Julien Chaumond's Recent LinkedIn Posts

the shift is real: in 2026, invest in AI builders 🔥

LLM Rig >> Mining Rig

We launched Storage Buckets, our alternative to S3 optimized for AI/ML, just five days ago, and we already host > 200 TB total storage size 🔥 let's go!! The cheapest, most convenient cloud storage for the AI/ML community 💪

With OAI v. Anthropic becoming a political choice, let me reiterate something: We DON'T want to have to choose between 2 model providers We want to choose between 1,000s of model providers

Super happy to officially welcome Arcee AI (the makers of uber-popular Trinity models) as early customers of our new Storage product on the Hugging Face Hub 🔥 Arcee are already hosting 250+ TB private datasets and feeding them into their training loops, at blazing speeds! GG Mark McQuade Lucas Atkins and team and looking forward to the next generation of models ⚡️

your regularly scheduled reminder that

If you like Claude Code/Codex and have at least 32GB of RAM: please run Qwen3.5-35B-A3B locally. There's a before and after for local agents: reliable tool calling, stable agentic loops, only 3B active params. Punches way above its weight! Now is the best time to get started with local models.

Precisely three years ago today Georgi Gerganov wrote his "inference at the edge" Manifesto Things have changed quite a lot in the field in the past 3 years yet many of the main points are still valid: • The strongest points of the current codebase are it's simplicity and efficiency. Performance is essential. • I would be really happy to see developers join in and help advance further the idea of "inference at the edge". • It's early to build a full-fledged edge inference framework. The code has to remain simple and compact in order to allow for quick and easy modifications. This helps to explore ideas at a much higher rate. Bloating the software with the ideas of today will make it useless tomorrow. • The AI models are improving at a very high rate and it is important to stay on top of it. The transformer architecture in it's core is very simple. There is no need to "slap" complex things on top of it. Of course: • This project will remain open-source And finally: • And most of all, it's important to have fun in the process! Here's to the next 3 years ggml! 🔥

Seen in Singapore airport Happy for HF to support those 1 billion Qwen downloads (and counting)🔥🔥 (via r/localllama)

BREAKING NEWS: Google just re-entered the game 🔥🔥 They want to take the crown 👑 back from Chinese open source AI. And... Gemma 4 is FINALLY Apache 2.0 aka real-open-source-licensed. From what I've seen it's going to be a pretty significant model. But give it a try yourself today: brew upgrade llama.cpp # you might need to install from source until build 8637 is in your package manager later today: brew install llama.cpp --HEAD 🔴 My personal recommendation: if you have at least 24GB of RAM or VRAM, run the (very good) 26B MOE: llama-server -hf ggml-org/gemma-4-26B-A4B-it-GGUF:Q4_K_M if you have 16GB of RAM or VRAM, run the dense E4B: llama-server -hf ggml-org/gemma-4-E4B-it-GGUF:Q8_0

Dataset Editing has landed for Parquet Datasets on the HF Hub ✍️ gg Caleb Fahlgren 🔥

I am so happy to announce that ggml / llama.cpp are going to join the HF family ❤️🔥 Georgi Gerganov (🐐) and team are joining HF with the goal of scaling and supporting the community behind ggml and llama.cpp as Local AI continues to make exponential progress in the coming years. We've been working with Georgi and team for quite some time (we even have awesome core contributors to llama.cpp like Xuan-Son and Aleksander in the team already) so this has been a very natural process. llama.cpp is the fundamental building block for local inference, and transformers is the fundamental building block for model definition, so this is basically a match made in heaven. 🔥 Our shared long-term goal is to provide the community with the building blocks to make open-source superintelligence accessible to the world over the coming years. Onwards!

We are hiring for a data/storage focused technical advocate at Hugging Face in case you know someone great, hit me up!

Ok let's go, training my first model using Unsloth AI Studio!!! (A100 80GB, there was no H100 available on Colab Pro) Once it's finetuned, just hit "Quantize" (select your favorite quants) then "Push to Hub". Voila!

llama.cpp just hit 100k stars on GitHub 🔥 Here's a bold prediction: within 18 months, 90% of all AI agents will be running locally on your own machine. Here's why local agents are inevitable:  → Models keep getting more efficient. MoE architectures like Qwen3.5-35B-A3B give you frontier-level quality while only activating 3B params at inference.  → Quantization keeps getting better. The work from Unsloth AI among others is outstanding.  → Apple Silicon and MLX made local inference fast and accessible. 64, then 128GB of unified memory will go a long way.  → Agents don't just need 200 tok/s. They also need reliable tool use, good reasoning, and long context — all things local models are getting great at. Your data never leaves your machine. No API calls, no rate limits, no vendor lock-in. 2026 is the year of Local Agents.

