Dwarak Rajagopal

VP/Head of AI Eng @ Snowflake, ex-{Google, FB/Meta, Uber, Apple, AMD}

[Dwarak Rajagopal @dwarak]

I’m excited to share new research work from the Snowflake AI Research Team focused on advancing enterprise AI systems.

Arctic-Text2SQL-R2 is a reasoning model designed for enterprise SQL generation. Trained on Snowflake-native data and optimized for real-world enterprise SQL workloads, the specialized model outperforms larger frontier models on difficult SQL benchmarks despite being 30–150x smaller than other high-performing models.

To make specialized models like Arctic-Text2SQL-R2 practical at scale, the team also introduced ZoRRo (Zero Redundancy Rollouts), a set of optimizations that eliminate redundant computation in long-context RL workflows. ZoRRo accelerated RL training by up to 3.5x, reducing runtime from over five days to only 1.5 days. It also reduced memory consumption enough to support 3.2x longer context windows, enabling more efficient training on complex enterprise reasoning workloads.

Together, this work demonstrates how the next wave of enterprise AI innovation will be driven by both stronger domain-specific models and more efficient training systems. Read more in the blog posts in the comments:

Arctic-Text2SQL-R2: https://t.co/vnD4OVhISV
ZoRRo: https://t.co/w0PxipLHPn

@yao_zhewei @yuxionghe @samyamrb @jeffra45 @StasBekman

[sridhar @RamaswmySridhar]

Conference didn't share the attendee list until I arrived. No problem.

Pasted the CEO list into @SnowflakeDB Intelligence. 2 minutes later: customers, prospects, ARR — all mapped.

This is what AI + your own data looks like in practice. Not a demo. Not a deck. Just answers.

[Dwarak Rajagopal @dwarak]

Snowflake Intelligence updates for administrators blog post: https://t.co/UKQU0joO0M

Snowflake Intelligence updates for business users blog post:
https://t.co/2vwZdIxBe1

Cortex Code updates blog post:
https://t.co/Or6K1rQm2c

[Dwarak Rajagopal @dwarak]

AI systems are moving from answering questions to taking action. The challenge is making that work in practice across fragmented data, enterprise systems, and AI models.

Today, @Snowflake announced updates to Snowflake Intelligence and Cortex Code to support how these systems are built and run in practice.

Snowflake Intelligence is evolving into a personal work agent for business users that can reason over governed data and take action across systems. Cortex Code expands the builder layer, enabling teams to develop, orchestrate, and operationalize AI across the enterprise data ecosystem.

Together, they create a centralized approach for both business and technical users to govern, connect, and orchestrate their data, models, and enterprise apps — cementing Snowflake as the control plane for enterprise AI.

[sridhar @RamaswmySridhar]

CoCo is quickly becoming a force multiplier for our customers and partners alike.

They're not just experimenting. They’re transforming how work gets done.

The speed, the productivity gains, the shift in how teams build—it’s real, and it’s happening now.

Don't take my word for it:

— Trent Foley of @letsevolv: Cortex Code has become the core infrastructure for how we scale and drive adoption. A single integration session executed 2,500 automated actions... that's 5-8x productivity. Weeks of manual development happening in hours.

— Leading Automotive Dealership Group: We’ve been digging away with shovels for years, and now Snowflake just showed up with excavators. It’s easily the most practical and well-developed AI tool we’ve seen.

— Vibhor Gupta of @Shelter_Ins: Cortex Code reduces friction in everyday data and AI development while maintaining the oversight we need in a regulated environment.

[Dwarak Rajagopal @dwarak]

We’re seeing a clear shift in how teams build with AI, moving from isolated assistance to deeply integrated, agentic workflows.

With @Snowflake's latest updates to Cortex Code, we’re making that shift tangible.

❄️Cortex Code is now generally available in Snowsight, with a persistent AI coding agent embedded directly in the data workflow.

💻Cortex Code CLI now supports Windows, expanding access for developers working across different environments.

🤖Agent Teams enable coordination of complex, multi-step tasks by running work in parallel.

The result: faster iteration, tighter feedback loops, and the ability to take on significantly more ambitious data and AI workloads, without adding complexity.

Read more in the blog post below:
https://t.co/wDQyO2lHYB

[Dwarak Rajagopal @dwarak]

The shift to agentic enterprises requires grounding in trusted data, strong governance, and seamless action.

