Ollama llama runner crashes with exit status 2 when loading Qwen2.5 14B models on GPU

What this error means

Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 is a Ollama failure pattern reported for developers trying to fix ollama llama runner exit status 2 crash when loading large models. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue #16102 (closed, 4 comments): Qwen2.5 14B models crash llama runner on GTX TITAN X (12GB VRAM, CUDA 13). Error: ‘llama runner process has terminated: signal arrived during cgo execution’. GPU memory only reaches ~1GB before crash. 7B models work fine on same hardware

Common causes

• Developers using Ollama for local LLM inference experience crashes with specific model sizes on certain GPU hardware, breaking their development and testing workflows
• GitHub Issue #16102 (closed, 4 comments): Qwen2.5 14B models crash llama runner on GTX TITAN X (12GB VRAM, CUDA 13). Error: ‘llama runner process has terminated: signal arrived during cgo execution’. GPU memory only reaches ~1GB before crash. 7B models work fine on same hardware

Quick fixes

1. Confirm the exact error signature matches Error: 500 Internal Server Error: llama runner process has terminated: exit status 2.
2. Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
3. Compare the failing environment with a known working setup, then change one configuration value at a time.

Platform/tool-specific checks

• Verify the command, editor, extension, or API client that produced the error.
• Compare local settings with CI, deployment, or editor-level settings when the error appears in only one environment.
• Avoid deleting credentials, local model data, or project settings until the failing scope is clear.

Step-by-step troubleshooting

1. Capture the exact error message and the command, editor action, or request that triggered it.
2. Check whether the failure is account/auth, quota/rate, model/provider, local runtime, or deployment configuration.
3. Review the source evidence below and compare it with your environment.
4. Apply one change at a time and rerun the smallest failing action.
5. Keep the working fix documented for the team or deployment environment.

How to prevent it

• Keep provider/tool configuration documented.
• Record non-secret diagnostics such as tool version, provider name, model name, and command path.
• Add a lightweight check before CI or production workflows depend on the tool.

Sources checked

• https://github.com/ollama/ollama/issues/16102

Evidence note: GitHub Issue #16102 (closed, 4 comments): Qwen2.5 14B models crash llama runner on GTX TITAN X (12GB VRAM, CUDA 13). Error: ‘llama runner process has terminated: signal arrived during cgo execution’. GPU memory only reaches ~1GB before crash. 7B models work fine on same hardware

Related errors

• Ollama model loading failed GPU
• Ollama llama runner terminated signal
• Ollama CUDA out of memory error

FAQ

What should I check first?

Start with the exact Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 text and the smallest action that reproduces it.

Can I ignore this error?

No. Treat it as a failed Ollama workflow until the root cause is understood.

Is this guaranteed to have one fix?

No. The imported evidence supports the troubleshooting path above, but tool behavior can vary by account, plan, version, provider, and local configuration.

How do I know the fix worked?

Rerun the same command, editor action, or request. The fix is working when that action completes without Error: 500 Internal Server Error: llama runner process has terminated: exit status 2.