Sequence packing, smart shuffling, and avoiding the bottlenecks that waste GPU time
9 min readJust now
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Data processing infrastructure doesn’t get much attention in LLM training, but it’s often the bottleneck. When you’re feeding trillions of tokens to hundreds of GPUs, a poorly designed pipeline means idle hardware and wasted money. Here’s how to build data systems that work at scale.
The Architecture of a Data Pipeline
Think of a data pipeline as having four distinct layers, each with its own job:
The ingestion layer reads raw data from wherever it lives (S3 buckets, HDFS clusters, local storage). This sounds simple until you realize you’re dealing with petabytes of data spread across thousands of files in different…
Sequence packing, smart shuffling, and avoiding the bottlenecks that waste GPU time
9 min readJust now
–
Press enter or click to view image in full size
Data processing infrastructure doesn’t get much attention in LLM training, but it’s often the bottleneck. When you’re feeding trillions of tokens to hundreds of GPUs, a poorly designed pipeline means idle hardware and wasted money. Here’s how to build data systems that work at scale.
The Architecture of a Data Pipeline
Think of a data pipeline as having four distinct layers, each with its own job:
The ingestion layer reads raw data from wherever it lives (S3 buckets, HDFS clusters, local storage). This sounds simple until you realize you’re dealing with petabytes of data spread across thousands of files in different formats. Some files are compressed with gzip, others with zstandard. Some are JSON, others are Parquet or raw text. The ingestion layer needs to handle all of this gracefully.
The processing layer does the heavy lifting: filtering out low-quality documents, removing duplicates, and tokenizing text. This is where you apply all the quality rules you’ve carefully designed. But here’s the catch: processing trillions of tokens means this layer needs to be highly…