What is the fastest GPU for OCR?

For single-GPU OCR throughput, the RTX 6000 PRO (96GB) delivers the highest pages-per-minute. For consumer GPUs, the RTX 5090 (32GB) offers the fastest processing. The RTX 3090 (24GB) provides strong production throughput at a lower price point.

What GPU do I need for PaddleOCR?

PaddleOCR runs on both CPU and GPU but GPU acceleration dramatically increases throughput. An RTX 4060 (8GB) handles development use. For production, the RTX 3090 (24GB) offers best value at approximately 190 pages per minute.

Is GPU OCR faster than cloud OCR APIs?

A dedicated GPU typically matches or exceeds cloud API speeds with no network round-trip latency. You also process unlimited pages at a flat monthly rate instead of paying per page.

What is the difference between PaddleOCR and VLM-based OCR?

PaddleOCR is a traditional pipeline that detects text regions and recognises characters in separate stages. VLM-based OCR uses a vision-language model that sees the entire document at once, understanding layout, tables, formulas, and mixed content end-to-end.

Is self-hosted OCR cheaper than Google Document AI?

At production volumes, significantly cheaper. Google Document AI charges approximately £1.20 per 1,000 pages. A self-hosted RTX 3090 processing 1M pages per month works out to roughly £0.15 per 1,000 pages — about 8 times cheaper.

Where are GigaGPU servers located?

All servers are located in the UK, which is important for organisations with UK/EU data residency requirements.

OCR Speed Benchmarks

Q: How much VRAM do I need for OCR?

PaddleOCR runs well on 4-8GB VRAM. Small VLM-OCR models like PaddleOCR-VL (0.9B) fit on 8GB. Larger VLMs like DeepSeek-OCR need 16-32GB at full precision.

Q: Can I run OCR on AMD GPUs?

Yes. PaddleOCR and PyTorch-based models like Surya work on AMD GPUs via ROCm. The RX 9070 XT and R9700 both deliver solid OCR throughput.

GPU OCR Throughput — Pages Per Minute by GPU for Self-Hosted Document Processing

Compare OCR processing speeds across GigaGPU’s dedicated GPU range. See how many pages per minute each GPU can process running popular open source OCR models like PaddleOCR, Surya, and modern VLM-based engines.

Why GPU-Accelerated OCR?

Modern OCR has moved far beyond basic text recognition. Today’s open source models — PaddleOCR, Surya, DeepSeek-OCR, GOT-OCR 2.0, and vision-language models like Qwen2.5-VL — can parse complex documents including tables, formulas, multi-column layouts, and handwriting. These models rely on GPU acceleration to process documents at production throughput.

Self-hosting OCR on a dedicated GPU server means you process unlimited pages at a flat monthly rate with no per-page API fees, full data privacy, and the flexibility to switch models or fine-tune for your specific document types. The question is which GPU matches your throughput requirements — and that’s what these benchmarks answer.

10+

OCR Models Tested

GPU Tiers Compared

167×

Cheaper Than APIs

Data Centre

OCR Speed Benchmark — GPU Comparison

Estimated OCR throughput across GigaGPU’s dedicated GPU range. Figures show pages processed per minute using PaddleOCR (PP-OCRv5) and Surya OCR on standard A4 document scans at 300 DPI. Higher is faster.

GPU	VRAM	PaddleOCR pages/min	Surya OCR pages/min	VLM-OCR (3B) pages/min	Best Fit	Relative Throughput
RTX 3050	6 GB	~35	~18	—	Light testing, small batches	8%
RTX 4060	8 GB	~80	~42	~8	Small business, dev/staging	18%
RTX 4060 Ti	16 GB	~110	~58	~14	Entry production, VLM-OCR capable	25%
RTX 5060	16 GB	~120	~65	~16	Entry production, Blackwell gen	28%
RTX 3090	24 GB	~190	~95	~28	Best value for production OCR	44%
RTX 5080	16 GB	~230	~120	~32	High-throughput pipeline	53%
RX 9070 XT	16 GB	~140	~70	~18	AMD alternative, PaddleOCR optimised	32%
R9700	20 GB	~170	~85	~22	AMD mid-range, good VRAM headroom	39%
RTX 5090	32 GB	~380	~195	~58	Maximum single-GPU throughput	87%
Arc Pro B70	24 GB	~100	~48	~12	Intel option, emerging support	23%
AI MAX+ 395	128 GB	~85	~40	~35	Massive VRAM, huge VLM models	20%
RTX 6000 PRO	96 GB	~430	~220	~70	Enterprise, multi-model concurrent	100%

