What this error means

Potential OOM crash during GGUF metadata parsing in v0.24.0 (readString/n_kv allocation) is a Ollama failure pattern reported for developers trying to fix ollama crash caused by unbounded memory allocation when parsing large gguf model files — particularly affects loading quantized models on systems with limited ram. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

GitHub Issue ollama/ollama#16175 identifies unbounded allocation during GGUF metadata parsing leading to OOM crashes. Separately, issue #16147 confirms GGML_ASSERT(buffer) failures during multimodal model loading due to CUDA OOM. Both affect users running local LLMs who encounter silent crashes when loading larger models.

Common causes

  • GitHub Issue ollama/ollama#16175 identifies unbounded allocation during GGUF metadata parsing leading to OOM crashes. Separately, issue #16147 confirms GGML_ASSERT(buffer) failures during multimodal model loading due to CUDA OOM. Both affect users running local LLMs who encounter silent crashes when loading larger models.

Quick fixes

  1. Confirm the exact error signature matches Potential OOM crash during GGUF metadata parsing in v0.24.0 (readString/n_kv allocation).
  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.