RTX 5060 Ti 16GB for OCR Pipeline

OCR at scale is a workhorse workload: insurance claims, legal discovery, historical archives and invoice automation all feed on reliable page-to-structured-text conversion. The RTX 5060 Ti 16GB on UK dedicated GPU hosting delivers 34 PaddleOCR pages per second – 2.9 million pages per day – on a single Blackwell GB206 card, with enough VRAM left to stage a Llama 3.1 8B FP8 semantic post-processor in the same process.

Contents

• Stack overview
• Throughput and page budget
• Layout and table extraction
• Multilingual coverage
• LLM post-processing

Stack overview

PaddleOCR v2.8 (PP-OCRv4) is the current sweet spot: three-stage detection, orientation, recognition running on Blackwell FP16 tensor cores. Model weights fit in under 2 GB, leaving 14 GB for layout models, table extraction (PP-Structure) and an optional LLM semantic layer. See our PaddleOCR benchmark for the full tuning profile.

Throughput and page budget

At £X/month fixed cost for the dedicated card, the per-page economics beat Google Document AI’s $1.50/1,000 pages by roughly two orders of magnitude at production volume.

Layout and table extraction

PP-Structure-V2 returns page regions as a JSON tree (title, paragraph, figure, table, footer). Tables are reconstructed to HTML with cell-level coordinates. At 12 pages/second end-to-end including table parsing, one 5060 Ti processes a 50,000-page archive in about 70 minutes wall time – comfortably inside a single overnight batch.

Multilingual coverage

LLM post-processing

OCR returns a bag of lines; your product wants structured fields. Pipe PaddleOCR output into Llama 3.1 8B FP8 (112 t/s batch 1, 720 t/s aggregate – see our FP8 Llama deployment) with a JSON-schema constraint to extract invoice line items, contract clauses or form values. Both models co-resident on a single 5060 Ti push a sustained 8-10 structured documents per second end-to-end.

See also: vLLM setup, classification, embedding server, content tagging.