If your FFmpeg YouTube stream stops, first establish whether Linux or a service/container memory limit killed FFmpeg. A stopped process alone does not prove an out-of-memory (OOM) event: input, network, FFmpeg, or YouTube broadcast problems can produce a similar symptom.
Preserve the logs and failure time before restarting repeatedly. Then compare kernel and service evidence with memory measurements at the boundary that ran FFmpeg; tune the pipeline only when those records point to a particular cause.
Confirm that the FFmpeg YouTube stream stopped
Start by defining what “stopped” means in your setup. Did the FFmpeg process exit, remain alive but stop sending data, or continue running while YouTube showed the broadcast as ended or unhealthy? These are different failures, and a restart can erase the distinction if you do not record what you observed first.
Note the time and time zone, the process state, the service or container name, and what YouTube Studio showed. Save FFmpeg’s stderr output and the relevant service logs before rotating or clearing them. If a systemd service manages the job, its journal can show whether the process exited, was signalled, or was restarted. If a container runs it, retain the container’s exit information and any available OOM status as well.
Check whether the input was still available at that moment. A file reaching its end, a playlist failing to advance, a mounted volume becoming unavailable, or a wrapper script exiting can all stop output without the kernel killing FFmpeg. If the stream uses a playlist or media source, compare its expected playback behaviour with the checks in how to loop a guided meditation playlist on YouTube Live. That is a separate question from whether the Linux process was killed.
Make a brief incident record rather than relying on memory: the last time the stream was known to work, the observed failure time, the last FFmpeg error, the service result, and the broadcast state. If the times differ, retain them separately. A delay between process exit and the moment you notice the stream can otherwise lead you to inspect the wrong part of the logs.
Check kernel and service logs for an OOM kill
On a systemd host, inspect the service journal and kernel journal around the recorded time. For example, replace the placeholders with your actual unit and time:
journalctl -u YOUR_SERVICE --since "2026-10-05 02:00:00"
journalctl -k --since "2026-10-05 02:00:00"
Use the real incident time, not the example time above. Look for kernel OOM-killer messages identifying ffmpeg or a related process, service exit status or result, and any relevant messages from the container runtime. A service failure by itself is not proof of an OOM kill. The key is whether the records identify a memory-pressure event or a process being killed for that reason.
Containerised workloads need evidence from both the host and the container’s memory boundary. A host kernel log may identify the killed task, while container status or cgroup counters can show that the container reached its own limit. A service can hit a configured cap even when the host still has memory available, so a host-wide view alone may miss the applicable constraint.
For cgroup v2, inspect the memory accounting files for the cgroup that actually contains FFmpeg. The kernel documents memory.events counters including high, max, oom, and oom_kill. Where available, memory.events.local helps distinguish events in that cgroup itself from hierarchical counts that include descendants. Compare counter values before and after a failure if you have them; a single accumulated value without a baseline can be difficult to interpret.
The kernel’s cgroup v2 memory controller documentation describes the meaning of these controls and counters. memory.high is a threshold above which processes are throttled and reclaim pressure is applied; reaching it does not by itself invoke the OOM killer. memory.max is a hard limit, and if usage reaches it without being reduced, the cgroup OOM killer can be invoked. The exact files and paths depend on the host’s cgroup layout and kernel.
If your host uses cgroup v1, do not copy v2 file names or assumptions into it. The hierarchy and controls differ; consult the kernel’s cgroup v1 memory controller documentation and identify the files present on that system. If no kernel, service, or cgroup evidence supports an OOM kill, move on to FFmpeg, input, network, and broadcast checks rather than labelling the incident “out of memory”.
Inspect memory limits and usage at the failure time
Once an OOM event is supported by evidence, identify which boundary was reached. For cgroup v2, inspect memory.current for current usage, memory.peak for the recorded peak where available, memory.high and memory.max for the configured thresholds, and memory.events for event counts. These values answer different questions: a peak near a hard cap, for example, is more informative when paired with a changed OOM counter than when read in isolation.