the HF team f-ing COOKED 😮 Today, we are launching a S3 c̶o̶m̶p̶e̶t̶i̶t̶o̶r̶ alternative, called 'Storage Buckets' -- optimized for AI/ML Useful for: • assembling training or fine-tuning data • streaming data directly into your training loop, fast • agentic storage (remote object storage where your agents can work) • ... • and interoperable with HF models and datasets (you can instantly move files between repos) It's not git-backed, freeing you from some of the constraints of git, while still being Xet-based hence high performance upload and download speeds 🔥🔥 Thanks to our awesome private beta partners for kicking the tires of Buckets in the past few weeks: - Jasper - Arcee AI - IBM - PixAI The feature is now available to everyone (user and organization) on the Hugging Face Hub 🎉

I really really wonder what Meta AI is up to these days. I feel they might either ship the mother of all AI bangers or fade themselves into oblivion.

Appropriate 2026 weekend activity: cardio while watching Dario Amodei You’ve got to still be fit enough when AGI hits!

There's a new file format on Hugging Face called Optimized-parquet 🔥 Here is why it's amazing and how it works: Optimized-parquet uses Parquet content-defined chunking under the hood. Parquet CDC cuts pages at locations that depend on the content: this makes pages boundaries very likely the same across different files 🎯 This is crucial because pages are the unit block for compression. Optimized-parquet is the perfect format for content-adressable storage, like Xet on HF It enables fast deduped dataset downloads + uploads and editing So in summary: Optimized-parquet = CDC + page index - CDC for Xet compat - page index to enable random access and associate parquet pages to Xet chunks for Parquet surgery / diffing

Just shipped! Hugging Face storage add-ons 📦 Starting at $12/month per TB - 3x cheaper than regular cloud storage, with very fast uploads and downloads powered by Xet's deduplication. You can now buy, upgrade, and cancel storage plans directly from your billing settings (for both users and organizations).

HF team members Aritra Roy Gosthipaty and Sayak Paul are building a ML Club India 🇮🇳 What they are planning: 1. Online talks 2. IST compatible timing 3. Open to all They have just locked in the first guest for the series. Some updates: 1. The talk is scheduled on 10th of March 2. It is going to be online, (links will be made available) 3. 2000 HRS IST is the set time. Follow Aritra and Sayak for further updates! 🔥

Super happy to announce that LanceDB and Hugging Face are partnering up to unlock the next generation of large dataset storage on the Hub 🔥 What is the Lance dataset format? It's an open-source data format optimized for ML and AI workloads: - Supports storing embeddings (and their indexes) directly alongside the data - Vector search / similarity search is built-in - Large multimodal datasets (text, images, video) One more thing... Fast random access at scale thanks to the HF Hub, just use the hf:// prefix: db = lancedb. connect("hf://datasets/julien-c/hub-stats-lance") This is fire! GG Chang She Prashanth Rao and team 🔥🔥

I’m franckly shocked that not many more American startups and big tech have noticed the massive opportunity and gap in the market for American open-source AI I think we're about to see a cambrian explosion of open source AI in the coming months, we're here for it 🔥

Isometric 3D views for the win 🤓 New on Hugging Face, your settings now include a Repositories page where you & your orgs can visualize repository storage consumption. 🔥 This update makes it easier to monitor usage, understand how storage is distributed across repositories, and manage resources more effectively.

Many of you asked for this over the past few weeks... Today we are introducing HF-MOUNT 🔥 → Mount any repo from the Hub (Storage Buckets, but also models, datasets) as a local filesystem This is a game changer, as it allows you to attach remote storage that is 100x bigger than your local machine's disk. Read-write for Storage Buckets, read-only for models and datasets. Here's an example with FineWeb-edu (a 5TB slice of the Web): 1️⃣> hf-mount start repo datasets/HuggingFaceFW/fineweb-edu /tmp/fineweb It takes a few seconds to mount, and then: 2️⃣> du -h -d1 /tmp/fineweb 4.1T ./data 1.2T ./sample 5.3T . 🤯😮 Two backends are available: NFS (recommended) and FUSE This is also perfect for Agentic storage!! Hugging Face

get PRO on Hugging Face and instantly 10x your storage to 1 TB private + 10 TB public ...for $9 a month 😮 a deal this good should be illegal