LightOnOCR 2 1B — Hardware Requirements & GPU Compatibility
Vision
LightOnOCR-2-1B is LightOn's flagship end-to-end OCR vision-language model, a 1-billion-parameter system that converts documents such as PDFs, scans, and photos directly into clean, naturally ordered markdown without a separate OCR pipeline. It handles tables, receipts, forms, multi-column layouts, and math notation, is trained on a large multilingual corpus with particular strength in French, arXiv papers, and scanned documents, and this second-generation release adds RLVR (reinforcement learning from verifiable rewards) training on top of the base model for extra accuracy. LightOn reports state-of-the-art results on the OlmOCR-Bench benchmark while being roughly nine times smaller and several times faster than competing OCR systems. At just 1 billion parameters, it runs comfortably even on a modest single consumer GPU. Context length is 16,384 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in January 2026, alongside base, bounding-box, and merged "soup" variants of the same model.
144.2K downloads
831 likesSpecifications
- Publisher
- lightonai
- Parameters
- 1.0B
- Architecture
- LightOnOCRForConditionalGeneration
- Context Length
- 16,384 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-01-16
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does LightOnOCR 2 1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | 1.7 GB | 0.43 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.9 GB | 1.7 GB | 0.44 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.9 GB | 1.7 GB | 0.49 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 1.0 GB | 1.8 GB | 0.60 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.1 GB | 2.0 GB | 0.72 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.3 GB | 2.1 GB | 0.83 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | 2.3 GB | 1.01 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run LightOnOCR 2 1B?
Q4_K_M · 1.0 GBLightOnOCR 2 1B (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 16K context window can add up to 0.8 GB, bringing total usage to 1.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run LightOnOCR 2 1B?
Q4_K_M · 1.0 GB59 devices with unified memory can run LightOnOCR 2 1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download LightOnOCR 2 1B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does LightOnOCR 2 1B need?
LightOnOCR 2 1B requires 1.0 GB of VRAM at Q4_K_M, or 2.4 GB at BF16. Full 16K context adds up to 0.8 GB (1.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.0B × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.2 GB (at full 16K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context1.8 GB- What's the best quantization for LightOnOCR 2 1B?
For LightOnOCR 2 1B, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
Q2_K0.8 GBQ3_K_L0.9 GBQ4_K_M ★1.0 GBQ5_K_S1.1 GBQ5_K_M1.1 GBBF162.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run LightOnOCR 2 1B on a Mac?
LightOnOCR 2 1B requires at least 0.8 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run LightOnOCR 2 1B locally?
Yes — LightOnOCR 2 1B can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is LightOnOCR 2 1B?
At Q4_K_M, LightOnOCR 2 1B can reach ~4706 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~642 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 1.0 × 0.65 = ~5098 tok/s
Estimated speed at Q4_K_M (1.0 GB)
~5098 tok/s~642 tok/s~5098 tok/s~4706 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of LightOnOCR 2 1B?
At Q4_K_M, the download is about 0.60 GB. The full-precision BF16 version is 2.01 GB. The smallest option (Q2_K) is 0.43 GB.
- Which GPUs can run LightOnOCR 2 1B?
52 consumer GPUs can run LightOnOCR 2 1B at Q4_K_M (1.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run LightOnOCR 2 1B?
59 devices with unified memory can run LightOnOCR 2 1B at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.