PaddleOCR benchmarked with PP-OCRv5 (detection + recognition pipeline) on 300 DPI A4 scans, batch size 8. Surya OCR benchmarked with default detection + recognition, single-stream. VLM-OCR tested with a 3B-parameter vision-language model (e.g. DeepSeek-OCR, PaddleOCR-VL) via vLLM. All tests on a single GPU with no other workloads running. Real-world throughput varies with document complexity, image resolution, batch size, and concurrent requests.

PaddleOCR Throughput by GPU — Visual Chart

Estimated pages per minute running PaddleOCR PP-OCRv5 on 300 DPI document scans. Single GPU, batch size 8. Higher is faster.

RTX 6000 PRO

~430 pg/min

430

RTX 5090

~380 pg/min

380

RTX 5080

~230 pg/min

230

RTX 3090

~190 pg/min

190

R9700

~170 pg/min

170

RX 9070 XT

~140 pg/min

140

RTX 5060

~120 pg/min

120

RTX 4060 Ti

~110 pg/min

110

Arc Pro B70

~100 pg/min

100

AI MAX+ 395

~85 pg/min

RTX 4060

~80 pg/min

RTX 3050

~35

Estimates only · PaddleOCR PP-OCRv5 · 300 DPI A4 scans · Batch size 8 · Single GPU

Popular OCR Models — At a Glance

The OCR landscape has shifted dramatically in 2024–2025. Traditional engines like Tesseract are now joined by GPU-accelerated pipelines and vision-language models that understand document structure end-to-end.

PaddleOCR (PP-OCRv5)

Baidu / PaddlePaddle

Production-ready detection + recognition pipeline. Supports 80+ languages, runs on GPU and CPU. Fast inference, strong table/layout handling. Apache 2.0.

80+ langsGPU + CPUApache 2.0

Surya OCR

Datalab

Line-level detection and recognition in 90+ languages. Layout analysis, reading order detection, and table recognition built in. Competitive with cloud APIs on accuracy.

90+ langsGPU requiredLayout aware

DeepSeek-OCR

DeepSeek AI

3B-parameter VLM with 10× token compression. Processes 200K+ pages per day on a single A100-class GPU. Outputs structured Markdown and LaTeX. MoE architecture — only 570M parameters active.

3B (570M active)10× compressionMIT

PaddleOCR-VL

Baidu / PaddlePaddle

0.9B-parameter vision-language model with 109 language coverage. Two-stage pipeline — layout analysis then content recognition. Compact enough to run on 8GB VRAM GPUs.

0.9B params109 langs8GB VRAM

GOT-OCR 2.0

StepFun

Unified end-to-end model that handles text, formulas, tables, sheet music, and geometric shapes in a single pass. Strong on complex mixed-content documents.

End-to-endMulti-typeGPU only

Tesseract

Google (Maintained)

The original open source OCR engine. CPU-based, 100+ languages, extremely mature. Still useful for clean printed text at scale, but struggles with complex layouts and tables without post-processing.

CPU-based100+ langsApache 2.0

Qwen2.5-VL

Alibaba / Qwen

Multimodal vision-language model with top-tier OCRBench v2 scores. Handles text, diagrams, charts, and tables with bounding box and point detection built in. Available in 3B, 7B, and 72B sizes.

3B–72BOCRBench v2 leaderApache 2.0

Datalab Marker

Datalab

Full end-to-end pipeline that converts PDFs and images into structured Markdown, JSON, or HTML. Uses Surya as its OCR backbone with optional LLM enhancement for higher fidelity output.

PDF → MarkdownSurya backboneLLM optional

Which GPU Do You Need for OCR?

The right GPU depends on your document volume, model choice, and whether you need traditional pipeline OCR or VLM-based document intelligence.

Development & Testing

RTX 4060 (8GB) or RTX 4060 Ti (16GB). Plenty for running PaddleOCR, Surya, or small VLM models during development. Process up to ~110 pages/min with PaddleOCR.