Find the cgroup containing the actual FFmpeg process, not merely the parent service or host. Systemd services and containers typically run within cgroups, and nested groups can make a parent’s accounting differ from the process’s own boundary. If you are unsure which group applies, ask the host or container administrator to identify it before changing a limit. On cgroup v1, locate the corresponding memory controller and use its documented controls rather than assuming the v2 layout.
Memory use needs to be measured across the workload, including the minutes before a failure. A snapshot taken after FFmpeg has exited cannot establish its peak. Monitor the process’s resident set size (RSS) and the relevant cgroup usage; retain samples with timestamps so you can compare them with logs. Note other jobs sharing the same cgroup or host, because concurrent transcodes or services can consume capacity that was available when the stream was first tested.
Keep a record of the command, input, filters, encoder, resolution and frame rate, and concurrent work during the observation period. This context matters because FFmpeg can read, filter, transcode, and output media in very different ways. A plain file relay and a multi-filter transcode should not be assumed to have the same memory profile. If you are checking the pipeline rather than the memory limit, this guide to troubleshooting a YouTube Live stream provides a broader set of stream-health checks.
The FFmpeg documentation offers -benchmark and -benchmark_all for performance reporting, but says maximum memory consumption is unsupported on some systems and may appear as zero. Treat the operating system’s process and cgroup measurements as primary evidence. A zero from FFmpeg’s benchmark output is not proof that memory use was negligible. See the FFmpeg command-line documentation for the reporting options and their limitations.
Rule out FFmpeg errors, exhausted input, and network failure
If the logs do not show an OOM kill, read FFmpeg’s stderr from the end backwards and identify the first meaningful error, not just the final shutdown line. An output failure may follow an earlier input or filter problem. Check the exit status and any wrapper logs too: a script can terminate FFmpeg, fail to start its next playlist item, or exit after handling an error.
Confirm the input was still readable and had media left to send. For a single file, check whether it reached its natural end and whether the command was expected to loop. For a playlist or network input, inspect whether entries remained reachable and whether the process logged a read or demuxing error. A loop can still fail if the next item is missing or inaccessible; do not treat a repeated start as evidence that the source path is sound.
Then look for output-side symptoms such as RTMP connection failures, timeouts, write errors, or a connection reset. Check network connectivity and routing at the relevant time, along with any firewall, proxy, or credential change. A weak or interrupted connection can stop delivery while FFmpeg remains alive, and a reconnect message does not establish that memory was exhausted.
Keep the FFmpeg command intact while collecting evidence. Changing the encoder, resolution, filters, input, and restart policy together may make the stream work, but it will not show which change mattered. If you suspect an input or media-source configuration, compare it against the intended playback behaviour; for example, media source playback speed settings for a YouTube loop stream concerns a different class of playback issue from an OOM kill.
Check YouTube broadcast status and stream health
A healthy local process is not proof that YouTube is receiving a healthy broadcast. Check YouTube Studio’s live control room around the failure time for broadcast state, stream health, and any messages about the incoming connection. Confirm that the intended broadcast was still active and that the stream key in use belongs to it. Do not publish or paste a stream key into logs, tickets, or public messages while investigating.
Separate the broadcast’s lifecycle from FFmpeg’s lifecycle. The broadcast may have ended or become unavailable independently of the process; conversely, FFmpeg can exit while the broadcast remains visible in Studio. Compare timestamps and status messages from both sides before deciding which component failed first. YouTube’s official guide to streaming on YouTube explains the setup and the checks available in the live control room; follow the current guidance for your account and workflow.
If Studio reports a connection or stream-health issue, return to the output logs and network path rather than increasing memory limits without evidence. If Studio shows that the broadcast ended, verify its state and configuration before restarting FFmpeg into it. Restarting a local process cannot make an ended or invalid broadcast valid. Keep notes on the order of events, since the first failure often points towards the component that needs attention.
Tune only against measured evidence
A fix should match the boundary or process that the evidence identifies. If the service or container’s memory.max is below the measured workload peak, and the host has capacity after accounting for other services, consider revising the allocation in the service or container configuration. Check what else shares that capacity before raising a cap: a larger allowance for one process can leave less room for its neighbours.
