Fifty-two pages per second from a single GPU. If your document processing pipeline currently bottlenecks on OCR, the RTX 3090 may be the simplest fix. We benchmarked PaddlePaddle PP-OCRv4 (detection + recognition pipeline) on the NVIDIA RTX 3090 (24 GB VRAM) using a GigaGPU dedicated server, and the combination of raw throughput and massive VRAM makes this card a genuine production contender for document-heavy workloads.
Throughput Results
| Metric | Value |
|---|---|
| Pages/sec | 52 pages/sec |
| Latency per page | 19.2 ms |
| Precision | FP16 |
| Pipeline | Det + Rec + Cls |
| Performance rating | Very Good |
Benchmark conditions: FP16 inference, batch size 1, PP-OCRv4 full pipeline (detection + direction + recognition) on A4-format document scans.
VRAM Headroom
| Component | VRAM |
|---|---|
| Model weights (FP16) | 1.2 GB |
| Processing buffer | ~0.4 GB |
| Total RTX 3090 VRAM | 24 GB |
| Free headroom | ~22.8 GB |
Here is where the 3090 really separates itself. With 22.8 GB of VRAM sitting idle after PaddleOCR loads, you have enough room to co-host a full FP16 LLaMA 3 8B alongside the OCR pipeline. That means scanned documents can be extracted, parsed, and understood by an LLM in a single GPU — no inter-service networking, no serialisation overhead, just fast local inference.
What It Costs
| Cost Metric | Value |
|---|---|
| Server cost | £0.75/hr (£149/mo) |
| Cost per 1M pages | £4.01 |
| Pages per £1 | 249377 |
The per-page cost is marginally higher than the RTX 4060, but the 3090 gives you nearly double the throughput and triple the VRAM. For production PaddleOCR deployments that need to process tens of thousands of pages per hour — think insurance claim forms, legal discovery, or medical record digitisation — the 3090 earns its keep. Compare all cards at our benchmark page.
Production Fit
The RTX 3090 is the natural choice when PaddleOCR is just one stage in a larger document intelligence pipeline. Its 24 GB VRAM budget means you can run OCR, an embedding model, and an LLM concurrently. That kind of co-hosting slashes latency and infrastructure complexity compared to multi-GPU setups. If you are serious about building a self-hosted intelligent document processing system, start here.
Quick deploy:
docker run --gpus all -p 8866:8866 paddlecloud/paddleocr:latestSee our PaddleOCR hosting guide, best GPU for OCR, and all benchmark results. Related: LLaMA 3 8B on RTX 3090 benchmark.
Deploy PaddleOCR on RTX 3090
Order this exact configuration. UK datacenter, full root access.
Order RTX 3090 Server
