Ollama / Ollama

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

Fix Ollama llama runner exit status 2 crash when loading large models Includes evidence for Ollama troubleshooting demand.

Source-backed Last updated May 12, 2026 1 source Needs local verification

Category: Ollama
Error signature: Error: 500 Internal Server Error: llama runner process has terminated: exit status 2
Quick fix: Compare the failing environment with a known working setup, then change one configuration value at a time.
Updated: May 12, 2026
Verification status: Source-backed
Evidence: 1 public source URL

Before you change production

This page includes public source URLs in the imported troubleshooting record. Compare those references with your version and environment before applying changes.

Reproduce the smallest failing action and save non-secret logs before changing configuration.
Check versions for Ollama, related SDKs, package managers, CI runners, and hosting providers.
Change one setting or dependency at a time, then rerun the same failing command or request.
Avoid destructive commands, credential rotation, billing changes, or security relaxations without a rollback plan.

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

Confirm the exact error signature matches Error: 500 Internal Server Error: llama runner process has terminated: exit status 2.
Check the Ollama account, local tool state, and provider configuration involved in the failing workflow.
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

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

Diagnostic flow for this page

Match Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 exactly before applying the quick fix.
Compare the failing environment with Ollama versions, account scope, provider settings, and deployment context.
Check the listed common causes in order, starting with the cause that best matches your logs.
Use the evidence status below to decide whether to confirm against public sources or official documentation.
Apply one reversible change, rerun the smallest failing action, and keep rollback notes.

FAQ

What does Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 mean in Ollama?

Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 indicates the workflow described by "Ollama llama runner crashes with exit status 2 when loading Qwen2.5 14B models on GPU" failed in a Ollama context. Start by confirming the exact signature and the component that emitted it.

What should I check first for Error: 500 Internal Server Error: llama runner process has terminated: exit status 2?

Check whether this cause matches your environment: Developers using Ollama for local LLM inference experience crashes with specific model sizes on certain GPU hardware, breaking their development and testing workflows

Is the Error: 500 Internal Server Error: llama runner process has terminated: exit status 2 guidance source-backed?

The "Ollama llama runner crashes with exit status 2 when loading Qwen2.5 14B models on GPU" page is labeled source-backed. It has 1 public source URL from github.com, so use that evidence status when deciding how much additional verification you need.

When should I avoid the quick fix for Error: 500 Internal Server Error: llama runner process has terminated: exit status 2?

For "Ollama llama runner crashes with exit status 2 when loading Qwen2.5 14B models on GPU", avoid applying "Compare the failing environment with a known working setup, then change one configuration value at a time." when the error text differs, the affected version or provider is different, or the change would touch credentials, billing, DNS, production deploy settings, or irreversible data.

What should I collect before debugging Error: 500 Internal Server Error: llama runner process has terminated: exit status 2?

For "Ollama llama runner crashes with exit status 2 when loading Qwen2.5 14B models on GPU", collect the failing Ollama command or request, non-secret configuration, versions, timestamps, request IDs when available, and the smallest reproduction that still triggers the signature.