GPT-OSS 120B Crashes: Troubleshooting And Solutions

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Hey guys! Ever been super stoked to run a large language model, only to have it crash on you? Yeah, it's a total bummer. Today, we're diving deep into a specific issue with the GPT-OSS 120B model, where it loads up, starts running, but then bam!, crashes on that first, seemingly innocent query. We'll break down the problem, explore potential causes, and arm you with the knowledge to get this beast of a model running smoothly. Let's get started!

Understanding the Issue: GPT-OSS 120B Crashing After Initial Query

So, what's the deal? You fire up the ramalama tool, eager to chat with the massive GPT-OSS 120B model. It loads, looks good, and then you type your greeting – maybe a simple "Hi!". But instead of a witty response, you get the dreaded Error: could not connect to: http://127.0.0.1:8080/chat/completions. Ugh. It's like ordering a pizza and finding out they're out of dough after you've paid. Frustrating, right?

This error typically means there's a problem with the model server. It could be anything from resource limitations to software glitches. The 120B model is a hefty one, requiring significant resources, so we need to investigate if your system can handle the load. This initial crash often happens after the model appears to load successfully, making it even more puzzling. You see the GPU memory usage spike, indicating the model is loaded, but then it drops right back down after the crash. This suggests the process controlling the model is dying unexpectedly.

To really dig into this, we need to look at the steps to reproduce the issue and the symptoms observed. First, you'd run ramalama --debug run gpt-oss:120b. This command tells ramalama to run the GPT-OSS 120B model with debugging enabled, giving us a peek under the hood. Then, you wait for the prompt and try sending a simple message, like "Hi!". That’s when the pretty Braille spinner starts, taunting you with the promise of a response that never comes. Instead, boom, the error message appears. What you’ll see without the --debug flag is simply this:

$ ramalama run gpt-oss:120b
🦭 > hi
Error: could not connect to: http://127.0.0.1:8080/chat/completions
🦭 >

The frustrating part? It keeps doing that! But after the first attempt, the GPU memory usage doesn't even bother spiking up to that 60 GB "model loaded" state. It's like the system learned its lesson and is refusing to try again without some serious intervention. The expectation, of course, is a friendly greeting back from the robot, just like the smaller gpt-oss:20b model manages to do, and like LM Studio does on the same hardware. So, where do we even begin to unravel this mystery?

Key Symptoms of the Crash

Before we jump into solutions, let's nail down the key symptoms. This helps us narrow down the possible causes:

  • Crash on First Query: The model loads, but crashes specifically when you send the first query.
  • Connection Error: The error message is Error: could not connect to: http://127.0.0.1:8080/chat/completions, indicating a failure to connect to the model server.
  • GPU Memory Drop: GPU memory usage drops significantly after the crash, suggesting the model is being evicted from memory.
  • Container Disappearance: The ramalama_NOISE container disappears after the crash, indicating the process running the model has terminated.
  • Time Gap: There's a noticeable time gap (e.g., 16 seconds in the provided logs) between the request and the error, suggesting a potential timeout or processing issue.

With these symptoms in mind, we can start exploring the most likely culprits behind this crash.

Potential Causes and Troubleshooting Steps

Okay, so our GPT-OSS 120B pal is crashing on us. Let's put on our detective hats and investigate the prime suspects. We'll walk through potential causes, offering some troubleshooting steps along the way.

1. Insufficient Resources (RAM/VRAM)

This is the big one, especially with a model as massive as GPT-OSS 120B. These models need serious memory to operate. The user in our example has a beefy system with 128 GB of RAM and 96 GB dedicated to VRAM, which should be enough. However, let's double-check a few things:

  • Verify VRAM Allocation: Use tools like amdgpu_top (as the user did) or nvidia-smi to confirm that the VRAM is indeed allocated and available. Sometimes, what you think is allocated in UEFI isn't what the system is actually using.
  • Check RAM Usage: While VRAM is crucial for the model itself, RAM is still needed for the server process and other overhead. Use tools like top or htop to monitor RAM usage while the model is loading and running. If you're maxing out your RAM, that could be a problem.
  • Consider Other Processes: Are there other resource-intensive applications running simultaneously? Close them down temporarily to see if it makes a difference. It's like trying to bake a cake while running a marathon – something's gotta give.

If resources are the issue, you might need to:

  • Upgrade your hardware (more RAM or VRAM).
  • Try a smaller model (like the GPT-OSS 20B).
  • Reduce the number of threads used by the model (using the --threads flag in ramalama).

2. Container Configuration Issues

Ramalama uses containers (specifically podman in this case) to run the models. This is great for isolation and reproducibility, but it also means container configuration can be a source of problems. Let's investigate:

  • Podman Errors: The logs show podman inspect commands returning non-zero exit status 125. This suggests podman is having trouble inspecting the container, which could indicate a deeper issue with the container runtime.
  • Permissions: Container permissions can sometimes be tricky. Ensure the user running ramalama has the necessary permissions to interact with podman and access the model files.
  • Mount Issues: The logs show several --mount options. Double-check that the paths specified in these mounts exist and are accessible within the container. A missing or inaccessible file could cause the server to crash.

To troubleshoot container issues:

  • Check Podman Status: Use podman ps -a to see the status of all containers, including the one that crashed. Look for error messages or unusual states.
  • Inspect Container Logs: Use podman logs <container_id> to view the logs from the container. This might give you more specific error messages from the model server itself.
  • Restart Podman: Sometimes, a simple restart of the podman service can resolve transient issues.

