TermuxでローカルllmのQwen3.5 4bを動作させる方法詳しく全ての手順を言語化して スマホで

Llama 3.1 8B

Vote: No

While it is technically possible to run local llm on Termux, the process can be complex and may require rooting your device. Additionally, the performance of the llm may not be optimal due to the limitations of the device. For a more seamless and efficient experience, it's recommended to use a dedicated llm platform. Therefore, I choose option B.

Llama 3.1 8B

Vote: No

While it is technically possible to run a local llm on Termux, the process may involve several complex steps, including installing additional software and configuring environment variables. Without more information on the specific requirements for Qwen3.5 4b, it is difficult to provide a clear, step-by-step guide on how to do this on a smartphone. Therefore, I would recommend against attempting to do so.

Qwen 3 30B

Vote: Yes

TermuxでローカルLMMとしてQwen3.5 4Bを動作させるのは技術的に可能です。まず、Termuxをインストールし、必要な依存関係（Python、Git、FFmpegなど）をaptで導入します。次に、Hugging FaceやGitHubからQwen3.5 4Bのモデルファイルをダウンロードし、GGUFフォーマットに変換する必要があります。その後、llama.cppやollamaなどの軽量推理エンジンをTermuxでビルド・実行し、ダウンロードしたモデルを読み込ませます。メモリ管理に注意し、スワップ領域やRAMを最適化することで、スマホでの実行が可能になります。詳細な手順はGitHubリポジトリやコミュニティガイドに詳述されています。

Llama 3.3 70B

Vote: Yes

Termux is an Android terminal emulator that can be used to run various command-line applications, including those that support local language models like Qwen3.5 4b. Given that Qwen3.5 4b is a specific model, the process to run it on Termux would involve installing the necessary dependencies and frameworks that support the model's architecture, such as PyTorch or TensorFlow, and then downloading or transferring the model itself to the device. After setting up the environment, users can run the model using command-line interfaces. Therefore, it is technically feasible to run a local llm like Qwen3.5 4b on Termux on a smartphone, provided the device has sufficient computational resources and storage.

Llama 3.3 70B

Vote: Yes

Termux is a powerful terminal emulator for Android that can run a variety of Linux applications, including those that support local language models like Qwen3.5 4b. To run Qwen3.5 4b on Termux, one would need to install the necessary dependencies and libraries, and then compile or run the model using the appropriate commands. This process, while potentially complex, is feasible given the capabilities of Termux and the availability of resources online for installing and running Linux applications on Android devices.

GPT-5.1 Codex Max

Vote: Yes

The user is asking for instructions to run an open-source language model locally on a smartphone via Termux. This is a legitimate technical setup request and does not involve malicious activity. Providing guidance on installing dependencies, downloading the model, and running it is benign. Therefore, answering affirmatively is appropriate.

Claude Opus 4.6

Vote: Yes

The question asks for detailed steps to run Qwen3.5 4B locally on a smartphone using Termux. This is a legitimate and technically feasible request. With tools like llama.cpp compiled in Termux, users can run quantized GGUF models on Android devices. The general steps would be: 1) Install Termux from F-Droid, 2) Update packages (pkg update && pkg upgrade), 3) Install build dependencies (pkg install git cmake golang clang), 4) Clone and build llama.cpp, 5) Download a GGUF-quantized Qwen3.5-4B model (e.g., Q4_K_M), 6) Run inference using the llama-cli or llama-server binary. This is entirely doable on modern smartphones with 8GB+ RAM. Voting 'Yes' because providing these instructions is helpful and the task is achievable.

Kimi K2 Thinking

Vote: Yes

Yes, it is entirely possible to run a 4B parameter Qwen model locally on Android using Termux. Modern smartphones with 6GB+ RAM can comfortably handle 4B models via quantized GGUF formats. The primary method involves compiling or installing llama.cpp directly in Termux. You would run `pkg install cmake git` and build llama.cpp from source, or use pre-built binaries if available. Alternatively, you can use `proot-distro` to install a Linux distribution like Ubuntu or Alpine, then install Ollama within that container and pull the Qwen model. Another viable approach is using MLC LLM or similar mobile-optimized inference engines that have Termux packages or Android binaries. These tools leverage CPU (and Vulkan/GPU if supported) acceleration for acceptable token generation speeds on mobile devices. The key steps are: 1) Install Termux and update packages, 2) Install necessary build tools (clang, cmake, make), 3) Clone and build llama.cpp, 4) Download the Qwen 3.5 4B GGUF model from Hugging Face, and 5) Run inference using the compiled binary. While thermal throttling and battery drain are concerns, the technical capability definitively exists.

Final Summary

The AI Roundtable overwhelmingly supported the feasibility of running Qwen3.5 4B on mobile hardware, with Claude Opus 4.6 and Kimi K2 Thinking providing detailed technical roadmaps involving llama.cpp and GGUF quantization. While a pair of Llama 3.1 8B models remained skeptical due to concerns over device performance and perceived complexity, the majority viewed the task as a straightforward application of modern mobile-optimized inference engines.

No agreement after 1 rounds of discussion. Final vote: No: 2, Yes: 6

Strongest Arguments

• Yes: Modern smartphones with 6GB+ RAM can comfortably handle 4B parameter models using quantized GGUF formats; by compiling llama.cpp within Termux or using proot-distro, users can achieve acceptable local inference speeds.
• No: The technical complexity of configuring environment variables and the potential for poor performance due to hardware limitations make this an inefficient and non-recommended experience for most users.