Qwen 3.5 Small: Best Open-Source LLM for Running AI on Your Phone

Qwen 3.5 Small, a new addition to the landscape of language models (LLMs), has just been released by Alibaba Cloud and it packs a punch. At only 9 billion parameters, this model outperforms larger models that are up to 13 times its size in graduate-level reasoning tasks. This performance makes Qwen 3.5 Small not only groundbreaking but also ideal for running AI directly on your phone.

What is Qwen 3.5 Small?

Qwen 3.5 Small is a part of the larger Qwen model family, built by Alibaba Cloud. It features an impressive 9 billion parameters and operates under the Apache 2.0 license, ensuring that it is accessible for both academic research and commercial applications. One unique feature of Qwen is its hybrid thinking mode, activated with the /think toggle command, which allows the model to simulate deeper and more comprehensive thinking processes.

Benchmark Blowout

To understand why Qwen 3.5 Small stands out, let's take a look at some key benchmarks:

Comparison Table: Qwen 3.5 8B vs Competitors

Explanation of Benchmarks

• GPQA Diamond: The Qwen 3.5 model significantly outperforms the Llama and Gemma models in geometric proof solving, a tough test for mathematical reasoning.
• MATH-500: Similarly robust performance is shown in arithmetic reasoning tasks, surpassing both Llama and Gemma by around 14% and 9%, respectively.
• LiveCodeBench: In coding performance, Qwen’s code generation capabilities are 40% better than Llama and 23% better than Gemma.

These benchmarks demonstrate that even smaller models like Qwen can compete favorably with much larger ones on specialized tasks. This makes it an exceptional choice for mobile use where resources are limited but the need for AI is growing.

Model Size Variants

The Qwen family includes several variants, each tailored to different performance and resource requirements:

• Qwen 3.5 0.6B
• Qwen 3.5 1.7B
• Qwen 3.5 4B
• Qwen 3.5 8B *(small)*
• Qwen 3.5 14B
• Qwen 3.5 32B
• Qwen 3.5 30B-A3B

VRAM/RAM Requirements Per Size

For mobile devices, the smaller variants like Qwen 3.5 8B and below are typically more feasible to run efficiently.

Running on Phone

Running LLMs on mobile devices is a challenge due to hardware constraints. With the right setup, however, you can leverage even the powerful Qwen 3.5 Small model directly on your smartphone.

Setting up with MLC LLM (Android/iOS)

1. Install MLC Chat: Download and install MLC Chat from the Google Play Store or App Store. This app provides an interface to run different models on mobile.

2. Download Qwen 3.5 Small Model: Access a Qwen 3.5 Small model file in .gguf format and place it in your device's storage.

3. Load the Model in MLC Chat:

- Open MLC Chat app.

- Navigate to settings and select "Import Models".

- Browse to the location where the Qwen 3.5 Small model is stored.

Setting up with llama.cpp (Android/iOS)

Using llama.cpp for mobile devices requires more technical skills:

1. Install Termux: Download Termux from the Google Play Store or App Store.

2. Clone llama.cpp Repository:

`bash

git clone https://github.com/ggerganov/llama.cpp.git

cd llama.cpp

`

3. Compile and Run:

Follow the build instructions specific to your device’s architecture in the repository's README.

4. Download Model: Use wget or a similar tool to download Qwen 3.5 Small model in .bin format if needed.

Compatibility with Different Phone RAM

Running on Laptop

For those with more powerful hardware, running Qwen 3.5 Small or even larger variants becomes feasible.

Setting Up with Ollama (All Platforms)

Ollama is a versatile tool for deploying LLMs locally:

1. Download and Install Ollama:

- For MacOS or Windows/Linux.

2. Import Qwen 3.5 Model:

`bash

ollama import qwen_3_5_small.gguf

`

3. Run Ollama Server:

- Use the command ollama server and access through your browser or API calls.

Performance on Apple Silicon (M3/M4) & Budget GPUs (RTX 4060)

• Apple Silicon M1/M2/M3/M4: These chips provide impressive performance even with larger models like Qwen 8B.
• Budget GPUs RTX 4060: Ideal for running medium-sized LLMs smoothly, handling up to Qwen 14B.

Qwen 3.5 vs Llama 3.3 vs Gemma 3: Head-to-Head

Benchmark Comparison Table (Similar Parameter Counts)

Verdict: Qwen 3.5 outperforms both Llama and Gemma on all three benchmarks for models of similar sizes.

The MoE Secret Weapon

The Multi-Expert (MoE) architecture in Qwen 3.5 30B-A3B brings significant efficiency gains. This model has 30 billion parameters but only activates 3 billion at a time, drastically reducing memory usage and processing load while maintaining high performance.

Best Use Cases

Ideal Applications for Qwen 3.5 Small

• Coding Assistant on the Go: Leverage real-time code completions and debugging assistance anywhere.
• Private AI Chat: Enjoy conversations with an AI without sharing data to third parties.
• Offline Translation: Translate text even when you do not have internet access.
• Document Summarization: Quickly summarize long documents without needing a connected device.

