Best Local LLMs for OCR
作者： Jakub Rusinowski · 最后更新： 2026年8月15日
Turning scanned pages, screenshots and photographed documents into accurate text.
Top pick: Gemma 4 27B ⭐
Scores 93.7/100 for OCR and document scanning. 27B parameters, needing about 17.1 GB at Q4_K_M, 125K context, Gemma License (commercial OK).
Ranked for OCR and document scanning
| Model | Score | Params | Context | Licence | Quality index |
|---|---|---|---|---|---|
| 1. Gemma 4 27B ⭐ | 93.7 | 27B | 125K | Gemma License (commercial OK) | — (estimated) |
| 2. Mistral Small 3.1 24B | 93.5 | 24B | 125K | Apache 2.0 | — (estimated) |
| 3. Qwen 3.5 14B | 92.5 | 14B | 125K | Apache 2.0 | — (estimated) |
| 4. Qwen 3.7 35B-A3B | 92.2 | 35B | 256K | Apache-2.0 | — (estimated) |
| 5. Llama 3.2 11B Vision Instruct | 91.9 | 11B | 125K | Llama Community | — (estimated) |
| 6. Qwen 3.6 35B-A3B | 91.5 | 35B | 256K | Apache-2.0 | — (estimated) |
Frontier open-weight models (multi-GPU / private cloud)
These also score for OCR and document scanning, but none runs on a single consumer GPU or workstation, so they are listed apart rather than ranked against the local picks above.
- DeepSeek V4.1 Flash — Multi-GPU server
- Mistral Large 3 675B-A41B — Multi-GPU server
- Inkling (NVFP4) — Private cloud / datacenter cluster
- Kimi K2.6 — Private cloud / datacenter cluster
- MiMo-V2.6-Pro — Private cloud / datacenter cluster
Best pick for your memory budget
The strongest model overall is rarely the right answer — what matters is the strongest model that fits the memory you have. These picks are re-ranked per tier, so each one uses its budget rather than simply being small.
| Memory | Typical hardware | Recommended models |
|---|---|---|
| 8 GB | RTX 4060, RTX 3070, base MacBook Air | Llama 3.2 11B Vision Instruct (91.9) Ternary Bonsai 27B (91.2) Gemma 4 E4B (89.9) |
| 12 GB | RTX 3060 12 GB, RTX 5070 | Qwen 3.5 14B (93.3) Gemma 4 12B (92.9) Llama 3.2 11B Vision Instruct (91.9) |
| 16 GB | RTX 5080, RTX 4080, RX 9070 XT | Mistral Small 3.1 24B (95.5) Qwen 3.5 14B (93) Gemma 4 12B (91.5) |
| 24 GB | RTX 4090, RTX 3090, RX 7900 XTX | Gemma 4 27B ⭐ (97.2) Qwen 3.7 35B-A3B (95.6) Mistral Small 3.1 24B (95.5) |
| 48 GB | RTX 6000 Ada, MacBook Pro M4 Max 48 GB | Qwen 3.5 72B (97.1) Gemma 4 27B ⭐ (93.6) Qwen 3.7 35B-A3B (93.5) |
| 128 GB+ | Mac Studio, DGX Spark, multi-GPU | Qwen 3.5 72B (93.3) Llama 3.2 90B Vision Instruct (91.5) Qwen3.8-Flash-Next (91.2) |
How this ranking works
Shares the vision hard gate but weights structured-output ability higher (25% coding) and latency higher than general vision, because OCR is typically run in batch over many pages where throughput compounds.
Worked example — Gemma 4 27B ⭐: capability 93.5 × 0.393, quality 92.7 × 0.231, context 100 × 0.15, license 70 × 0.052, accessibility 80 × 0.173 + 3 tag bonus (vision).
Requirements applied: context floor 8,192 tokens (ideal 32,768), quality floor 45, licence weight 0.5, latency weight 0.8. Vision capability is required.
Running OCR and document scanning locally
- A vision model is not always the right OCR tool: for clean printed text a classical OCR engine is faster and more accurate, with the model used only to structure the result.
FAQ
What is the best local LLM for OCR and document scanning?
Gemma 4 27B ⭐, scoring 93.7/100 against this workload's published requirements. 57 models qualified.
What hardware do I need for OCR and document scanning?
A credible answer starts at 8 GB of memory. Larger budgets unlock materially stronger models — the table above lists the best pick at each tier.
How were these models ranked?
Shares the vision hard gate but weights structured-output ability higher (25% coding) and latency higher than general vision, because OCR is typically run in batch over many pages where throughput compounds.
Hardware for This Workload
- Best GPU for ocr
- Best models for the NVIDIA GeForce RTX 4060 Ti 8GB
- Best models for the NVIDIA GeForce RTX 3080 Ti
- Best models for the NVIDIA GeForce RTX 4090 Laptop GPU
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