Show HN: We built an OCR server that can process 270 dense images/s on a 5090

TurboOCR represents a significant breakthrough in local inference performance, achieving 270 dense images per second on a single RTX 5090 GPU. This demonstrates that with proper optimization techniques, local deployment can handle production-grade throughput requirements that rival or exceed cloud-based alternatives.

The project is relevant to local LLM practitioners because it showcases optimization patterns—likely including batching strategies, quantization, kernel optimization, and memory management—that apply broadly to language model inference. When practitioners deploy vision-language models locally or integrate multimodal capabilities, the architectural patterns used in TurboOCR provide valuable insights for achieving similar throughput improvements.

The GitHub repository at aiptimizer/TurboOCR offers concrete implementation details and benchmarks that serve as a reference for anyone building high-performance inference servers on consumer or enterprise hardware.

Source: Hacker News · Relevance: 9/10