Table of Contents
PaddleOCR Benchmark Overview
PaddleOCR is an open-source OCR toolkit from Baidu that supports text detection, recognition, and layout analysis in over 80 languages. Running it on a dedicated GPU server is essential for production document processing pipelines where throughput matters.
We measured end-to-end pages per second on GigaGPU servers using a mixed dataset of 100 scanned A4 documents at 300 DPI. Each page includes detection, recognition, and layout analysis. PaddleOCR requires approximately 1.5-2GB of VRAM with the PP-OCRv4 model.
Pages/sec Results by GPU
| GPU | VRAM | PaddleOCR Pages/sec | Notes |
|---|---|---|---|
| RTX 3050 | 6 GB | 3.2 | Usable for small batches |
| RTX 4060 | 8 GB | 5.8 | Good for medium workloads |
| RTX 4060 Ti | 16 GB | 7.5 | Solid throughput |
| RTX 3090 | 24 GB | 11.2 | High throughput |
| RTX 5080 | 16 GB | 14.8 | Excellent speed |
| RTX 5090 | 32 GB | 19.5 | Best throughput tested |
PaddleOCR is compute-bound rather than VRAM-bound, since the model is relatively small. This means faster GPU cores and higher clock speeds have a bigger impact than extra VRAM. The RTX 5080 outperforms the RTX 3090 despite having less VRAM, thanks to its newer architecture and faster cores.
Document Complexity Impact
OCR speed varies significantly with document complexity. We tested three document types on the RTX 5080 and RTX 3090.
| Document Type | RTX 3090 (pages/sec) | RTX 5080 (pages/sec) | RTX 5090 (pages/sec) |
|---|---|---|---|
| Simple text (typed, clean) | 18.5 | 24.0 | 32.0 |
| Mixed (text + tables) | 11.2 | 14.8 | 19.5 |
| Complex (handwriting + images) | 5.8 | 7.6 | 10.2 |
Complex documents with handwritten text and embedded images take roughly 3x longer to process than simple typed text. Plan your throughput estimates based on your typical document mix. For LLM-based document understanding after OCR, see our best GPU for LLM inference guide.
Cost Efficiency Analysis
| GPU | Pages/sec | Approx. Monthly Cost | Pages/sec per Pound |
|---|---|---|---|
| RTX 3050 | 3.2 | ~£45 | 0.071 |
| RTX 4060 | 5.8 | ~£60 | 0.097 |
| RTX 4060 Ti | 7.5 | ~£75 | 0.100 |
| RTX 3090 | 11.2 | ~£110 | 0.102 |
| RTX 5080 | 14.8 | ~£160 | 0.093 |
| RTX 5090 | 19.5 | ~£250 | 0.078 |
The RTX 3090 leads on cost efficiency for OCR workloads, followed closely by the RTX 4060 Ti. The RTX 5080 and 5090 offer absolute speed but at a higher cost per page.
GPU Recommendations
- Budget: RTX 4060 — 5.8 pages/sec is enough for small-to-medium document digitisation projects.
- Best value: RTX 3090 — highest throughput per pound for batch OCR processing.
- Best speed: RTX 5090 — 19.5 pages/sec for high-volume production pipelines.
- Balanced: RTX 4060 Ti — good throughput at an accessible price point.
For object detection benchmarks that complement OCR workflows, see our YOLOv8 FPS benchmark. For embedding-based document search after OCR, check the BGE embedding throughput benchmark. Browse all results in the Benchmarks category.
Conclusion
PaddleOCR is efficient enough to run on any GPU in our lineup, but throughput scales strongly with compute power. For high-volume document processing, the RTX 3090 offers the best cost efficiency, while the RTX 5090 delivers the highest raw speed. Choose based on whether your pipeline is cost-sensitive or latency-sensitive.
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