Morgan

Lead ML research engineer @kog__ai Ex tech cofounder of a startup.

[Morgan @morgangiraud]

After quite a while and some pretty hardcore engineering, we are out!

I’m really proud to be part of the team!

Reaching this milestone from the ground up was an intense journey!🔥

Check out our tech preview: https://t.co/5phD3Axm6T

[Kog @Kog__AI]

🚀 Launch today: Kog generates 3,000+ output tokens/s per single request, on standard datacenter GPUs.

We are bringing real-time LLM inference to hardware that companies already run in production.
The speed previously associated with purpose-built silicon is now delivered on NVIDIA H200 and AMD MI300X.

Today, we are opening our Tech Preview with a 2B coding model, with large frontier MoE support coming next.

Try our Playground → https://t.co/Rk9twi7Zdk

💥 Why that matters, and how we did it → https://t.co/5WjgKGMuUS

📖 Monokernel deep dive → https://t.co/ivMYGMO18v

📖 Delayed Tensor Parallelism research → https://t.co/5yLMYQI2k8

read the thread 👇

[Ciaran@ICML🇰🇷 @ciaran_regan_]

New paper with @kenneth0stanley !

We built a scalable fluid engine for artificial life 🧬

Paper: https://t.co/JTUv3vakb0

[Steeve Morin @steeve]

We sat down with @ylecun for a 1h30 discussion on world models @medjawii and JB Kempf of @videolan/@FFmpeg.

It's probably the deepest technical discussion on the subject ever recorded. And we added English subtitles.

Link in comment 👇

[Morgan @morgangiraud]

@ls_brd @ClementDelangue @huggingface Batch size 1 😊

[clem 🤗 @ClementDelangue]

Kog open-sourced on @huggingface the 2B model that they used to show a model running at 3,000+ tokens per second. Very cool work! https://t.co/fjCnAwQoWe

[Sam Wasserman @SamJWasserman]

Just watched the ai-native short film "A Face Only a Mother Could Love" by @rgaudetteai. Congrats Robert, what a charming, touching and wonderful example of what can be created with AI that otherwise may not have been made. Look forward to seeing what you make next.

[Arthur Mensch @arthurmensch]

We somehow got put in the spotlight the last few days! First we'd like to thank the organizers of the AI show for that, we can't get enough of this stuff. I'll say a few things about where we are and what we do.

[Kog @Kog__AI]

3,000 tokens/s inference speed pulls developers in. Our launch last week proved it.

Our post hit the Hacker News front page and stayed for 12 hours.

13,800 engineers read the Kog Labs technical breakdown.

2,240 developers tested our live playground, with a whooping 75% activation rate.

More than 4 million tokens generated across thousands of conversations at an average generation speed of ~3,200 tokens/s.

When inference is fast enough to feel different, developers come and build.

Read our technical blog posts and test it by yourself.

Try the playground → https://t.co/1ePyXNCerC

💥Why 3,000 tokens per second matters and how we got there → https://t.co/Y4dQcwerja

📖 Deep dive into the monokernel architecture on AMD MI300X → https://t.co/qsbXp69GCZ

📖 Delayed Tensor Parallelism, our approach to removing inter-GPU communication overhead → https://t.co/nJOQ6aoma2

[Christopher Potts @ChrisGPotts]

We take for granted that larger models are better than smaller ones, but why is this so? Our new paper, led by Jing Huang and @EkdeepL, traces this to a data-induced competition for resources (neurons), using formal analysis, idealized tasks, and real pretraining.

[Kog @Kog__AI]

🚀 Launch today: Kog generates 3,000+ output tokens/s per single request, on standard datacenter GPUs.

We are bringing real-time LLM inference to hardware that companies already run in production.
The speed previously associated with purpose-built silicon is now delivered on NVIDIA H200 and AMD MI300X.

Today, we are opening our Tech Preview with a 2B coding model, with large frontier MoE support coming next.

Try our Playground → https://t.co/Rk9twi7Zdk

💥 Why that matters, and how we did it → https://t.co/5WjgKGMuUS

📖 Monokernel deep dive → https://t.co/ivMYGMO18v

📖 Delayed Tensor Parallelism research → https://t.co/5yLMYQI2k8

read the thread 👇

[Morgan @morgangiraud]

@TomOlivier I can finally give you a public link 😎

[Morgan @morgangiraud]

After quite a while and some pretty hardcore engineering, we are out!

