HyperVL: How to Run Powerful Multimodal AI Smoothly on Your Phone

Have you ever imagined having an assistant as smart as ChatGPT right on your smartphone—one that can not only chat with you but also “see” the photos in your gallery, understand screenshots, and even extract information from complex charts? The reality, however, has been harsh. Those powerful Multimodal Large Language Models (MLLMs) typically require massive computational servers. Running them directly on edge devices like phones has seemed nearly impossible.

The primary roadblock is the enormous computational load and memory consumption required to process high-resolution images. But recently, a new research breakthrough named HyperVL offers a promising solution. It is an efficient and dynamic multimodal large language model specifically designed for on-device inference, significantly reducing latency and power consumption while maintaining impressive capabilities.

Today, let’s dive deep into the “secret sauce” behind HyperVL and unpack how it enables large models to run efficiently on resource-constrained devices like phones and tablets.

The “Multimodal Dilemma” for Edge Devices: Why is it So Hard to Run Large Models on Phones?

To appreciate HyperVL’s breakthrough, we must first understand the problem it aims to solve.

In recent years, multimodal large models like GPT-4o, Gemini, Claude, and Qwen-VL have advanced rapidly, demonstrating remarkable abilities in cross-modal understanding, visual reasoning, and OCR (Optical Character Recognition). Simultaneously, the demand for on-device AI capabilities has surged. Local processing enhances user privacy and avoids the high costs associated with cloud inference.

The core contradiction is this: These powerful models are primarily designed for the cloud. Their complex architectures and massive parameter counts (often tens or hundreds of billions) make them incredibly difficult to run efficiently on devices with highly constrained compute and memory budgets, such as smartphones and tablets.

A fundamental bottleneck is the visual encoder. Most current multimodal models rely on standard Vision Transformers (ViTs) to “interpret” images. ViTs have a critical weakness: their computational complexity grows quadratically with the resolution of the input image. When processing high-resolution inputs common on devices—like screenshots for UI understanding or photos for object recognition—this leads to prohibitively high memory usage and noticeable inference latency.

It’s akin to asking a compact car to tow a heavy freight trailer. Even if the engine (the efficient, smaller-parameter language part of the model) is capable, the drivetrain (the visual encoder) becomes the overwhelming bottleneck.

HyperVL was created to tackle this exact challenge, achieving an outstanding balance between performance and efficiency through a series of ingenious designs.

The Core Architecture of HyperVL: Three Key “Innovations” to Solve Efficiency Challenges

HyperVL’s goal is clear: deliver the strongest possible multimodal understanding within strict resource limits. It introduces three key technical innovations to achieve this.

Innovation #1: Image Tiling Strategy — “Divide and Conquer” to Control Peak Memory

When faced with a high-resolution image, a standard ViT attempts to process the entire image at once, generating massive intermediate activations (temporary data during computation) that can easily exhaust a mobile device’s limited memory.

HyperVL adopts an intuitive yet effective strategy—image tiling. It splits the high-resolution input into multiple smaller, non-overlapping patches and encodes these patches serially. The key benefit is that regardless of the original image size, the model only processes one fixed-size small patch at a time. This caps the peak memory consumption at a constant, low level.

Think of it like reading a thick book. Instead of trying to comprehend all the content in one glance (leading to information overload), you read page by page, understanding the information on each page sequentially.

Innovation #2: Visual Resolution Compressor — “Compute on Demand”

Do we really need to process every image at its highest resolution? Not really. For instance, recognizing if a cat is in a picture might not require seeing every single fur strand, but interpreting a legal document with fine print demands high clarity.

HyperVL incorporates a lightweight, plug-and-play Visual Resolution Compressor (VRC). It acts like an intelligent “image inspector”:

1. Rapid Assessment: Before full processing, a tiny neural network (like MobileNet) quickly analyzes the image to gauge its information density and complexity.
2. Dynamic Decision: Based on this analysis, it predicts an optimal compression ratio (from 10% to 100% of the original size).
3. Adaptive Processing: It scales the original image according to the predicted ratio before feeding it to the visual encoder.

