Ollama OOM crash during GGUF metadata parsing in v0.24.0 readString/n_kv allocation

What this error means

runtime: out of memory — readString/n_kv allocation in fs/gguf.readString tries to allocate 32GB+ RAM on corrupted model header is a Ollama failure pattern reported for developers trying to fix ollama v0.24.0 crash on startup due to oom during gguf metadata parsing with corrupted model headers. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Ollama v0.24.0 crashes immediately on startup with runtime: out of memory when parsing models with wonky/corrupted headers. New parser allocates massive RAM (32GB+) on bad n_kv field. Workaround: rollback to v0.21.0. Category: Ollama.

Common causes

• Ollama v0.24.0 crashes immediately on startup with runtime: out of memory when parsing models with wonky/corrupted headers. New parser allocates massive RAM (32GB+) on bad n_kv field. Workaround: rollback to v0.21.0. Category: Ollama.

Quick fixes

1. Confirm the exact error signature matches runtime: out of memory — readString/n_kv allocation in fs/gguf.readString tries to allocate 32GB+ RAM on corrupted model header.
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/16175

Evidence note: Ollama v0.24.0 crashes immediately on startup with runtime: out of memory when parsing models with wonky/corrupted headers. New parser allocates massive RAM (32GB+) on bad n_kv field. Workaround: rollback to v0.21.0. Category: Ollama.

Related errors

• Ollama

FAQ

What should I check first?

Start with the exact runtime: out of memory — readString/n_kv allocation in fs/gguf.readString tries to allocate 32GB+ RAM on corrupted model header 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 runtime: out of memory — readString/n_kv allocation in fs/gguf.readString tries to allocate 32GB+ RAM on corrupted model header.