3. Llama.cpp Server Issues

Ramalama uses llama.cpp as the runtime for many models, including GPT-OSS 120B. This is a fantastic piece of software, but it can have its quirks. Let's consider some llama.cpp-specific issues:

  • Compatibility: Ensure you're using a compatible version of llama.cpp with the model you're trying to run. Outdated or mismatched versions can lead to crashes.
  • Command-Line Arguments: The logs show a long command passed to llama-server. Double-check these arguments for correctness. Pay special attention to --model, --chat-template-file, --ngl, and --threads.
  • Flash Attention: The --flash-attn on flag enables Flash Attention, a technique that can significantly speed up inference. However, it's not always compatible with all hardware or models. Try disabling it temporarily (--flash-attn off) to see if it resolves the crash.

To troubleshoot llama.cpp:

  • Update Llama.cpp: If you're using a custom build of llama.cpp, make sure it's up-to-date.
  • Simplify Command-Line Arguments: Try running the model with a minimal set of arguments. If it works, gradually add arguments back in to identify the culprit.
  • Check Llama.cpp Logs: As mentioned earlier, container logs can provide valuable clues from the llama-server process.

4. Network Issues

The error message could not connect to: http://127.0.0.1:8080/chat/completions hints at a network connectivity problem. Even though it's a local connection (127.0.0.1), things can still go wrong:

  • Port Conflicts: Is another process using port 8080? Use tools like netstat or ss to check for port conflicts.
  • Firewall: A firewall might be blocking connections to port 8080. Temporarily disable the firewall to see if it resolves the issue (but remember to re-enable it afterward!).
  • Container Networking: Ensure the container is properly networked and can access the host's network. This is usually handled by podman, but it's worth checking.

To troubleshoot network issues:

  • Check Port Availability: Use netstat -tulnp or ss -tulnp to see what's listening on port 8080.
  • Test Local Connectivity: Try using curl or wget from within the container to access the model server (curl http://127.0.0.1:8080). This verifies network connectivity within the container.

5. Hardware Issues (GPU)

While less likely, hardware issues can't be ruled out, especially with demanding workloads like large language models:

  • GPU Drivers: Make sure you have the latest drivers installed for your GPU. Outdated drivers can cause instability.
  • GPU Overheating: Monitor your GPU temperature. Overheating can lead to crashes. Ensure your GPU cooling is adequate.
  • Hardware Fault: In rare cases, there might be a hardware fault with the GPU itself. If you suspect this, try running other GPU-intensive applications to see if they also crash.

To troubleshoot hardware issues:

  • Update GPU Drivers: Check your GPU manufacturer's website for the latest drivers.
  • Monitor GPU Temperature: Use tools like amdgpu_top or nvidia-smi to monitor GPU temperature.
  • Run GPU Stress Tests: Use tools like furmark or memtestG80 to stress-test your GPU and check for stability.

Diving Deeper: Analyzing the Provided Information

Let's circle back to the information provided in the original issue description and see if we can glean any more insights.

The user has a Framework Desktop with an AMD Ryzen AI Max+ 395 CPU and 128 GB of RAM, with 96 GB dedicated to VRAM. This is a powerful system, so resource limitations are less likely, but we still need to verify.

The logs show a 16-second gap between the request and the error, suggesting a potential timeout. This could be due to the model taking a long time to process the request, or it could indicate a hang or deadlock.

The fact that the GPU memory usage drops after the crash and the container disappears strongly suggests that the llama-server process is crashing. We need to find out why.

Given the podman inspect errors, the container configuration or runtime might be a good place to start. We should also investigate the llama-server logs for more specific error messages.

The command-line arguments passed to llama-server look reasonable at first glance, but it's worth double-checking the --chat-template-file path and the --ngl 999 argument (which sets the number of layers to offload to the GPU).

Next Steps: A Systematic Approach

Okay, we've got a bunch of potential causes swirling around. Where do we go from here? The key is to take a systematic approach:

  1. Verify Resources: Double-check VRAM allocation and RAM usage. Rule out resource exhaustion as the primary cause.
  2. Inspect Container Logs: This is crucial. Dig into the podman logs output for the crashed container. Look for any error messages or stack traces from llama-server.
  3. Simplify Llama.cpp Arguments: Try running the model with a minimal set of llama.cpp arguments. This helps isolate potential issues with specific arguments (like --flash-attn or --chat-template-file).
  4. Check Container Configuration: Investigate the podman inspect errors. Ensure the container is properly configured and has the necessary permissions and mounts.
  5. Test Network Connectivity: Verify that the container can connect to the model server on port 8080.
  6. Consider Hardware: If all else fails, explore potential hardware issues (GPU drivers, overheating, etc.).

By systematically working through these steps, you'll be well on your way to diagnosing and resolving the GPT-OSS 120B crash. Remember, debugging is a process of elimination. Don't get discouraged if the first few things you try don't work. Keep digging, and you'll eventually find the solution!

Conclusion: Getting Your LLM Up and Running

Crashing language models are definitely not fun, but hopefully, this guide has given you a solid foundation for troubleshooting issues with the GPT-OSS 120B model. Remember, these large models are complex beasts, and getting them to run smoothly can sometimes be a challenge.

By understanding the potential causes – from resource limitations to container configuration to llama.cpp quirks – and by taking a systematic approach to debugging, you'll be well-equipped to tackle these problems head-on.

So, go forth, troubleshoot, and get those LLMs up and running! And if you stumble, remember this guide and the power of a systematic approach. You got this! 💪