OlmOCR 2 7B 1025 — Hardware Requirements & GPU Compatibility
Vision
olmOCR-2-7B-1025 is Allen AI's 8.3-billion-parameter vision-language model, fine-tuned from Qwen2.5-VL-7B-Instruct specifically for document OCR rather than general chat. It converts scanned pages and PDFs into clean text, using GRPO reinforcement learning atop supervised fine-tuning to sharpen accuracy on math, tables, and tricky OCR cases. This is the full-precision BF16 release; Allen AI recommends its FP8 sibling for production use. At this size, it runs on a single mainstream-to-high-end consumer GPU once quantized. It supports a 128,000 token context window inherited from its Qwen2.5-VL base, useful for multi-page documents. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. Published in October 2025, it pairs with Allen AI's olmOCR toolkit, which handles page rendering and retries at scale via vLLM.
204.9K downloads
158 likesSpecifications
- Publisher
- Allen AI
- Family
- OLMo
- Parameters
- 8.3B
- Architecture
- Qwen2_5_VLForConditionalGeneration
- Context Length
- 128,000 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-10-06
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OlmOCR 2 7B 1025 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.9 GB | 11.2 GB | 3.52 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.0 GB | 11.3 GB | 3.63 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.5 GB | 11.7 GB | 4.04 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.6 GB | 11.8 GB | 4.15 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.4 GB | 12.6 GB | 4.98 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 6.3 GB | 13.6 GB | 5.91 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 7.3 GB | 14.5 GB | 6.84 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.7 GB | 15.9 GB | 8.29 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run OlmOCR 2 7B 1025?
Q4_K_M · 5.4 GBOlmOCR 2 7B 1025 (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 128K context window can add up to 7.2 GB, bringing total usage to 12.6 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run OlmOCR 2 7B 1025?
Q4_K_M · 5.4 GB58 devices with unified memory can run OlmOCR 2 7B 1025, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download OlmOCR 2 7B 1025
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does OlmOCR 2 7B 1025 need?
OlmOCR 2 7B 1025 requires 5.4 GB of VRAM at Q4_K_M, or 17 GB at BF16. Full 128K context adds up to 7.2 GB (12.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 4.8 bits ÷ 8 = 5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.6 GB (at full 128K context)
VRAM usage by quantization
Q4_K_M5.4 GBQ4_K_M + full context12.6 GB- What's the best quantization for OlmOCR 2 7B 1025?
For OlmOCR 2 7B 1025, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 2.7 GB.
VRAM requirement by quantization
IQ2_XXS2.7 GBIQ3_XS3.8 GBQ3_K_L4.7 GBQ4_K_M ★5.4 GBQ4_K_L5.5 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run OlmOCR 2 7B 1025 on a Mac?
OlmOCR 2 7B 1025 requires at least 2.7 GB at IQ2_XXS, 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 OlmOCR 2 7B 1025 locally?
Yes — OlmOCR 2 7B 1025 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OlmOCR 2 7B 1025?
At Q4_K_M, OlmOCR 2 7B 1025 can reach ~891 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 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 ÷ 5.4 × 0.65 = ~965 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~965 tok/s~122 tok/s~965 tok/s~891 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OlmOCR 2 7B 1025?
At Q4_K_M, the download is about 4.98 GB. The full-precision BF16 version is 16.58 GB. The smallest option (IQ2_XXS) is 2.28 GB.
- Which GPUs can run OlmOCR 2 7B 1025?
52 consumer GPUs can run OlmOCR 2 7B 1025 at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run OlmOCR 2 7B 1025?
59 devices with unified memory can run OlmOCR 2 7B 1025 at Q4_K_M (5.4 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.