This means for simple images, the VRC selects a high compression ratio, drastically reducing the number of visual tokens for subsequent processing and directly lowering computational load for both the ViT and the LLM. For complex, detail-rich images, the VRC preserves high resolution to ensure task accuracy. Experiments show the VRC adds a minimal overhead of just 2 milliseconds while reducing visual tokens by 20-30% on average and retaining over 98% of task performance.

(The VRC workflow: determining the optimal compression ratio per image during training, and applying it dynamically during inference.)

Innovation #3: Dual Consistency Learning — “Large and Small Model” Collaboration

Different devices have varying computational power, and different tasks have different accuracy requirements. HyperVL tackles this need for dynamism with a dual-branch visual encoder architecture.

• Large Branch: Uses a more powerful, higher-parameter visual encoder (e.g., SigLIP2-Large, 300M parameters) for high-precision features.
• Small Branch: Uses a lighter, lower-parameter visual encoder (e.g., SigLIP2-Base, 93M parameters) for high-efficiency features.
• Shared Core: Both branches share the same Large Language Model backbone (e.g., Qwen3 1.7B).

The crucial question is: how can the lightweight small branch learn the “essence” of the powerful branch? HyperVL employs a Dual Consistency Learning (DCL) strategy:

1. Alternating Training: During training, the two branches are activated alternately, forcing them to align within a unified semantic space.
2. Knowledge Distillation: The large branch acts as the “teacher,” and the small branch as the “student.” By minimizing the divergence (using KL-divergence loss) between their output distributions, the student branch’s output is guided to approximate the teacher’s, thereby acquiring similar semantic understanding capabilities.

(The core HyperVL architecture, including the VRC, dual-branch visual encoders, projector, and shared LLM.)

During on-device deployment, the system can dynamically switch between the small branch (for speed) and the large branch (for accuracy) based on current battery level, computational load, or task type, maximizing flexibility.

From Data to Training: Building the Foundation of a “Generalist” Model

A powerful model is built on high-quality, diverse training data. The HyperVL team constructed an exceptionally large-scale multimodal training corpus covering almost every conceivable visual understanding task:

To handle this vast, heterogeneous data, the team implemented a rigorous data governance pipeline involving data preparation/categorization, cleaning/normalization, and quality filtering/mixed packaging, ensuring the final training samples were of high quality and consistency.

With quality data in place, training proceeded in three progressive stages to unlock the model’s capabilities:

1. Vision-Language Alignment Stage: Freeze the parameters of the visual and language models, training only the projection adapter. This teaches the model to initially “translate” visual features into a space the LLM understands.
2. Knowledge Enhancement Stage: Unfreeze most parameters for full-parameter pre-training using diverse image-text and text-only data. The model absorbs broad visual and world knowledge.
3. Multi-Task Training Stage: Train on curated, high-quality multi-task data (especially synthetic data containing chains-of-thought) to further enhance the model’s complex reasoning and generalization abilities.

Putting It to the Test: How Does HyperVL Actually Perform?

Theoretical design is one thing; real-world performance is another. HyperVL was put through rigorous evaluation on both public benchmarks and internal business scenarios.

Public Benchmark Evaluation: A Comprehensive & Competitive Showcase

Researchers compared HyperVL against several top open-source models of similar parameter scale (~2B), including Qwen2-VL, Qwen3-VL, InternVL3.5, and SAIL-VL2.

Key Takeaway: Despite its base version having only 1.8B parameters (one of the smallest in the comparison), HyperVL’s overall performance (measured by OpenCompass average score) was competitive with many 2B+ models and excelled in several specific areas.

• OCR & Document Understanding: A standout strength for HyperVL. It scored 91.3 on DocVQA (document QA), 83.8 on ChartQA (chart QA), and 81.8 on AI2D (diagram reasoning), proving its exceptional ability to handle fine-grained visual structure and text.
• Comprehensive Multimodal Ability: HyperVL remained highly competitive on holistic benchmarks like MME and MMBench, indicating balanced capabilities.
• Hallucination Control: It showed stable performance on benchmarks like HallusionBench and POPE that evaluate factual hallucinations, suggesting reliable outputs.

When switching to the HyperVL-ViT_L variant (2.0B parameters) with a larger visual encoder, metrics improved consistently, demonstrating the framework’s good scalability.

