Run chandra-ocr-2 with Native FP4 Full Method

The fastest way to get this model running locally is via Docker.

Just follow the guidelines provided below.

1-click setup: the app automatically fetches the large weight files.

The deployment tool scans your environment and automatically chooses the ideal parameters for your OS.

🗂 Hash: a5ef6e921c52633217f2c6caa598e35c • Last Updated: 2026-06-22 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

The **chandra-ocr-2** model delivers *state-of-the-art* optical character recognition with unprecedented accuracy across diverse document types. It leverages a deep convolutional neural network architecture combined with attention mechanisms to capture both fine-grained character shapes and contextual layout cues. The model supports a wide range of languages and scripts, making it suitable for global enterprise workflows. Performance benchmarks show a character error rate below 0.5% on standard benchmarks, outperforming previous generations by over 15%. Integration is streamlined via a lightweight API that processes images in *real-time* with minimal hardware requirements.

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