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This answer covers “[AI-005] Local inference runs out of memory while loading context”. Read it with the summary and confirm that the symptom and service match what you are experiencing. Available options can vary by plan and account setup.
- Before changing anything, note what you expected, what happened and roughly when. Check whether the issue affects one page or the whole service.
- Save the full error message and note recent changes. Hide passwords, access codes, personal data and keys before sharing screenshots or logs.
- If a step requires administrator access, restarting services or editing server files, stop if you do not administer that server. Send support the service name, error and checks you have already made.
L3 technical diagnosis
User symptoms
The process exits while generating a long response or leaves the server out of memory.
Observed code or message
`OOMKilled` / `out of memory allocating context`Likely root cause
Memory use includes model weights, context KV cache, batches, workers and runtime. A large context or concurrent requests can exceed RAM/VRAM even if the model fits at load time.
Likely-cause and resolution matrix
Follow a branch only when evidence supports it. A symptom may have more than one cause; do not change layers by guesswork.
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Prerequisites
- Authorised access to the involved runtime, provider or automation.
- A test environment and synthetic data; never use customer prompts for diagnostics.
- A technical owner who can approve network, secret, GPU or resource changes.
Important warnings
- Do not expose local inference endpoints or webhooks without appropriate authentication, TLS and limits. Never put API keys, private prompts or personal data in commands, screenshots or logs.
- Commands are diagnostic references. Production changes, Docker/firewall rules and credential rotation require service-owner authorisation.
Step-by-step walkthrough
1. Confirm the symptom and scope
free -h
docker inspect -f 'oom={{.State.OOMKilled}} limit={{.HostConfig.Memory}}' ai-runtime
cat /sys/fs/cgroup/memory.events 2>/dev/null`OOMKilled` / `out of memory allocating context`
2. Correct the responsible component
3. Verify and close exposure
The check confirms the expected result without exposing services or data.
Troubleshooting & Solutions
`OOMKilled` / `out of memory allocating context`
Root cause: Memory use includes model weights, context KV cache, batches, workers and runtime. A large context or concurrent requests can exceed RAM/VRAM even if the model fits at load time.
Resolution: Reduce context and initial concurrency; evaluate a quantised or smaller model that meets quality needs. The operator must reserve memory for the OS and other containers before changing limits; change one variable at a time.
Rollback and recovery plan
Restore the last approved configuration, stop the affected workflow and revoke any exposed credential. If real data was processed through an unauthorised path, retain minimal redacted records and follow the internal incident-response process.
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