Internal Business Benchmarks: Practical Utility Shines

Public tests measure general ability, while internal tests target real business applications. HyperVL demonstrated impressive results in four critical tasks:

1. Intent Recognition & Recommendation (Score: 94.0): Generate search queries reflecting user intent based on device screenshots. HyperVL ranked at the top here, showing powerful deep semantic understanding.
2. Image-Text Creation (Score: 49.8): Generate high-quality, context-aware text (e.g., for social media posts) based on a user-uploaded image and application scenario. HyperVL ranked #1 in this challenging task, showcasing superior creativity and multimodal alignment.
3. UI Understanding & Structured Parsing (Score: 84.2): Extract key field information from complex interfaces like order detail pages without predefined templates. While slightly below some models specialized for this, its performance is robust enough for downstream interactive applications.
4. Image Relevance Ranking (Score: 51.5): Precisely filter and rank candidate images based on their semantic relevance to a query. HyperVL also ranked #1 here, highlighting its fine-grained cross-modal matching ability, crucial for search and recommendation systems.

Crucially, HyperVL achieved these leading results while having the smallest parameter count (1.8B) among compared models, indicating an outstanding “performance-per-parameter” efficiency.

Efficiency & On-Device Deployment: Real Hardware, Real Results

The ultimate question for edge deployment: Can it actually run on a phone? How fast? What’s the memory footprint?

Testing on real mobile hardware with a Qualcomm Snapdragon platform revealed:

• Constant Memory Footprint: Thanks to image tiling, HyperVL’s peak memory usage remains stable regardless of input image resolution. In contrast, a standard ViT’s memory consumption skyrockets with resolution. HyperVL achieved up to a 6.8x reduction in peak memory.
• Linear Latency Scaling: While standard ViT latency grows exponentially with resolution, HyperVL’s latency scales almost linearly. For large images, this translated to a speedup of up to 12.9x.
• Quantization-Friendly: The model is robust to low-bit quantization (e.g., W4A16: 4-bit weights, 16-bit activations). The quantized model retained nearly all performance on tasks like DocVQA (dropping only 0.1 points) while significantly reducing memory bandwidth requirements, making it ideal for deployment on NPUs and other specialized hardware.

(Real-device tests demonstrate HyperVL’s significant advantages in memory and latency.)

The Road Ahead

HyperVL charts a clear path for deploying capable multimodal large models on edge devices. By addressing the visual encoder bottleneck through image tiling, dynamic resolution compression, and dual-branch collaboration, it successfully bridges the gap between capability and efficiency.

Looking forward, the research team plans to explore several directions:

• Incorporating adaptive token sparsification and attention pruning for further efficiency gains.
• Extending the model’s capabilities to video understanding and interactive scenarios.
• Integrating user-adaptive personalization, allowing the model to better understand individual users and optimize performance based on available resources.

Appendix: What Can HyperVL Do? — Qualitative Examples

Let’s look at a few concrete examples to intuitively grasp HyperVL’s capabilities:

1. Mathematical Calculation & Reasoning

• User Prompt: “Please solve this problem.” (The image shows a fractional equation)
• HyperVL Response: It not only recognizes the equation but also provides a detailed, step-by-step solution, arrives at the correct answer x = -1, and performs verification.

2. Chart Information Extraction

• User Prompt: “What’s the increase of step therapy between 2005 to 2013? Please calculate it step by step.”
• HyperVL Response: It accurately identifies the data points for 2005 (27%) and 2013 (67%) from the chart and calculates the increase as 40%.

3. GUI Understanding & Recommendation

• User Prompt: “How can I buy a phone most cost-effectively?” (In Chinese)
• HyperVL Response: It analyzes the screenshot of a shopping app, points out the “Up to 200 RMB off on phones” promotion, and guides the user to check the “Year-End Sale” and “Phone Buying Guide” for more information.

These examples vividly illustrate how an efficient, on-device multimodal model can become a genuinely useful intelligent assistant in our daily lives.

In summary, the emergence of HyperVL represents a significant step forward for multimodal large language models in the realm of edge computing. Through a series of innovative engineering and algorithmic designs, it finds an excellent equilibrium between performance, efficiency, and practicality. As this technology matures, every smart device could potentially host a locally-run, general-purpose AI companion, truly heralding a new era of human-computer interaction.