Small Business Production

RTX 3090 (24GB). Best price/performance ratio for production OCR. Handles PaddleOCR at ~190 pages/min and has enough VRAM for 3B VLM-OCR models at full precision.

High-Throughput Pipeline

RTX 5090 (32GB). Blackwell-generation speed processes ~380 pages/min with PaddleOCR and delivers strong VLM-OCR throughput. Ideal for document processing APIs.

Enterprise / Multi-Model

RTX 6000 PRO (96GB). Run multiple OCR models concurrently, process complex multi-page documents with large VLMs, or handle massive batch jobs. ~430 pages/min with PaddleOCR.

Large VLM Document Intelligence

AI MAX+ 395 (128GB) or RTX 6000 PRO (96GB). Run 7B–8B vision-language models like Qwen2.5-VL or Chandra-OCR at full precision for maximum accuracy on complex documents.

Budget / AMD Alternative

RX 9070 XT (16GB) or R9700 (20GB). Solid PaddleOCR performance at competitive pricing. AMD GPU support for OCR workloads continues to improve with ROCm.

Real-Time OCR API

RTX 5080 (16GB) or RTX 5090 (32GB). Low-latency single-page processing for live OCR endpoints. Blackwell-generation compute delivers sub-second response times for on-demand document capture.

Batch Archive Processing

RTX 3090 (24GB). Ideal for overnight or scheduled batch jobs processing large document backlogs. Strong throughput at the lowest cost per page — maximise volume without time pressure.

Self-Hosted GPU vs Cloud OCR APIs — Cost Comparison

At scale, self-hosted OCR on a dedicated GPU is dramatically cheaper than per-page cloud API pricing. The more pages you process, the wider the gap.

Cloud OCR API Pricing

Google Document AI~£1.20/1K pages

AWS Textract~£1.20/1K pages

Azure Document Intelligence~£1.20/1K pages

1M pages/month~£1,200/mo

10M pages/month~£12,000/mo

Cloud API prices are approximate and based on standard text extraction tiers. Table/form extraction tiers cost significantly more — typically £8–£40 per 1K pages.

Self-Hosted GPU (Flat Rate)

RTX 3090 — unlimited pagesfrom £149/mo

RTX 5090 — unlimited pagesfrom £399/mo

RTX 6000 PRO — unlimited pagesfrom £699/mo

1M pages/month (RTX 3090)~£0.15/1K pages

10M pages/month (RTX 5090)~£0.04/1K pages

GigaGPU flat monthly pricing. No per-page fees. Prices above are indicative — view current pricing. Effective cost per page decreases as volume increases.

OCR Hosting Use Cases

Self-hosted GPU OCR serves any workload where document processing volume, data privacy, or model flexibility matters.

Bulk Document Digitisation

Convert scanned archives, contracts, and paper records into searchable text at scale. Process millions of pages per month at a fraction of cloud API costs.

PDF-to-LLM Pipelines

Extract structured text from PDFs for RAG pipelines, knowledge bases, and LLM ingestion. PaddleOCR and Surya output clean Markdown that feeds directly into vector stores.

Invoice & Receipt Processing

Automate accounts payable with GPU-accelerated OCR that extracts line items, totals, dates, and vendor details from invoices at production speed.

Compliance & Legal Discovery

Process sensitive legal documents on-premises. No data leaves your server — ideal for GDPR, financial regulation, and legal hold requirements.

Healthcare Document Processing

Digitise patient records, prescriptions, and clinical notes with full data privacy. Self-hosted OCR keeps protected health information on your own infrastructure.

Multilingual OCR at Scale

PaddleOCR-VL supports 109 languages including Cyrillic, Arabic, Devanagari, and CJK scripts. Process multilingual document sets without per-language API charges.

Education & Research

Digitise academic papers, textbooks, and handwritten notes. VLM-based models like GOT-OCR 2.0 handle mathematical formulas, diagrams, and mixed-content pages that traditional OCR misses.

Insurance & Claims Processing

Extract structured data from claim forms, policy documents, and supporting evidence at scale. Automate intake workflows with GPU-accelerated OCR that handles handwritten and printed content.

Benchmark Methodology

How we measured OCR throughput across GPU tiers.

