We Hit 100% GPU Utilization–and Then Made It 3× Faster by Not Using It
11 days ago
- #GPU-optimization
- #Qwen3
- #text-embedding
- Used Qwen3-Embedding-0.6B to embed millions of text documents with near-100% GPU utilization.
- Developed a pipeline to read documents from S3, chunk them using spaCy, compute embeddings, and write to turbopuffer.
- Optimized parameters like NUM_GPU_NODES, CHUNKING_PARALLELISM, and batch sizes for efficiency.
- Discussed chunking strategies: sentence-level, paragraph-level, section-level, and fixed-size chunks.
- Implemented sentence-level chunking with spaCy for robust sentence boundary detection.
- Chose Qwen3-Embedding-0.6B for its performance-to-size ratio and state-of-the-art results.
- Configured distributed processing on a Ray cluster with 8 g5.2xlarge workers.
- Executed a pipeline that reads, chunks, embeds, and writes data efficiently.
- Provided customization tips for adjusting batch sizes, scaling workers, and changing models.
- Highlighted performance considerations like GPU memory, model loading, and quantization.
- Teased future improvements with custom GPU pipelining and vLLM for 3× faster processing.