I’m really proud to be part of the team!

Reaching this milestone from the ground up was an intense journey!🔥

Check out our tech preview: https://t.co/5phD3Axm6T

[Kog @Kog__AI]

🚀 Launch today: Kog generates 3,000+ output tokens/s per single request, on standard datacenter GPUs.

We are bringing real-time LLM inference to hardware that companies already run in production.
The speed previously associated with purpose-built silicon is now delivered on NVIDIA H200 and AMD MI300X.

Today, we are opening our Tech Preview with a 2B coding model, with large frontier MoE support coming next.

Try our Playground → https://t.co/Rk9twi7Zdk

💥 Why that matters, and how we did it → https://t.co/5WjgKGMuUS

📖 Monokernel deep dive → https://t.co/ivMYGMO18v

📖 Delayed Tensor Parallelism research → https://t.co/5yLMYQI2k8

read the thread 👇

[Andrew Gordon Wilson @andrewgwils]

How much does a language model forget when finetuned on new tasks? We show both model size and optimization matter and forgetting can be nearly eliminated with self-generated replay!
https://t.co/Qs9A4n095s
w/@mrtnm @dongkyucho @ShikaiQiu @rumichunara @Pavel_Izmailov 1/8

[Morgan @morgangiraud]

Always been amazed by how much performance is left on the table by mainstream framework.👀👀

[Gaël Delalleau @gaeldelalleau]

Learn how Kog AI utilizes creative GPU Engineering optimizations to build a real-time LLM inference engine on AMD Instinct GPUs.

This is the video recording of our talk at AMD AI DevDay 2026 in SF two weeks ago.

Ping me directly for the slides 😊
Feedbacks welcomed.

https://t.co/jS9Epm8TSr

[Assemblée nationale @AssembleeNat]

🔴 Les dépendances structurelles et les vulnérabilités systémiques dans le secteur du numérique

🎙️ Audition d'@arthurmensch, cofondateur et directeur général de Mistral AI, et @AHerblin, directrice des affaires publiques et de la communication.
#DirectAN https://t.co/gajCheKlkQ

[Adithya S K @adithya_s_k]

Excited to release the Ultimate guide to RL environments!

Definitions of RL environments differ wildly in the LLM era, so we spent the last month building several RL environments across 6 different frameworks, domains and complexities to map out which are easiest to build with and which can be scaled to 1000s.

[John Yang @jyangballin]

How much of SQLite, FFmpeg, PHP compiler can LMs code from scratch? Given just an executable and no starter code or internet access.

Introducing ProgramBench: 200 rigorous, whole-repo generation tasks where models design, build, and ship a working program end to end. 🧵

[Seb Johnson @SebJohnsonUK]

A European startup that built a foundational model with only $9m has just been acquired for $1bn+.

The company - @prior_labs, has built a state-of-the-art foundation model for tabular data.

It was founded by @FrankRHutter, @noahholl and Sauraj Gambhir, and only announced a $9m pre-seed led by @balderton (@Jameswise) last year.

Tabular data, i.e. structured data in tables, spreadsheets, and databases, plays an essential role is many critical industries, but was neglected in the early advances in AI that focused on text and images.

Today SAP has announced that it is acquiring the company for $1bn+.

To date Prior Labs has only raised $9m which means it will likely be a great result for its founders, employee and early investors who include Balderton, @guypod, @Thom_Wolf, @petersarlin.

It's great to see a good exit for the German tech ecosystem and even better to see it staying in Europe!

It also shows how much there is to play for in AI. The team have built an insanely high quality, hyper-focused model and have got a unicorn outcome on just under a year.

Amazing news!

[David Bessis @davidbessis]

Thank you, everyone, for the incredible feedback on "the fall of the theorem economy"!
The subject is of course bigger than just AI and math—it's about the future of human cognition. A few remarks that didn't make it to the published version:⤵️

[AI at AMD @AIatAMD]

Roll call! Who's here for AMD #AIDevDay?

[Ineffable Intelligence @IneffableLabs]

Introducing Ineffable Intelligence. Led by David Silver, we're assembling the best engineers and researchers in the world to make first contact with superintelligence. We’ll be solving the hardest problems in AI on the way. Come join us.

https://t.co/zUuvPJGmcq