Ollama Performance Issues In Termux: What You Need To Know

If you're a Termux user who's been experiencing a significant slowdown when running Ollama, especially with local LLM models, you're definitely not alone. Many users have reported a severe performance regression that seems to have started with Ollama version 0.11.5. This article dives deep into this issue, what might be causing it, and how you can potentially work around it. We'll cover the symptoms, explore some reproduction steps, and discuss the expected behavior you should be seeing. For those of you running your large language models on your Android devices via Termux, this slowdown can be a real buzzkill, impacting everything from testing new models to using them for daily tasks. It's frustrating when a tool that's supposed to boost productivity suddenly becomes a bottleneck. We understand how important it is to have your LLMs running smoothly, and we're here to shed some light on this particular performance hiccup. The good news is that older versions of Ollama, specifically 0.11.4 and below, don't seem to suffer from this problem, which gives us a crucial clue. Furthermore, this issue appears to be Termux-specific, as reports indicate it doesn't manifest on Linux systems, even without GPU acceleration. This distinction is key in pinpointing the root cause. We'll guide you through the process of identifying the problem and provide the context needed to understand why this might be happening. So, grab your device, and let's get to the bottom of this Ollama performance puzzle in Termux.

Understanding the Ollama Performance Regression in Termux

The core of the problem lies in a noticeable and drastic reduction in generation speed for local LLM models within Ollama, specifically when using versions 0.11.5 and any subsequent releases. To put it into perspective, if you were getting a decent token generation rate with Ollama version 0.11.4, you might now be experiencing speeds that are 5 to 10 times slower with the newer versions. This is not a minor glitch; it's a substantial performance hit that can render the LLM experience on Termux quite frustrating. The issue has been observed and confirmed across different devices, including the Redmi Note 8 and Samsung Galaxy A04s, both running Android. This widespread occurrence on specific hardware reinforces the idea that it's a systemic problem rather than an isolated incident. Crucially, this performance degradation does not appear when using llama-cli directly, which is the underlying tool provided by the llama-cpp package. This observation is critical because it suggests that the issue isn't with the llama.cpp library itself, but rather how Ollama is interacting with or building its llama.cpp component within the Termux environment. The fact that this performance regression is Termux-specific is another vital piece of the puzzle. When tested on a Linux system (specifically Arch Linux in one user's case), even without GPU acceleration, Ollama performed as expected, matching the speed of older versions. This strongly points towards an environment-specific configuration, build flag, or optimization issue within the Termux package build process for Ollama. The original reporter of this issue speculated that it might be related to compiler optimizations or flags used during the build of Ollama's version of llama.cpp. It's possible that certain compiler flags intended for optimization are being overridden or are not being applied correctly in the Termux build environment, leading to this significant performance penalty. Without further investigation into the build process, this remains a strong hypothesis. The implications of this bug are far-reaching for Termux users who rely on Ollama for various tasks. Whether you're experimenting with new LLMs, integrating them into scripts, or using them for code generation, the dramatically slower speeds can impede workflow and reduce the overall usability of these powerful models on mobile devices. This article aims to provide a comprehensive overview of the situation, backed by user-reported data and testing steps, to help the community understand and potentially contribute to a solution.

Reproducing the Ollama Performance Bug in Termux

To truly understand and help resolve the Ollama performance regression in Termux, it's essential to be able to reproduce the bug consistently. The good news is that there are clear steps provided by the community to replicate this issue. This not only helps in diagnosing the problem but also allows developers to verify fixes. For those looking to test this, you'll need a bit of storage space, ideally 1-2GB, to download the necessary files. The most straightforward way to test is by using an automated reproduction script. This script streamlines the process, ensuring all dependencies are met, models are downloaded, and tests are run automatically. Before you start, ensure your Termux environment is set up correctly. Running termux-setup-storage is usually the first step if you haven't already, granting Termux access to your device's storage. The automated script relies on having specific older versions of Ollama available. You can obtain these by downloading a zip archive containing .deb files for older Ollama builds (ollama_0.11.4_aarch64.deb and ollama_0.11.5_aarch64.deb). These files were historically available through GitHub Actions, but it's important to note that these links might expire. The archive should be extracted directly into your Android's /storage/emulated/0/Download directory. Once the older builds are in place, you'll download a bash script (ollama-repro.sh) which automates the entire process. This script needs to be moved to your Termux home directory and made executable using chmod +x ollama-repro.sh. Running ./ollama-repro.sh will then handle installing dependencies, pulling models, and performing the performance tests. It's highly recommended to enable the