What this error means

Ollama 0.30.0-RC15: GGML_ASSERT(buffer) failed during loading of multimodal model gemma4:26b due to CUDA OOM is a Ollama failure pattern reported for developers trying to fix ggml_assert(buffer) failure and cuda oom when loading large multimodal models in ollama. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Reproduced on 0.30.0-RC15. gemma4:26b loads 31/31 LLM layers but crashes on GGML_ASSERT(buffer) during multimodal component loading. CUDA OOM is the root cause but error message doesn't indicate this clearly.

Common causes

  • Loading large models like gemma4:26b crashes with GGML_ASSERT(buffer) failed. The main LLM layers load successfully but the multimodal component triggers CUDA OOM. Users can't identify which component is consuming VRAM.
  • Reproduced on 0.30.0-RC15. gemma4:26b loads 31/31 LLM layers but crashes on GGML_ASSERT(buffer) during multimodal component loading. CUDA OOM is the root cause but error message doesn't indicate this clearly.

Quick fixes

  1. Confirm the exact error signature matches Ollama 0.30.0-RC15: GGML_ASSERT(buffer) failed during loading of multimodal model gemma4:26b due to CUDA OOM.
  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.