Snowflake AI Research

Snowflake's Cortex AI Translate offers reliable translations across 14 languages, optimized for enterprise use with ease of operation and business relevance.

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Translation En Acción: A Model Built for the Real Business World

SwiftKV: Accelerating Enterprise LLM Workloads with Knowledge Preserving Compute Reduction

Snowflake’s Arctic Embed 2.0 Goes Multilingual: Empowering Global-Scale Retrieval with Inference Efficiency and High-Quality Retrieval

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46 Results

Publication

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data

Publication

SwiftKV: Fast Prefill-Optimized Inference with Knowledge-Preserving Model Transformation

Publication

Efficiently Serving LLM Reasoning Programs with Certaindex

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Pruning in Snowflake: Working Smarter, Not Harder

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ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction, and Column Exploration

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SuffixDecoding: A Model-Free Approach to Speeding Up Large Language Model Inference

Connect with usand explore our projects

Github

Arctic Inference

Arctic Inference combines Shift Parallelism, SwiftKV, fast speculative decoding, and optimized embedding inference to deliver state-of-the-art open-source AI performance.

Github

Arctic Training

Snowflake’s framework for optimizing large language models using scalable, enterprise-grade techniques like execution-based feedback, retrieval-augmented learning, and efficient fine-tuning.

Hugging face

Arctic Models

Download Snowflake’s enterprise-ready OSS models and frameworks for state-of-the-art retrieval, inference, speculative decoding, and Text2SQL optimization.

Github

Trulens

Tracing, evals, experiment tracking for LLM applications. Created and maintained by researchers & developers now primarily with Snowflake.

Github

DeepSpeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Github

vLLM

A high-throughput and memory-efficient inference and serving engine for LLMs. Snowflake researchers and engineers actively contribute to vLLM.