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Snowflake has thousands of enterprise customers that use the company’s data and AI technologies. Though many issues with generative AI are solved there is still lots of room for improvement.
Two such issues are text-to-SQL query and AI inference. SQL is the query language used for databases and it has been around in various forms for over 50 years. Existing large language models (LLMs) have text-to-SQL capabilities that can help users to write SQL queries. Vendors including Google have introduced advanced natural language SQL capabilities. Inference is also a mature capability with common technologies including Nvidia’s TensorRT being widely deployed.
While enterprises have widely deployed both technologies, they still face unresolved issues that demand solutions. Existing text-to-SQL capabilities in LLMs can generate plausible-looking queries, however they often break when executed against real enterprise databases. When it comes to inference, speed and cost efficiency are always areas where every enterprise is looking to do better.
That’s where a pair of new open-source efforts from Snowflake are aiming to make a difference: Arctic-Text2SQL-R1 and Arctic Inference.
Snowflake’s approach to AI research is all about the enterprise
Snowflake AI Research is tackling the issues of text-to-SQL and inference optimization by fundamentally rethinking the optimization targets.
Instead of chasing academic benchmarks, the team focused on what actually matters in enterprise deployment. One issue is making sure the system can adapt to real traffic patterns without forcing costly trade-offs. The other issue is understanding if the generated SQL actually execute correctly against real databases? The result is two breakthrough technologies that address persistent enterprise pain points rather than incremental research advances.
“We want to deliver practical, real-world AI research that solves critical enterprise challenges,” Dwarak Rajagopal, VP of AI Engineering and Research at Snowflake told VentureBeat. “We want to push the boundaries of open source AI, making cutting edge research accessible and impactful.”
Why text-to-SQL isn’t a solved problem (yet) for enterprise AI and data
Multiple LLMs have had the ability to generate SQL from basic natural language queries. So why bother to create yet another text-to-SQL model?
Snowflake evaluated existing models to first see if in fact text-to-SQL was, or wasn’t, a solved issue.
“Existing LLMs can generate SQL that looks fluent, but when queries get complex, they often fail,” Yuxiong He, Distinguished AI Software Engineer at Snowflake explained to VentureBeat. “The real world use cases often have massive schema, ambiguous input, nested logic, but the existing models just aren’t trained to actually address those issues and get the right answer, they were just trained to mimic patterns.”
How execution-aligned reinforcement learning improves text-to-SQL
Arctic-Text2SQL-R1 addresses the challenges of text-to-SQL through a series of approach.
It uses execution-aligned reinforcement learning that trains models directly on what matters most: does the SQL execute correctly and return the right answer? This represents a fundamental shift from optimizing for syntactic similarity to optimizing for execution correctness.
“Rather than optimizing for text similarity, we train the model directly on what we care about the most. Does a query run correctly and use that as a simple and stable reward?” she explained.
The Arctic-Text2SQL-R1 family achieved state-of-the-art performance across multiple benchmarks. The training approach uses Group Relative Policy Optimization (GRPO). The GRPO approach uses a simple reward signal based on execution correctness.
Shift parallelism helps to improve open-source AI inference
Current AI inference systems force organizations into a fundamental choice: optimize for responsiveness and fast generation, or optimize for cost efficiency through high throughput utilization of expensive GPU resources. This either-or decision stems from incompatible parallelization strategies that cannot coexist in a single deployment.
Arctic Inference solves this through Shift Parallelism. It’s a new approach that dynamically switches between parallelization strategies based on real-time traffic patterns while maintaining compatible memory layouts. The system uses tensor parallelism when traffic is low and shifts to Arctic Sequence Parallelism when batch sizes increase.
The technical breakthrough centers on Arctic Sequence Parallelism, which splits input sequences across GPUs to parallelize work within individual requests.
“Arctic Inference makes AI inference up to two times more responsive than any open-source offering,” Samyam Rajbhandari, Principal AI Architect at Snowflake, told VentureBeat.
For enterprises, Arctic Inference will likely be particularly attractive as it can be deployed with the same approach that many organizations are already using for inference. Arctic Inference will likely attract enterprises because organizations can deploy it using their existing inference approaches.Arctic Inference deploys as an vLLM plugin. The vLLM technology is a widely used open-source inference server. As such it is able to maintain compatibility with existing Kubernetes and bare-metal workflows while automatically patching vLLM with performance optimizations. “
“When you install Arctic inference and vLLM together, it just simply works out of the box, it doesn’t require you to change anything in your VLM workflow, except your model just runs faster,” Rajbhandari said.
Strategic implications for enterprise AI
For enterprises looking to lead the way in AI deployment, these releases represent a maturation of enterprise AI infrastructure that prioritizes production deployment realities.
The text-to-SQL breakthrough particularly impacts enterprises struggling with business user adoption of data analytics tools. By training models on execution correctness rather than syntactic patterns, Arctic-Text2SQL-R1 addresses the critical gap between AI-generated queries that appear correct and those that actually produce reliable business insights. The impact of Arctic-Text2SQL-R1 for enterprises will likely take more time, as many organizations are likely to continue to rely on built-in tools inside of their database platform of choice.
Arctic Inference offers the promise of much better performance than any other open-source option, with an easy path to deployment too. For enterprises currently managing separate AI inference deployments for different performance requirements, Arctic Inference’s unified approach could significantly reduce infrastructure complexity and costs while improving performance across all metrics.
As open-source technologies, Snowflake’s efforts have the potential to benefit all enterprises that are looking to improve on challenges that aren’t yet entirely solved.
