Ollama / Ollama

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

Fix Ollama GGML_ASSERT(buffer) failed crash when loading large multimodal models with CUDA out of memory Includes evidence for Ollama troubleshooting demand.

Source-backed Last updated May 14, 2026 1 source Needs local verification

Category: Ollama
Error signature: GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM
Quick fix: Compare the failing environment with a known working setup, then change one configuration value at a time.
Updated: May 14, 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

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

Confirm the exact error signature matches GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM.
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 GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM 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 GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM mean in Ollama?

GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM indicates the workflow described by "Ollama GGML_ASSERT(buffer) Failed — CUDA Out of Memory When Loading Multimodal Models like gemma4:26b" failed in a Ollama context. Start by confirming the exact signature and the component that emitted it.

What should I check first for GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM?

Check whether this cause matches your environment: 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.

Is the GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM guidance source-backed?

The "Ollama GGML_ASSERT(buffer) Failed — CUDA Out of Memory When Loading Multimodal Models like gemma4:26b" 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 GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM?

For "Ollama GGML_ASSERT(buffer) Failed — CUDA Out of Memory When Loading Multimodal Models like gemma4:26b", 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 GGML_ASSERT(buffer) failed during loading of multimodal model due to CUDA OOM?

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