Benchmark Overview
Embedding generation is the entry point of every RAG pipeline. Whether GPU acceleration is necessary depends on your ingestion volume and latency requirements. We benchmarked popular embedding models on CPU (Intel Xeon 8480+, 32 cores) versus GPU (RTX 5090, RTX 6000 Pro) to quantify the performance gap and identify when GPU investment is justified for dedicated GPU hosting deployments.
Test Configuration
Models: BGE-Large (335M params), E5-Large-V2 (335M params), GTE-Large (335M params), BGE-M3 (568M params). Input: 512-token text chunks. Batch sizes: 1, 32, 128, 512. CPU: Intel Xeon 8480+ with 32 cores, ONNX Runtime. GPU: CUDA with PyTorch, FP16 precision. Indexed into Qdrant and ChromaDB.
Single-Query Embedding Latency
| Model | CPU (Xeon 32-core) | RTX 5090 | RTX 6000 Pro 96 GB | GPU Speedup |
|---|---|---|---|---|
| BGE-Large | 18ms | 3ms | 2ms | 6-9x |
| E5-Large-V2 | 19ms | 3ms | 2ms | 6-10x |
| GTE-Large | 17ms | 3ms | 2ms | 6-9x |
| BGE-M3 | 32ms | 5ms | 3ms | 6-11x |
Batch Embedding Throughput (Chunks per Second)
| Batch Size | CPU (BGE-Large) | RTX 5090 (BGE-Large) | RTX 6000 Pro (BGE-Large) |
|---|---|---|---|
| 1 | 55/s | 330/s | 500/s |
| 32 | 180/s | 1,800/s | 2,400/s |
| 128 | 210/s | 2,800/s | 3,800/s |
| 512 | 220/s | 3,200/s | 4,500/s |
When GPU Acceleration Matters
For real-time query embedding (single chunks for RAG search), CPU latency of 18ms is acceptable for most applications. The GPU advantage of 2-3ms is meaningful only for sub-10ms latency requirements like voice agents or high-frequency retrieval.
For batch ingestion, the GPU advantage is dramatic. Ingesting 1 million chunks takes 75 minutes on CPU versus 5.5 minutes on RTX 6000 Pro (13x faster). If you process more than 10,000 documents daily, GPU-accelerated embedding pays for itself in time savings. See GPU selection for hardware recommendations and benchmarks for throughput comparisons.
Cost Efficiency Analysis
CPU embedding is essentially free when running on your existing server hardware. A dedicated GPU for embedding alone is hard to justify unless ingestion volumes exceed 100K documents daily. The practical approach: share the GPU between LLM inference (vLLM) and embedding generation, scheduling batch embedding during low-inference periods on private AI hosting.
Recommendations
Use CPU embedding for real-time query encoding and small-batch ingestion under 10K documents. Use GPU embedding for batch ingestion above 10K documents and when the GPU is shared with LLM inference. Deploy on GigaGPU dedicated servers where the CPU handles real-time queries while the GPU processes batch jobs. Visit the benchmarks section and LLM hosting guide for infrastructure planning.
