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An offline YouTube stream does not, by itself, prove OBS ran out of memory. First check whether OBS reported an allocation or encoder error, crashed, or stayed open while its stream output disconnected. Match OBS logs and Stats with RAM/VRAM monitoring and YouTube Live Control Room at the same timestamp; the pattern points toward the right cause.
First determine what actually failed
Write down the exact time and wording of the incident, then compare what viewers saw with what OBS and YouTube showed. “OBS runs out of memory while streaming” can describe several different failures, and YouTube going offline does not distinguish among them.
- OBS reports an allocation or CUDA/NVENC error, or the process exits: investigate memory pressure, an encoder failure, or a software issue. Save any crash log as well as the session log.
- OBS stays open but its stream output stops or reconnects: check the OBS log and dropped-frame counters. A network interruption can take the stream offline without exhausting memory.
- YouTube reports an encoder error or says it is not receiving data: compare that message and timestamp with OBS’s output state and local recording.
- Only some viewers report playback trouble: distinguish viewer playback problems from a stream that is offline for everyone.
Do not call an interruption a memory leak unless the logs or monitoring support that explanation.
Collect evidence before changing settings
Save the OBS session and system details
Save the OBS log for the affected session and any crash log before changing settings. Record the operating system, OBS version, GPU model and driver, encoder, scene and source setup, active plugins, output resolution and frame rate, and whether recording or Replay Buffer was also active. Note whether the failure followed an output stop, reconnect, or scene/source change.
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Watch OBS Stats and memory use together
In applicable OBS versions, open View → Stats. Record Memory Usage, CPU usage, average render time, frames missed due to rendering lag, and frames skipped due to encoding lag. These counters help distinguish resource pressure from a disconnected output; memory use alone does not identify which resource or component is responsible.
At the same time, monitor system RAM and GPU memory (VRAM) using the operating system or the GPU vendor’s diagnostic tools. Note whether memory rises steadily, spikes when a particular scene or source appears, or accumulates after stream output stops and reconnects. Capture the exact encoder error, if one appears. Correlation in time matters more than a single reading taken after the stream has failed.
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Compare OBS with YouTube and the local archive
Check YouTube Live Control Room for encoder errors and viewer reports. YouTube’s troubleshooting guidance recommends checking encoder CPU load and a local archive when stream quality is poor; if the encoder’s picture and sound look healthy, it advises testing the outbound internet connection (YouTube live-stream troubleshooting). A local recording can help establish whether the encoded output itself was degraded, but it does not prove that the connection to YouTube stayed healthy.
Use the evidence to choose a diagnostic branch
| Observed pattern | More likely direction | What to check next |
|---|---|---|
| OBS exits or logs an allocation failure, while system RAM is exhausted or OBS process memory climbs | Host RAM pressure, a source or plugin growth issue, or a software defect | Correlate process and system RAM with scene/source changes, plugins, and log entries. Compare with a clean OBS profile only as a controlled test; there is no universal RAM threshold or established source-level fix. |
CUDA/NVENC reports CUDA_ERROR_OUT_OF_MEMORY; GPU memory rises after output stops, starts, or reconnects |
GPU VRAM or encoder allocation path | Capture GPU memory and encoder logs around each restart. Compare software and hardware versions, then check whether the reported issue applies to your setup. |
| OBS remains open, output disconnects, dropped frames rise, and no allocation failure appears | Connection instability to the ingest server or bitrate beyond what the upload connection can sustain | Follow the connection checks below; do not treat dropped frames alone as proof of memory exhaustion. |
| CPU load is high and rendering or encoding lag rises | Rendering or encoder capacity problem | Compare OBS Stats with YouTube’s encoder errors and the local archive. Test a lower workload or encoder setting one change at a time. |
| Display or output initialization fails only after GPU memory is consumed | GPU resource pressure may be affecting more than stream encoding | Preserve the logs and GPU telemetry. Lowering stream bitrate alone is not established as a way to restore VRAM. |
If the evidence points to a connection drop
OBS’s connection guide defines dropped frames as a connection that is unstable or cannot sustain the configured bitrate; excessive dropped frames can disconnect the stream. The guide’s framing is specifically about dropped-frame and intermittent-disconnection symptoms, not an explicit allocation failure (OBS stream connection troubleshooting).
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- Compare the configured bitrate with the connection’s stable upload capacity, not just a brief peak. Lower the bitrate if the connection cannot sustain it.
- Try another server or streaming service where possible, and check whether a VPN, security software, network optimizer, or network driver is affecting the connection.
- Prefer wired networking for a streaming setup when possible; Wi-Fi adds another possible source of instability.
- OBS dynamic bitrate can mitigate congestion by reducing bitrate, but it is not a root-cause fix and can reduce video quality.
OBS sends to the selected streaming service’s ingest path; a connection problem is between the computer and that path, not a reason to look for an OBS-operated streaming server.
Interpret memory-leak reports narrowly
A report filed in OBS Studio’s GitHub issue tracker in 2026 describes NVENC surface allocations not being freed after output stop/start, including reconnects, eventually producing CUDA_ERROR_OUT_OF_MEMORY (OBS Studio issue #13656). The reporter described an increase of about 200 MiB per reconnect and a test setup with 4 GiB of VRAM. Those are measurements from that report, not general OBS requirements or population-wide rates. The issue does not establish that every user can reproduce the problem or that an official fix has shipped.
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An earlier issue opened on 2023-12-07 described a leak under a specific reproduction involving OBS 30.0.0-rc1 on Fedora 37, Stream Delay, and an RTMP disconnect (OBS Studio issue #10274). It is not evidence that a current stable release has the same bug. Reports like these are useful leads only when your error, restart pattern, and telemetry match.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Test one change at a time
- Keep the original session log, crash log, and time-correlated RAM/VRAM and Stats observations.
- Choose the branch best supported by the evidence: host RAM, GPU VRAM/encoder, rendering or encoding load, or network connection.
- Change one relevant variable, such as a scene/source, plugin, encoder setting, or network condition, and record the result with a fresh log.
- Use a short controlled reproduction if needed. Avoid repeated reconnects during a live event if each restart appears to increase GPU memory use.
- Compare the before-and-after behavior rather than assuming that a stream staying online once proves the cause is fixed.
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If your goal is to keep uploaded videos playing as a 24/7 YouTube stream, StreamNeo is a cloud option rather than a fix for the OBS failure you are diagnosing. Upload a recording or build a playlist, add your YouTube stream key, and go live. Nothing has to stay on at home; it streams your upload as made, up to 4K 60fps at one price per slot, and automatically recovers if YouTube drops the stream. The first day is free with no card. Monthly pricing is $9.99 per month. It plays uploaded videos and streams to YouTube only. Learn more at StreamNeo, or start the free first day.
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