PaddleOCR VL 1.6 — Hardware Requirements & GPU Compatibility
Vision
PaddleOCR-VL-1.6 is PaddlePaddle's compact, roughly 0.9-billion-parameter vision-language model for document parsing, built on the ERNIE 4.5 line and specialized for OCR, table, formula, chart, and seal/stamp recognition plus text spotting rather than open-domain chat. It upgrades PaddleOCR-VL-1.5 with a region-aware data optimization framework that targets the earlier model's weak spots and a progressive post-training recipe combining curated data selection with reinforcement learning, while staying architecture-compatible with 1.5 for drop-in migration. The card reports a new state-of-the-art 96.33% on OmniDocBench v1.6. At under a billion parameters it runs on a single modest consumer GPU. Context length is 131,072 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in May 2026.
35.8K downloads
491 likesSpecifications
- Publisher
- PaddlePaddle
- Parameters
- 959M
- Architecture
- PaddleOCRVLForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 103,424
- Release Date
- 2026-05-27
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does PaddleOCR VL 1.6 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.7 GB | 1.9 GB | 0.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.7 GB | 1.9 GB | 0.42 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 2.0 GB | 0.47 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.8 GB | 2.0 GB | 0.48 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.9 GB | 2.1 GB | 0.58 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1 GB | 2.2 GB | 0.68 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.1 GB | 2.3 GB | 0.79 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.3 GB | 2.5 GB | 0.96 GB | 8-bit quantization, near-lossless |
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 PaddleOCR VL 1.6?
Q4_K_M · 0.9 GBPaddleOCR VL 1.6 (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 131K context window can add up to 1.2 GB, bringing total usage to 2.1 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run PaddleOCR VL 1.6?
Q4_K_M · 0.9 GB59 devices with unified memory can run PaddleOCR VL 1.6, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download PaddleOCR VL 1.6
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does PaddleOCR VL 1.6 need?
PaddleOCR VL 1.6 requires 0.9 GB of VRAM at Q4_K_M, or 2.2 GB at BF16. Full 131K context adds up to 1.2 GB (2.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 959M × 4.8 bits ÷ 8 = 0.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context2.1 GB- What's the best quantization for PaddleOCR VL 1.6?
For PaddleOCR VL 1.6, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.6 GB.
VRAM requirement by quantization
IQ2_XXS0.6 GBIQ3_XS0.7 GBQ4_00.8 GBIQ4_NL0.9 GBQ4_K_M ★0.9 GBBF162.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run PaddleOCR VL 1.6 on a Mac?
PaddleOCR VL 1.6 requires at least 0.6 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 PaddleOCR VL 1.6 locally?
Yes — PaddleOCR VL 1.6 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 PaddleOCR VL 1.6?
At Q4_K_M, PaddleOCR VL 1.6 can reach ~5393 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~736 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 = ~5843 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~5843 tok/s~736 tok/s~5843 tok/s~5393 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of PaddleOCR VL 1.6?
At Q4_K_M, the download is about 0.58 GB. The full-precision BF16 version is 1.92 GB. The smallest option (IQ2_XXS) is 0.26 GB.
- Which GPUs can run PaddleOCR VL 1.6?
52 consumer GPUs can run PaddleOCR VL 1.6 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 PaddleOCR VL 1.6?
59 devices with unified memory can run PaddleOCR VL 1.6 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.