HunyuanOCR — Hardware Requirements & GPU Compatibility
Vision
HunyuanOCR is Tencent's 1.1-billion-parameter vision-language model built for OCR and document understanding, unifying document parsing, text spotting, information extraction, and text-image translation in one model rather than a general chat assistant. The current checkpoint, HunyuanOCR-1.5, adds DFlash speculative decoding, where a lightweight draft model proposes tokens that the main model verifies in one pass, cutting latency on long structured outputs like tables and formulas. It also ships GGUF weights for llama.cpp, running on CPUs and laptop or consumer GPUs, not just server-grade vLLM. At just over 1 billion parameters, it is light enough for a single modest consumer GPU. Context length is 131,072 tokens; pretraining supports image resolutions up to 4K. It is released under the Tencent Hunyuan Community License, a custom license permitting commercial use outside the EU, UK, and South Korea and below 100 million monthly active users. It was published in November 2025.
713.3K downloads
823 likesSpecifications
- Publisher
- Tencent
- Family
- Hunyuan
- Parameters
- 1.1B
- Architecture
- HunYuanVLForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 120,818
- Release Date
- 2025-11-18
- License
- Other
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HuggingFace
How Much VRAM Does HunyuanOCR Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.9 GB | 7.2 GB | 0.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.9 GB | 7.2 GB | 0.49 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.9 GB | 7.3 GB | 0.55 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 1.0 GB | 7.3 GB | 0.56 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 1.1 GB | 7.4 GB | 0.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.2 GB | 7.5 GB | 0.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.3 GB | 7.7 GB | 0.92 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.5 GB | 7.9 GB | 1.12 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run HunyuanOCR?
Q4_K_M · 1.1 GBHunyuanOCR (Q4_K_M) requires 1.1 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 131K context window can add up to 6.3 GB, bringing total usage to 7.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run HunyuanOCR?
Q4_K_M · 1.1 GB59 devices with unified memory can run HunyuanOCR, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download HunyuanOCR
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 HunyuanOCR need?
HunyuanOCR requires 1.1 GB of VRAM at Q4_K_M, or 2.6 GB at BF16. Full 131K context adds up to 6.3 GB (7.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.1 GBQ4_K_M + full context7.4 GB- What's the best quantization for HunyuanOCR?
For HunyuanOCR, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.7 GB.
VRAM requirement by quantization
IQ2_XXS0.7 GBIQ3_XS0.9 GBQ4_01.0 GBIQ4_NL1.0 GBQ4_K_M ★1.1 GBBF162.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run HunyuanOCR on a Mac?
HunyuanOCR requires at least 0.7 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 HunyuanOCR locally?
Yes — HunyuanOCR can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is HunyuanOCR?
At Q4_K_M, HunyuanOCR can reach ~4486 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~612 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 ÷ 1.1 × 0.65 = ~4860 tok/s
Estimated speed at Q4_K_M (1.1 GB)
~4860 tok/s~612 tok/s~4860 tok/s~4486 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of HunyuanOCR?
At Q4_K_M, the download is about 0.67 GB. The full-precision BF16 version is 2.24 GB. The smallest option (IQ2_XXS) is 0.31 GB.
- Which GPUs can run HunyuanOCR?
52 consumer GPUs can run HunyuanOCR at Q4_K_M (1.1 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 HunyuanOCR?
59 devices with unified memory can run HunyuanOCR at Q4_K_M (1.1 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.