Annapurna Labs
Circuit mix
Where Annapurna Labs shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Cloud providers100%4 events
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
| Conference | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|---|---|---|---|---|
| AWS re:Invent |
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Where they've grown or pulled back
Annapurna Labs has never sponsored a tracked conference, so this follows where its speakers appear instead.
FlatHad speakers at 1 conference in 2026, flat from 1 in 2022.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. A conference counts as no longer seen only when later events published speaker lists.
Not on the company map: lookalikes are computed only for companies present at four or more conferences.
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 0 this company sponsored).
No co-sponsors: this company has no recorded sponsorships, or sponsored alone.
Sponsorships 0
No sponsorships recorded for Annapurna Labs.
Speakers 12
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Congkai Tan | AWS re:Invent 2026 | workshop | Optimize smarter: EKS performance with APerf and AI-powered analysis | — |
| Congkai Tan | AWS re:Invent 2026 | code talk | Aperf: An all-in-one tool for performance data collection and analysis | — |
| Esha Lakhotia | AWS re:Invent 2026 | chalk talk | Optimizing model performance with agentic loops on AWS Trainium | — |
| Louise Ping | AWS re:Invent 2026 | chalk talk | Build production AI assistant with Hugging Face Libraries and models | — |
| Maen Suleiman | AWS re:Invent 2026 | chalk talk | Beyond fine-tuning: RL for reasoning agents with AWS Trainium | — |
| Maen Suleiman | AWS re:Invent 2026 | breakout | The Neuron Stack: Enabling open source ML solutions on AWS Trainium | — |
| Pinak Panigrahi | AWS re:Invent 2026 | code talk | Build nanochat models with PyTorch and Trainium-Easier than you think | — |
| Pinak Panigrahi | AWS re:Invent 2026 | workshop | Scaling Frontier AI model inference on AWS Trainium | — |
| Pinak Panigrahi | AWS re:Invent 2025 | workshop | Inference on AWS Trainium and Amazon EKS using vLLM | — |
| Pinak Panigrahi | AWS re:Invent 2025 | chalk talk | Deploying LLMs at scale with vLLM on AWS AI chips | — |
| Emily Webber | AWS re:Invent 2024 | code talk | Build & optimize models on AWS Trainium using Neuron Kernel Interface | — |
| Kamran Khan | AWS re:Invent 2022 | breakout | Sustainability and AWS silicon | Slides · Video |