Test Conditions

PaddleOCR: PP-OCRv5 detection + recognition pipeline, batch size 8, processing A4 document scans at 300 DPI. Mixed document types including printed text, tables, and multi-column layouts. Single GPU, no other workloads running.

Surya OCR: Default detection + recognition configuration, single-stream processing (RECOGNITION_BATCH_SIZE matched to GPU VRAM). Same document set as PaddleOCR tests.

VLM-OCR (3B): 3B-parameter vision-language model (representative of DeepSeek-OCR, PaddleOCR-VL class) served via vLLM with default settings. Pages processed end-to-end including layout understanding and structured output generation.

Important: These are indicative benchmarks for GPU comparison purposes. Real-world throughput varies significantly with document complexity, image resolution, pre/post-processing pipeline, batch size, concurrent requests, and model version. We recommend running your own benchmarks with your specific document types before making purchasing decisions.

OCR Speed Benchmarks — FAQ

For single-GPU OCR throughput, the RTX 6000 PRO (96GB) delivers the highest pages-per-minute across all model types. For consumer-class GPUs, the RTX 5090 (32GB) offers the fastest OCR processing thanks to Blackwell-generation compute. If you’re running traditional pipeline OCR like PaddleOCR, even the RTX 3090 (24GB) delivers strong production throughput at a lower price point.

PaddleOCR runs on both CPU and GPU, but GPU acceleration dramatically increases throughput. An RTX 4060 (8GB) handles development and light production use. For sustained production workloads, the RTX 3090 (24GB) offers the best value at ~190 pages/min. The RTX 5090 pushes this to ~380 pages/min for high-volume pipelines.

In terms of raw throughput, a dedicated GPU typically matches or exceeds cloud API speeds because there’s no network round-trip latency. More importantly, you process unlimited pages at a flat monthly rate. Cloud APIs like Google Document AI and AWS Textract charge per page, so costs scale linearly with volume — self-hosted OCR costs stay fixed regardless of how many pages you process.

PaddleOCR is a traditional pipeline — it detects text regions, classifies orientation, and recognises characters in separate stages. It’s fast and reliable for standard documents. VLM-based OCR (DeepSeek-OCR, PaddleOCR-VL, GOT-OCR 2.0, Qwen2.5-VL) uses a vision-language model that sees the entire document at once, understanding layout, tables, formulas, and mixed content end-to-end. VLM-OCR is more accurate on complex documents but requires more compute and runs slower per page.

It depends on the model. PaddleOCR (traditional pipeline) runs well on 4–8GB VRAM. Surya OCR benefits from 8–16GB. Small VLM-OCR models like PaddleOCR-VL (0.9B) fit on 8GB. Larger VLMs like DeepSeek-OCR (3B) or Chandra-OCR (8B) need 16–32GB at full precision, or less with quantisation. For running multiple models concurrently, 24–96GB is recommended.

Yes — PaddleOCR and PyTorch-based models like Surya work on AMD GPUs via ROCm. The RX 9070 XT and R9700 both deliver solid OCR throughput. AMD GPU support for the broader ML ecosystem continues to improve, though NVIDIA GPUs still have the widest compatibility with OCR frameworks and the best out-of-the-box performance.

At production volumes, significantly cheaper. Google Document AI charges approximately £1.20 per 1,000 pages for basic text extraction. A self-hosted RTX 3090 processing 1M pages/month works out to roughly £0.15 per 1,000 pages — about 8× cheaper. The savings multiply further at higher volumes and when you need structured extraction (tables, forms) which cloud APIs charge a premium for.

All servers are located in the UK. This is important for organisations with UK/EU data residency requirements, especially when processing sensitive documents like financial records, legal documents, or healthcare data that must stay within UK jurisdiction.

Self-Host OCR on Dedicated GPU Servers

Process unlimited documents at a flat monthly rate. Full GPU resources, no shared infrastructure, no per-page fees. Deploy PaddleOCR, Surya, DeepSeek-OCR, or any open source OCR model on bare-metal GPU servers in the UK.

Perfect for document digitisation, PDF-to-LLM pipelines, invoice processing, compliance workflows, and any other OCR workload where volume, privacy, or model flexibility matters.

Get in Touch

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