Ollama ROCm Crashing GPUs: A Deep Dive

Hey everyone! 👋 I've been wrestling with a pretty gnarly issue involving Ollama's ROCm support and my AMD GPU (gfx1030), and I wanted to share my findings and see if anyone else has run into this. Basically, after updating my Ollama Docker image, my entire system started crashing hard. We're talking full-on reboots required, no graceful recovery – the works! Let's break down what's happening and how to potentially address it.

The Core Problem: Ollama ROCm and the System Crash 💥

So, the headline is the gist of it: Ollama ROCm versions v0.12.6-rc0 and later are causing my gfx1030 GPU and the entire Linux system to crash. No error logs, no warnings – just a sudden, brutal shutdown that necessitates a full reboot. This is pretty disruptive, especially when you're in the middle of a project or, like me, relying on Ollama for some heavy lifting.

The issue manifested after I did a docker pull and my stack went kaput. After some digging, I traced the problem back to the specific Ollama ROCm Docker image versions. I spent a whole day going through different versions, trying to figure out what was breaking the system. It quickly became clear that something changed between v0.12.5 (which worked fine) and v0.12.6-rc0 (which brought the pain). It's always a bit of a heart-sink moment when an update wrecks everything! 😩

For those unfamiliar, ROCm (Radeon Open Compute) is AMD's platform for high-performance computing, including GPU acceleration. Ollama uses ROCm to leverage AMD GPUs for running large language models (LLMs). This is amazing when it works, but when things go sideways, it's a real headache. The crashes I've experienced are complete system lockups. No amount of killall, kill -9, or Docker service restarts can bring the system back to life. It's a hard stop, and only a reboot can get things moving again. Pretty frustrating, right? 😤

Impact and Troubleshooting

The impact is significant: any work in progress is lost, and the system is unavailable until the reboot completes. Troubleshooting has been tricky due to the lack of informative logging. There aren't any clear error messages to point to the root cause, which makes pinpointing the problem even harder. I've been through the usual suspects: checking system logs, monitoring GPU temperatures, and verifying driver versions. But so far, nothing has provided a definitive answer beyond the version number correlation. It seems the problem lies deep within the interaction between Ollama, ROCm, and the AMD GPU drivers. Hopefully, we can find a solution together.

Diving into the Technical Details 🧐

Alright, let's get a little more technical, guys. This is where we try to understand the why behind the what. I’m running a Linux-based system (specific details below), which serves as the foundation for the Docker environment where Ollama resides. The AMD GPU, identified as gfx1030, is the key player here. It handles the intense computations required for running LLMs, and it's the component that's taking the hit. The specific Docker image versions (v0.12.6-rc0 and later) seem to introduce a conflict or incompatibility. This is something that seems to cause the GPU to go haywire and crash the entire system. 😠

The absence of helpful log messages is a major hurdle. When a crash occurs without any accompanying error information, it's like trying to navigate in the dark. It makes it extremely hard to diagnose the underlying issue. Standard system logs typically don't reveal much, and the Docker logs for the Ollama container are equally unhelpful. This lack of feedback forces us to rely on version comparisons, testing, and community input. We have to try to narrow down the problem in the absence of explicit error messages.

Kernel and Driver Versions

I'll be sure to provide the exact kernel version, ROCm driver versions, and other relevant details as soon as possible. Because my PC keeps crashing, I had to use another device to file this bug report. It is my top priority to get this information to the forefront to further assist any future debugging efforts and see if anyone else has a solution, or perhaps even a similar experience that can provide more clues.

I’ll also explore potential workarounds and solutions, such as downgrading to a stable Ollama version or trying different ROCm driver configurations. However, the ideal scenario would be a fix within the newer Ollama versions to ensure compatibility and stability. This will help leverage the latest features and optimizations. The community's collective knowledge can provide effective remedies, so please feel free to share any insights or experiences you may have. 😎

Potential Causes and Workarounds 🤔

So, what could be causing this? Well, without detailed logging, we're left to speculate a bit. Here are some of the potential culprits and some temporary workarounds we can explore:

• Driver Incompatibility: There might be a conflict between the Ollama ROCm version and the specific AMD GPU drivers installed on my system. Driver updates can sometimes introduce regressions or unexpected behavior. Compatibility is critical when working with GPU acceleration.
• ROCm Library Bugs: Bugs within the ROCm libraries could trigger instability, particularly if they are not fully tested with the specific hardware and software configuration. These libraries are the backbone for GPU computation, so problems here can have a widespread impact.
• Resource Conflicts: Ollama might be competing with other processes or applications for GPU resources, leading to instability. Resource management can be tricky when running multiple demanding applications simultaneously.
• Memory Issues: The LLMs being loaded might be pushing the GPU's memory limits, causing the system to crash. Large models can be very demanding on GPU memory. Memory allocation and deallocation issues can also contribute to system crashes.
• Ollama Version Bugs: There could be bugs within the newer Ollama versions themselves that cause the crashes. Rollbacks to the last known working versions might provide an immediate solution if this is the case.

Workarounds to Try

1. Downgrading Ollama: The easiest and most effective temporary fix is to revert to a known stable Ollama ROCm version (like v0.12.5). This is the immediate solution to prevent crashes and ensure system stability. This is a common and reliable strategy until the underlying issue is resolved in a newer version.
2. Driver Experiments: Try updating or downgrading your AMD GPU drivers. Sometimes, newer drivers fix problems, but other times, they introduce new ones. Always create a system backup before making significant changes to driver configurations. Experimenting with different driver versions can sometimes reveal a more stable setup.
3. Resource Monitoring: Monitor GPU memory usage and temperature during model execution. Excessive memory usage or overheating could indicate the problem. Keep an eye on resource usage using tools like nvidia-smi (even though it's an AMD GPU, it still often works on Linux to monitor GPU stats). High temperatures can also lead to system instability, so ensure your cooling system is adequate.
4. Model Optimization: If you're using large language models, try using smaller models or optimizing the model loading process. Some models are inherently more demanding than others. Experiment with model quantization to reduce memory usage and improve performance. This can also prevent excessive resource consumption.
5. Clean Docker Setup: Ensure your Docker environment is clean and that there are no conflicting containers or images. Run a docker system prune -a to remove unused images and containers. A fresh setup often eliminates potential conflicts that might be contributing to the crashes.

Seeking Community Input and Future Steps 🙏

I'm opening this up to the community, hoping others have experienced something similar. If you're running Ollama with ROCm on an AMD GPU, particularly a gfx1030, and you've seen similar crashes, please chime in! Sharing your experiences, system configurations, and any workarounds you've found can be incredibly valuable.

Call for Help and Further Investigation

I’ll be continuing to investigate this issue, trying different configurations, and collecting more data. I plan to:

• Gather Detailed System Information: Provide specific details about my OS, kernel version, ROCm driver versions, and other relevant information to help diagnose the problem.
• Test Different Ollama Versions: Conduct further testing with various Ollama ROCm versions to pinpoint the exact version where the issue started.
• Explore ROCm Driver Configurations: Experiment with different ROCm driver settings and configurations.
• Reach Out to Ollama Developers: Reach out to the Ollama developers for assistance and to report the bug.
• Analyze System Logs: Investigate system logs and Docker logs more thoroughly (if I can get them to generate any).

This is a team effort, guys! Together, we can hopefully find a solution or at least a workaround to get things running smoothly again. Thanks for reading and for any help you can offer! 🙌 Let's get those LLMs running stable again!

If you have any insights, suggestions, or questions, please drop them in the comments below. Let's get this fixed! 🚀