1 Billion Classifications

Source: HuggingFace Date: 2025-02-13 URL: https://huggingface.co/blog/billion-classifications

Summary

Methodology guide: HF benchmarks cost and throughput for 1B+ daily inference requests on encoder models using HF Inference Endpoints + Infinity server. Key numbers: 1B classifications (DistilBERT-135M) = $253 on L4 at batch 64; 1B embeddings (ModernBERT-149M) = $409; 1B vision embeddings (ColQwen2 2.21B) = $44,496 — two orders of magnitude more expensive. L4 GPUs consistently beat T4 on cost/performance.

Implications

Thread: HF as open-source ML hub / open-weights ecosystem health. The $253 vs. $44,496 gap between text classification and vision embeddings is the headline: ColBERT-style multi-vector vision retrieval is currently economically unviable at scale for most applications. ModernBERT recommended over DistilBERT for the embedding use case is the practical guidance teams need. The methodology (k6 load testing + binary search for optimal VU count) is reusable framework for any HF Inference Endpoints deployment. L4 over T4 for inference workloads is actionable hardware selection guidance.