OvisOCR2 — Hardware Requirements & GPU Compatibility
VisionFunctions
OvisOCR2 is a compact 853-million-parameter end-to-end vision-language model for page-level document OCR, built by post-training Qwen3.5-0.8B with a data engine that mixes real and synthetic documents and a multi-stage supervised fine-tuning, reinforcement learning, and OPD training recipe. Given a document page image it outputs a single Markdown document that reproduces the natural reading order, including body text, formulas rendered as LaTeX, tables as HTML, and captioned image regions, replacing traditional multi-stage OCR pipelines with one model. It set a new state of the art on the OmniDocBench v1.6 leaderboard, the first end-to-end model to top a benchmark long dominated by pipeline-based approaches, and also leads PureDocBench. At under a billion parameters it runs comfortably on a single consumer GPU or even a CPU. Context length is 262,144 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in July 2026.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 256K)Best on 24 GB (Q4_K_M · ~762 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~83 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run OvisOCR2
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/Abiray/OvisOCR2-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 23.1 GB free · 16K context
Download: OvisOCR2-Q4_K_M.gguf0.53 GBAbiray
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (11 GB headroom) · Q4_K_M
- Generation speed
- ~690 tok/s
- generation speed
- Cost per 1M output tokens
- $0.049
- per 1M output tokens
How Much VRAM Does OvisOCR2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.7 GB | 0.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~923 tok/s | |
| 0.7 GB | 0.9 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~910 tok/s | |
| 0.8 GB | 0.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~851 tok/s | |
| 0.8 GB | 0.9 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~840 tok/s | |
| 0.9 GB | 1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~762 tok/s | |
| 1.0 GB | 1.1 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~683 tok/s | |
| 1.1 GB | 1.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~624 tok/s | |
| 1.2 GB | 1.4 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~546 tok/s |
Selected Q4_K_M
4-bit medium quantization0.9GBat 16K context
- Weights
- 0.5 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.1 GB free · ~762 tok/s
≥50 tok/s needs ≥66 GB/s · RTX 5090 ~1354, RTX 3090 Ti ~762
Which Hardware Can Run OvisOCR2?
Q4_K_M · 0.9 GBOvisOCR2 (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 262K context window can add up to 1.6 GB, bringing total usage to 2.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run OvisOCR2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.1 GB free · up to 256K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~575Simulate output on RTX 3080RTX 2080 Ti~466Simulate output on RTX 2080 TiRTX 3070 Ti~460Simulate output on RTX 3070 TiM3*8 GB~83Simulate output on iMac 24" M3 (8 GB)Orin Nano~77Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~56Simulate output on iPhone 1720 GB
fits · 19.1 GB free · up to 256K ctx
1 GPU
RX 7900 XT~558Simulate output on RX 7900 XT32–40 GB
fits · 31.1 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1354Simulate output on RTX 5090M5 Max36 GB~374Simulate output on Mac Studio M5 Max (36 GB)M532 GB~125Simulate output on MacBook Air 13" M5 (32 GB)Orin~155Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~102Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
2| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| NVIDIA Nemotron Parse 2.0NVIDIA | — | 3.4 GB | ~329 | same size |
| Qwen3.5 0.8BAlibaba | — | 0.9 GB | ~753 | same size |
Specifications
- Publisher
- ATH-MaaS
- Parameters
- 853M
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-07-13
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does OvisOCR2 need?
OvisOCR2 requires 0.9 GB of VRAM at Q4_K_M, or 2.1 GB at BF16. Full 262K context adds up to 1.6 GB (2.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 853M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for OvisOCR2?
For OvisOCR2, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (0.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 0.6 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run OvisOCR2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run OvisOCR2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.9 GB of usable unified memory (RAM minus macOS overhead).
Can I run OvisOCR2 locally?
Yes — OvisOCR2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is OvisOCR2?
At Q4_K_M, OvisOCR2 can reach ~5581 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~762 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 ÷ 0.86 × 0.65 = ~6047 tok/s
Estimated speed at Q4_K_M (0.9 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of OvisOCR2?
At Q4_K_M, the download is about 0.51 GB. The full-precision BF16 version is 1.71 GB. The smallest option (IQ2_M) is 0.29 GB.
Which GPUs can run OvisOCR2?
52 consumer GPUs can run OvisOCR2 at Q4_K_M. 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 OvisOCR2?
163 devices with unified memory can run OvisOCR2 at Q4_K_M (0.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.