If the whole host is under pressure, reduce competing jobs or services, or provision capacity based on observed demand. There is no universal RAM amount for a 24/7 FFmpeg YouTube stream. Memory needs depend on the input and processing path, concurrent workload, and applicable limits; the title of the incident cannot supply those missing facts.
If usage rises over time, compare samples from the beginning and later stages of the same workload. Investigate the command, wrapper, input and filter path, process supervision, and FFmpeg version against that pattern. The available evidence does not establish a universal FFmpeg memory leak or a specific defect for YouTube streaming, so do not prescribe a version change as a diagnosis on its own.
Simplify only the part of the pipeline that measurement implicates. Removing an unnecessary filter, avoiding a transcode that is not needed, or reducing concurrency are experiments to evaluate against the same observations. Reducing resolution may be appropriate for some workflows, but it is not a general memory remedy without evidence about what the current pipeline consumes. Hardware acceleration is also capability-dependent: some paths copy frames between GPU and system memory, so acceleration does not universally lower system RAM use. FFmpeg’s documentation on hardware acceleration describes the available options and their constraints.
Do not disable OOM handling as a routine way to keep FFmpeg alive. A hard shortage remains after the kill mechanism is changed, and the resulting pressure can affect other services or the host. Prefer reducing evidenced demand, resolving competing usage, or making a measured allocation change. Make one change at a time where practical, then observe the same process, cgroup, and stream-health signals to see whether the specific failure condition changed.
Use restart behaviour without masking the cause
Manage a long-running FFmpeg process with a service manager or another supervisor so that startup, logs, and exit behaviour are explicit. A bounded restart policy can restore a process after a transient failure, but repeated exits should trigger an alert and investigation. If the kernel repeatedly kills FFmpeg at a memory boundary, an automatic restart simply runs into the same pressure again.
For background invocations, use -nostdin or redirect standard input from /dev/null where appropriate. The FFmpeg FAQ on background tasks explains that FFmpeg normally checks console input and that terminal behaviour can suspend a background task; -nostdin prevents those input checks. This addresses an interactive-input issue, not OOM, network failure, exhausted input, or a YouTube broadcast problem.
Make restarts observable. Preserve each run’s stderr, exit status, and start and stop times, and alert when exits repeat rather than allowing an unnoticed loop. If a restart begins the same command against the same unavailable input or ended broadcast, it will not correct that condition. Verify independently that the input is available and the YouTube broadcast is in the expected state after the process restarts.
If maintaining a local Linux process through the night is itself the recurring operational burden, compare that burden with the rest of your workflow before changing how you run the stream. A hosted approach can remove the need to keep your own computer on, but it does not replace choosing a valid video and broadcast setup. For a local machine versus a hosted workflow, the 24/7 streaming cost comparison can help frame what to compare. StreamNeo removes the need to keep FFmpeg running on your Linux machine by turning an uploaded video into a YouTube live stream that continues while your computer is off.
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FAQ
How do I check if Linux OOM killer killed FFmpeg?
Match the failure time to kernel logs and look for an OOM-killer message naming FFmpeg or a related process. Then check the service or container result and the relevant cgroup’s OOM counters, such as oom and oom_kill on cgroup v2. A process exit alone does not prove an OOM kill.
Why can FFmpeg be killed when the server still has free memory?
A service or container can reach its own cgroup limit while the host has memory available outside that boundary. Check the cgroup containing FFmpeg, including its configured hard limit and event counters, as well as host-wide pressure. The applicable controls depend on whether the host uses cgroup v1 or v2.
Should I lower resolution or change encoder settings when the stream stops?
Only if measurements or logs point to the processing path as a cause. First confirm an OOM event and locate the constrained boundary, or rule out input, network, FFmpeg, and broadcast failures. Changing several settings at once can conceal which condition mattered.
Does restarting FFmpeg fix an out-of-memory failure?
A restart can bring the process back, but it does not remove the pressure or raise an undersized limit. Use bounded restarts and retain logs so repeated exits remain visible; then address the measured cause. Also verify separately that the input and YouTube broadcast are still valid.