, Oct 6, 2026+0+00
Wed, Oct 7, 2026+0+00
Thu, Oct 8, 2026+0+00
Fri, Oct 9, 2026+0+00
Sat, Oct 10, 2026+0+00
Sun, Oct 11, 2026+0+00
Mon, Oct 12, 2026+0+00
Tue, Oct 13, 2026+0+00
Wed, Oct 14, 2026+0+00
Thu, Oct 15, 2026+0+00
Fri, Oct 16, 2026+0+00
Sat, Oct 17, 2026+0+00
Sun, Oct 18, 2026+0+00
Mon, Oct 19, 2026+0+00
Tue, Oct 20, 2026+0+00
Wed, Oct 21, 2026+1+00
Thu, Oct 22, 2026+0+00
Fri, Oct 23, 2026+0+00
Sat, Oct 24, 2026+0+00
Sun, Oct 25, 2026+0+00
Mon, Oct 26, 2026+0+00
Tue, Oct 27, 2026+0+00
Wed, Oct 28, 2026+0+00
Thu, Oct 29, 2026+0+00
Fri, Oct 30, 2026+0+00
Sat, Oct 31, 2026+0+00
Sun, Nov 1, 2026+1+00
Mon, Nov 2, 2026+0+00
Tue, Nov 3, 2026+0+00
Wed, Nov 4, 2026+0+00
Total+5+00
  • 1#1 on the list
  • 7Rank that day
  • Not trending

Mentions and achievements

Where people are talking about Prasannajaga/inference-book, and every badge it has earned
  1. Jul 261 post
    • Day 4/30 of inference infrastructure series Vllm & sglang today we look at how actual LLM serving engines execute requests in production focusing on what happens inside a single request in a serving…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

  2. Jul 251 post
    • Day 3/30 of inference infrastructure series Kv cache & Quantization Part 1: kv cache: the state behind LLM inference last day on day 2 we broke down how prefill and decode run on the gpu prefill…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

  3. Jul 241 post
    • Part 2: Decode In decode, the GPU generates one token at a time autoregressively in a loop, at each step, the model takes your newly generated token and runs another forward pass to predict the next…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

  4. Jul 232 posts
    • Day 1/30 of inference infrastructure series Part 1: Request Lifecycle The request lifecycle shows you how your query goes through the server infrastructure the big difference today is theres no…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

    • I dont know whether this is the new algorithm update or just the ragebait title, but this post blew up and became my highest engagement post yet! A small intro about me, Im an inference engineer…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

  5. 14 days earlier
  6. Jul 91 post
    • I found some interesting case when benchmarking GEMV kernels you calculate bandwidth using: Bandwidth (GB/s) = Logical Memory Bytes / (Latency in seconds * 10^9) but when your FP16 matrix is, for…

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

  7. Jul 31 post
    • kernel optimization is all about the how you move the data in GPU threads efficiently! if you someone into kernels, would love to connect

      Read full on X, posted by @jaga_prasanna

      @jaga_prasanna · x.com

You might also like

Repositories close to Prasannajaga/inference-book
FeaturedAdvertise to 180K+ monthly visitors
Bifrost

Fastest Enterprise AI Gateway for Routing, Governing, and Securing all your AI Traffic

modular/llm-inference-handbook

TypeScript

Everything you need to know about LLM inference

47555

elizabetht/100-days-of-inference

Jupyter Notebook

100 days of LLM inference engineering — daily posts, experiments, and visualizations

1.9k255

amitshekhariitbhu/llm-inference-engineering

Markdown

Learn LLM Inference Engineering step by step - from KV cache, PagedAttention, and continuous batching to vLLM, SGLang, and GPUs.

39651

aerlabsAI/ai-inference-resources

Curated collection of AI inference engineering resources — LLM serving, GPU kernels, quantization, distributed inference, and production deployment. Compiled from the AER Labs community.

36345

liulanze/awesome-ai-infra

1067

harshuljain13/llm-inference-at-scale

Jupyter Notebook

A Practitioner handbook for production llm serving.

27256

orca3/llm-model-inference

Jupyter Notebook

Source code repo for book: Hands-On LLM Serving and Optimization: Hosting LLMs at Scale

14754

harleyszhang/llm_note

Python

LLM notes, including model inference, transformer model structure, and llm framework code analysis notes.

89691

navaneethkrishnansuresh/Inference-Engineering

Jupyter Notebook
14413