GOT OCR2 0 — Hardware Requirements & GPU Compatibility
Vision
GOT-OCR2.0 is a compact 716-million-parameter vision-language model built specifically for OCR, unifying plain-text OCR, formatted-text extraction (tables, formulas, and markup), and fine-grained region- or color-guided recognition in a single end-to-end model rather than a general chat assistant. It reads an image plus a task prompt and outputs recognized text, with an optional rendering mode that reconstructs the original layout as HTML. Unlike two-stage OCR pipelines that pair a separate detector with a text recognizer, GOT-OCR2.0 performs detection-free recognition end to end, and at under a billion parameters it runs comfortably on a single modest consumer GPU or even a CPU. Context length is 32,768 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. It was published in September 2024 by StepFun alongside its "General OCR Theory" paper.
613.9K downloads
1.6K likesSpecifications
- Publisher
- StepFun
- Parameters
- 716M
- Architecture
- GOTQwenForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,860
- Release Date
- 2024-09-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does GOT OCR2 0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.8 GB | 3.8 GB | 0.30 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.8 GB | 3.9 GB | 0.35 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 0.9 GB | 4.0 GB | 0.43 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.0 GB | 4.0 GB | 0.51 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.1 GB | 4.1 GB | 0.59 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.2 GB | 4.2 GB | 0.72 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 1.9 GB | 5.0 GB | 1.43 GB | Brain floating point 16 — preferred for training |
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 GOT OCR2 0?
Q4_K_M · 0.9 GBGOT OCR2 0 (Q4_K_M) requires 0.9 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 33K context window can add up to 3.0 GB, bringing total usage to 4.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run GOT OCR2 0?
Q4_K_M · 0.9 GB59 devices with unified memory can run GOT OCR2 0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does GOT OCR2 0 need?
GOT OCR2 0 requires 0.9 GB of VRAM at Q4_K_M, or 1.9 GB at BF16. Full 33K context adds up to 3.0 GB (4.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 716M × 4.8 bits ÷ 8 = 0.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.6 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context4.0 GB- What's the best quantization for GOT OCR2 0?
For GOT OCR2 0, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.0 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 GBQ4_K_M ★0.9 GBQ5_K_M1.0 GBQ6_K1.1 GBQ8_01.2 GBBF161.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run GOT OCR2 0 on a Mac?
GOT OCR2 0 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 GOT OCR2 0 locally?
Yes — GOT OCR2 0 can run locally on consumer hardware. At Q4_K_M quantization it needs 0.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is GOT OCR2 0?
At Q4_K_M, GOT OCR2 0 can reach ~5161 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~705 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 ÷ 0.9 × 0.65 = ~5591 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~5591 tok/s~705 tok/s~5591 tok/s~5161 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of GOT OCR2 0?
At Q4_K_M, the download is about 0.43 GB. The full-precision BF16 version is 1.43 GB. The smallest option (Q2_K) is 0.30 GB.
- Which GPUs can run GOT OCR2 0?
52 consumer GPUs can run GOT OCR2 0 at Q4_K_M (0.9 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 GOT OCR2 0?
59 devices with unified memory can run GOT OCR2 0 at Q4_K_M (0.9 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.