Ollama GGML_ASSERT(buffer) Failed — CUDA Out of Memory When Loading Multimodal Models like gemma4:26b

What this error means

GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM is a Ollama failure pattern reported for developers trying to fix ollama ggml_assert(buffer) failed crash when loading large multimodal models with cuda out of memory. Based on the imported evidence, treat this as a tool-specific troubleshooting page rather than a generic API error.

Why this happens

Open issue on ollama/ollama. Error occurs during multimodal model loading when CUDA runs out of memory for the vision component. 31/31 LLM layers offload successfully but CLIP/mmproj initialization fails. Clear error signature with specific model name.

Common causes

• Loading large multimodal models (e.g., gemma4:26b) crashes with GGML_ASSERT(buffer) failed during vision component initialization. The main LLM layers offload successfully but the CLIP/mmproj component fails with cudaMalloc OOM. Users see cryptic assertion failure instead of clear OOM message.
• Open issue on ollama/ollama. Error occurs during multimodal model loading when CUDA runs out of memory for the vision component. 31/31 LLM layers offload successfully but CLIP/mmproj initialization fails. Clear error signature with specific model name.

Quick fixes

1. Confirm the exact error signature matches GGML_ASSERT(buffer) failed during loading of multimodal model 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.

Sources checked

• https://github.com/ollama/ollama/issues/16147

Evidence note: Open issue on ollama/ollama. Error occurs during multimodal model loading when CUDA runs out of memory for the vision component. 31/31 LLM layers offload successfully but CLIP/mmproj initialization fails. Clear error signature with specific model name.

Related errors

• Ollama exit status 2 crash loading gemma4 models
• Ollama CUDA OOM errors with gemma3

FAQ

What should I check first?

Start with the exact GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM 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 GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM.