Snowflake is at the center of the enterprise AI revolution, and our Q3 results show the momentum. 📈 Product revenue up 29% YoY to $1.16B, with RPO at $7.88B (37% YoY). 💡 Snowflake Intelligence marks our fastest product adoption ever, helping @TSImagine_ , @Fanatics & @USABS

Alright, quite a few things wrt Snowflake AI Research at @NeurIPS in San Diego this week 1. [Expo Booth] Come and talk to us and get a Snowflake T-shirt and swag 2. [Meetup] Snowflake x FastVideo - fireside conversations, food, light drinks - Thursday, Dec 5 @ 5pm - RSVP’s

Have you ever wondered by how much is your MoE implementation slower than its dense equivalent - let's say Qwen3-Next-80B-A3B and we want to compare its performance to its 3B dense equivalent which doesn't exist. Well, just set `config.num_experts=0` and voila, you get the dense

18 months ago, @karpathy set a challenge: "Can you take my 2h13m tokenizer video and translate [into] a book chapter". We've done it! It includes prose, code & key images. It's a great way to learn this key piece of how LLMs work. https://t.co/aSgsZz0VxO

AI doesn’t steal jobs. It hands you the keys to all of them.

In era of pretraining, what mattered was internet text. You'd primarily want a large, diverse, high quality collection of internet documents to learn from. In era of supervised finetuning, it was conversations. Contract workers are hired to create answers for questions, a bit

We published new speculative decoding models for gpt-oss-20b and gpt-oss120b! They are based on the LSTMs and make gpt-oss generation 1.6-1.8x faster 🚀 The speculator models are open-sourced and ready-to-run in Arctic Inference 👇

Our team trained and released Arctic Speculator, which improves vllm generation speed by 1.6-1.8x for OpenAI’s recent gpt-oss models. Check it out here: https://t.co/GBf2kUTSvR

Most takes on RL environments are bad. 1. There are hardly any high-quality RL environments and evals available. Most agentic environments and evals are flawed when you look at the details. It’s a crisis: and no one is talking about it because they’re being hoodwinked by labs

We are thrilled to announce that @OpenAI’s most advanced model, GPT-5, is now available natively on Snowflake Cortex AI for customers to use. This integration unlocks a wide range of enterprise use cases within Snowflake’s secure, governed environment: ❄️ Transform data into

🇺🇸 Today is a day we have been working towards for six months. We are announcing America’s AI action plan putting us on the road to continued AI dominance. The three core themes: - Accelerate AI innovation - Build American AI infrastructure - Lead in international AI

Today is a deep dive into sequence tiling compute. Sequence tiling massively reduces activation memory footprint and can be applied to computations w/o token inter-dependency. The plot shows a huge memory saving with tiled fused logits loss computation. See section 3.1 in our

Arctic Inference helps @allhands_ai complete real-world coding tasks 2x faster through faster LLM inference. Check it out!

As I'm diving into Sequence/Context parallelism in the last few days I wanted to share this write up in 2 parts that nicely compares the few approaches out there and some of their combinations with papers: p1: https://t.co/kUaocgRd78 p2:

Wrapped up Stanford CS336 (Language Models from Scratch), taught with an amazing team @tatsu_hashimoto @marcelroed @neilbband @rckpudi. Researchers are becoming detached from the technical details of how LMs work. In CS336, we try to fix that by having students build everything:

Shift Parallelism from @Snowflake AI Research is a game-changer! 🚀 3.4x faster LLM inference with Arctic + @vllm_project . Loving the throughput boost!

Arctic-Text2SQL-R1 outperforms BIRD by 11+ points, using simple rewards, strong reasoning, & scalable design. Enhances SQL generation for complex database queries in analytics platforms. https://t.co/4TYCwDZZha

Arctic Inference delivers 2x faster response times using Shift Parallelism, SwiftKV, & speculative decoding. Optimized for enterprise AI, it scales compute for real-time apps like recommendation systems. https://t.co/1kypypoaIg

16x faster embeddings in Cortex by optimizing serialization, tokenization, & GPU usage for text processing. Achieved 3x throughput over vLLM, cutting costs for scalable AI in search & analytics workloads. https://t.co/62tC2VJ6nI

🤝 A new era for AI development! @percyliang's Marin lab is redefining open-source AI with open development—fully transparent and collaborative. Join the movement! https://t.co/tH7yKgO68L #AIInnovation #OpenScience