Which GPU should you actually use for embedding workloads?

Benchmarking seven embedding models across up to 24 GPU types, three input lengths, and three serving engines found that software choice can change embedding throughput by as much as 11x on identical hardware, often outweighing GPU selection. Text Embeddings Inference (TEI) generally performed best for BERT-family models such as MiniLM, e5, and bge-large, while vLLM led for Qwen3 embedding models; bge-m3 showed near parity between the engines, making operational preferences more important. For 512-token bge-m3 passages, the RTX A5000 offered the lowest measured cost at about $0.78 per billion input tokens, while H200 and H100-class GPUs delivered higher peak throughput but worse cost efficiency, supporting the conclusion that embedding workloads often favor smaller GPUs over flagship accelerators. Single-query latency for bge-m3-class models on pods was already low across tested cards, so steady bulk workloads should prioritize throughput per dollar, whereas Serverless is more appropriate for bursty traffic despite substantially higher single-request latency caused mainly by platform overhead. The results also emphasize validating vector equivalence, output modes, sequence lengths, concurrency requirements, and engine compatibility before using benchmark rankings to make deployment decisions.

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