Recommendation

Choosing the right Qwen 3.5 variant for your device depends on both technical specifications and your usage needs:

• For Mobile Devices:

- If your phone has at least 12GB RAM, consider Qwen 3.5 8B or even Qwen 3.5 4B.

- For devices with limited RAM (under 8-10GB), Qwen 3.5 1.7B is recommended.

• For Laptops:

- With Apple Silicon M3/M4: Try out larger models like Qwen 3.5 14B or even larger.

- For budget GPUs and systems with at least 8GB RAM, using Qwen 3.5 8B provides a good balance of performance and resource usage.

FAQ

Q: Can Qwen 3.5 run on any Android phone?

A: While theoretically possible, it's most optimal on higher-end devices with at least 12GB of RAM. Devices with less memory will likely experience lag.

Q: Do I need a GPU to run Qwen 3.5 Small locally?

A: No, some models like Qwen 3.5 8B can run entirely on CPU, though having a GPU will improve performance and reduce latency, especially for larger models.

Q: How do I train my own data with Qwen 3.5?

A: Training custom data requires significant computational resources. For local training, consider using Ollama or other deep learning frameworks. Alternatively, you can fine-tune the model on cloud services like Vast.ai.

Conclusion

Qwen 3.5 Small represents a breakthrough in the field of LLMs by offering exceptional performance at a fraction of the resource requirements typically needed for models of similar caliber. Whether you're an AI enthusiast looking to run models on your phone or a developer seeking efficient local solutions, Qwen 3.5 Small is a standout choice. By choosing the right variant and setup method, you can harness its capabilities to enhance productivity and exploration in various applications.

For those keen on leveraging even more powerful versions of Qwen, consider exploring devices with higher-end hardware such as the Mac Mini M4 or budget-friendly mini PCs from Amazon. Additionally, cloud GPUs through services like Vast.ai can support running larger models without needing robust local hardware setups. Happy experimenting with Qwen 3.5 Small!

Practical Applications on Mobile Devices

Running Qwen 3.5 Small on your phone opens up a plethora of possibilities, from enhanced productivity to immersive gaming experiences. Here are some practical applications:

Personal Productivity

• Note-Taking and Organization: Use Qwen 3.5 Small to summarize long notes, generate to-do lists, and categorize information on the go.
• Language Translation: Instantly translate text between multiple languages, making international communication seamless.
• Content Creation: Draft emails, social media posts, and even short articles with the help of Qwen’s natural language generation capabilities.

Mobile Gaming

• AI-Driven NPCs: Integrate Qwen into mobile games to create more intelligent and adaptive non-player characters (NPCs).
• Dynamic Storytelling: Enhance storytelling in games by generating unique narratives and dialogue options based on player actions.

Educational Tools

• Interactive Learning: Develop educational apps that provide personalized learning experiences, offering explanations and answering questions in real-time.
• Language Learning: Create language learning applications that offer instant feedback and practice exercises tailored to individual learning styles.

How to Run Qwen 3.5 Small on Your Phone

Running Qwen 3.5 Small on a mobile device requires some technical setup, but it’s achievable with the right tools and hardware. Here’s a step-by-step guide:

Hardware Requirements

• Phone Model: Ensure your phone has sufficient processing power and memory. Devices with at least 8GB of RAM and a powerful processor like the Qualcomm Snapdragon 8 Gen 2 are recommended.
• Storage: At least 16GB of free storage is needed to accommodate the model and its data.

Software Setup

1. Install a Compatible App: Use apps like Termux or PyTorch Mobile, which support running AI models on mobile devices.

2. Download Qwen 3.5 Small: Obtain the Qwen 3.5 Small model from the official Alibaba Cloud repository.

3. Set Up Environment: Install necessary libraries and dependencies required to run the model. This may include Python and specific AI libraries.

4. Run the Model: Execute the model using the installed app. You may need to write a script to interface with the model and handle input/output.

Advanced Features and Customization

Qwen 3.5 Small offers several advanced features that can be customized to suit specific needs:

• Hybrid Thinking Mode: Activate the /think toggle command to enable deeper reasoning processes, which can be particularly useful in complex tasks.
• Custom Prompts: Tailor the model’s responses by providing specific prompts or instructions, allowing for more accurate and relevant outputs.
• Integration with APIs: Connect Qwen 3.5 Small with other APIs to expand its functionality, such as integrating with cloud storage services for data handling.

Future Developments

Alibaba Cloud continues to invest in advancing Qwen, with plans to release even more efficient and powerful versions in the future. The next iteration, Qwen 4.0, is expected to launch in 2026, promising even better performance and additional features.

Key Takeaways

• Performance: Qwen 3.5 Small outperforms larger models in key benchmarks, making it a standout choice for mobile applications.
• Accessibility: With its open-source nature and Apache 2.0 license, Qwen 3.5 Small is accessible for both research and commercial use.
• Versatility: Its applications range from personal productivity to gaming and education, showcasing its broad utility.
• Future Prospects: Ongoing development by Alibaba Cloud ensures continued improvements and new features in future releases.

Additional Resources

For more information on leveraging AI models on mobile devices, check out our articles on best practices for mobile AI and hardware recommendations for AI enthusiasts.