Welcome to the Daft blog
Join us as we explore innovative ways to handle multimodal datasets, optimize performance, and simplify your data workflows.
GPU Inference with @daft.cls
Run GPU models on millions of rows without OOM. Real patterns from ByteDance, Essential AI, and more.
Benchmarks for Multimodal AI: Spark, Ray Data, and Daft
Multimodal AI workloads break traditional data engines. Daft ran 2-7x faster than Ray Data and 4-18x faster than Spark while finishing jobs reliably across audio, video, document, and image workloads.
Introducing Flotilla: Simplifying Multimodal Data Processing at Scale
Flotilla, Daft's new distributed engine, processes terabytes of multimodal data in a single query up to 18x faster than Spark and Ray Data, while running efficiently, reliably, and without manual tuning.
Exploring Daft's Local Execution: The Swordfish Engine
Explore how Daft's Rust-powered engine executes DataFrame and SQL queries. Learn how Swordfish enables fast, streaming image processing at scale.
After the First Run
Using Daft's observability tools to uncover performance pitfalls
Making GPUs Zoom (Part 1)
How Daft is approaching large-scale model inference with advanced GPU optimizations for faster multimodal AI workloads
End-to-End Distributed PDF Processing Pipeline
Build production-ready PDF processing pipelines with distributed computing, OCR, spatial analysis, and GPU embeddings
How to Build Scalable, End-to-end Batch Inference Pipelines with Daft
Daft makes it easy to express these pipelines end-to-end, while seamlessly scaling them up to handle massive workloads.
24 Trillion Tokens, 0 Crashes: How Essential AI Built Essential-Web v1.0 with Daft
Essential AI leveraged Daft's data engine to process a massive web-scale dataset for large language model (LLM) training.
Embedding Millions of Text Documents With Qwen3
Learn how to achieve near-100% GPU utilization processing millions of text documents with Qwen3 embeddings.