Project SnowWork brings this to life: autonomous agents for business users that respect controls, observe every step, and drive real results.

Excited to see this evolve → https://t.co/uKjZhAyApo

[Dwarak Rajagopal @dwarak]

Thrilled to share Jacobi Forcing from Snowflake AI Research—transforming autoregressive LLMs into parallel decoders via progressive distillation on generated trajectories, unlocking up to 4x inference speedup with near-AR quality preserved.

Trains models on Jacobi decoding trajectories with a progressive noise schedule, shifting AR models to efficient parallel decoders while retaining causal attention and KV-cache compatibility.

Achieves 3.8× wall-clock speedup on coding/math benchmarks (e.g., HumanEval, GSM8K) with minimal performance loss.

Introduces multi-block decoding and rejection recycling for 4.5× more tokens accepted per forward pass, outperforming diffusion LLMs by 7-53× in speed-quality tradeoff.

No architectural changes or draft models needed—seamless integration with existing serving systems.

Huge shoutout to the team: Lanxiang Hu, Siqi Kou, Yichao Fu, Tajana Rosing, Zhijie Deng, @samyamrb , @haozhangml , @yuxionghe
Paper: https://t.co/y9Ojiw3PQP
Code: https://t.co/rLlBsaw91h
#AI #LLMInference #MachineLearning

[Deedy @deedydas]

Today, I'm excited to launch my lifelong passion project, Grand Old Books!! 🚀

There are 1000s of beautiful novels of the past, not in English, locked up in old PDFs, with no physical copies left. We started with Indian texts and brought back 12 books in 6 languages with pictures and annotations.

This is, and will always be, completely free.

We can't let time wash away history.

Please comment to let me know what book you'd like to see added.

[Dwarak Rajagopal @dwarak]

Couldn't agree more— this is transforming data workflows in ways we only dreamed of. Game changer! ❄️

[sridhar @RamaswmySridhar]

Cortex Code CLI is the most amazing product I have used in a long time...

I have done everything from setting up an openflow pipeline to running an eval on agent that a colleague shared with me! It's a total game changer for data.

Proud of the team!

https://t.co/ntfikwjUrr

[sridhar @RamaswmySridhar]

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 + over a thousand more customers harness agentic AI.

🤝 Expanding impact through partnerships with @AnthropicAI, @SAP, @awscloud, @Accenture, @Workday, @PalantirTech, @splunk & @UiPath.

🚀 370 Product launches YTD (35% YoY), a record 615 new customers, and 40K+ #SnowflakeWorldTour attendees (40%+ YoY).

The best is yet to come. ❄️❄️❄️
https://t.co/NqyCMn7n44

[Stas Bekman @StasBekman]

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 going fast! https://t.co/MlohIUhFON

3. [Paper] SuffixDecoding: Extreme Speculative Decoding for Emerging AI Applications by Gabriele Oliaro - Friday, Dec. 6 @ 11am | Exhibit Hall C,D,E #816 - Learn more: https://t.co/EfnjTTx2JI

4. [Workshop] Arctic Inference: Breaking the Speed Cost Tradeoff in LLM Serving by [email protected] - Friday, Dec. 6 @ 6:25pm | Hard Rock Hotel - Register: https://t.co/bEHpjL6lTQ

5. [Jobs] We are hiring: https://t.co/RWMBWC0wYm

See you at the conference.

[Stas Bekman @StasBekman]

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 equivalent w/o coding anything.

You just won't get the shared expert in Next, but it's 512 vs 1, so it's quite negligible.

Just remember you'd have to adapt the number of tokens when comparing because compute per token will be different. Thanks to @samyamrb for this last insight since I originally completely missed it!

[Dwarak Rajagopal @dwarak]

@soumithchintala End of an era! It was so much fun working with you and the team during my time at Meta. Huge Kudos and all the very best to build the next big thing!

[Dwarak Rajagopal @dwarak]

@xianjun_agi @johnschulman2 Please reach out and happy to chat about opportunities at snowflake.

[Dwarak Rajagopal @dwarak]

@tydsh Please reach out and happy to chat on opportunities @Snowflake

[Dwarak Rajagopal @dwarak]

@cuijiaxun We are looking for RL researchers/engineers to join the team. Happy to chat Please dm me.

[Jeremy Howard @jeremyphoward]

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