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An offline YouTube stream does not, by itself, mean OBS ran out of memory. First establish what happened at the failure timestamp: did OBS crash or log an allocation error, did its stream output disconnect while OBS stayed open, or did YouTube report a separate encoder or playback problem? Compare the OBS log and Stats with system RAM and GPU memory monitoring and YouTube Live Control Room before changing settings or hardware.
What “out of memory” can mean
Memory is not a single resource. System RAM is used by OBS and other applications; GPU video memory (VRAM) supports graphics and some encoding work. CPU load, rendering lag, encoding lag, and an unstable connection can also interrupt a stream without proving that either RAM or VRAM was exhausted.
Record what viewers saw and what OBS and YouTube showed at the same time. Note the exact error wording and timestamp, whether OBS remained open, whether the output stopped or reconnected, whether YouTube said it was not receiving data or showed an encoder error, and whether the stream was offline for everyone or only some viewers reported playback trouble. Treat “OBS runs out of memory while streaming” as a diagnosis to verify, not a description of every interruption.
Collect evidence before changing the setup
Save OBS logs and crash information
Keep the OBS log for the affected session and any crash log. Record your operating system, OBS version, GPU model and driver, encoder choice, scene and source setup, active plugins, output resolution and frame rate, and whether recording or Replay Buffer was active. Note whether the incident followed an output restart or reconnect.
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Watch OBS Stats during a controlled observation
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 separate memory growth from rendering or encoding pressure; none should be interpreted in isolation.
Monitor system RAM and GPU memory in parallel
Use your operating system or GPU vendor’s diagnostic tools to capture system RAM and GPU memory/VRAM around the event. Look for the pattern: steady growth, a spike when a scene or source appears, or memory that increases after each output stop and restart. Preserve the GPU encoder error and its exact wording if one appears.
Compare with YouTube and a local recording
Check YouTube Live Control Room for encoder errors and viewer reports. YouTube’s guidance for poor stream quality includes checking encoder CPU load and a local archive; if the encoder picture and sound look healthy, it points to testing the outbound internet connection. Compare the local archive with what viewers received: a clean archive alongside a failed live output can help focus attention on the connection or ingest path rather than the source video.
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Change one variable at a time and preserve before-and-after logs. A short controlled reproduction can help, but avoid repeated reconnect loops during a live event if each restart appears to increase GPU memory use.
Use the evidence to choose a diagnostic branch
| Observed evidence | Likely area to investigate | Next check |
|---|---|---|
| OBS exits or explicitly reports an allocation failure; system RAM is exhausted or OBS process memory climbs. | Host memory pressure, a source or plugin growth issue, or a software defect. | Correlate per-process and system RAM with scenes, browser sources, plugins, and log timestamps. Compare with a clean profile only as a controlled test. There is no universal RAM threshold or source-level fix established for all setups. |
CUDA_ERROR_OUT_OF_MEMORY appears from CUDA/NVENC, and GPU memory rises after output stops, starts, or reconnects. |
GPU VRAM or encoder allocation path. | Capture GPU memory and encoder logs around each restart, record software and hardware versions, and check the status of the specific OBS issue discussed below before treating it as your cause. |
| OBS stays open, output disconnects, dropped frames rise, and no allocation failure appears. | Connection to the ingest server or configured bitrate exceeding stable upload capacity. | Follow the OBS connection checks: compare bitrate with stable upload, try another server or service where possible, review VPN, security and network-optimization software and network drivers, and prefer wired networking. |
| CPU load is high and rendering or encoding lag rises; stream quality is poor. | Rendering or encoder capacity. | Compare OBS Stats with YouTube 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 affect more than encoding. | Preserve logs and GPU telemetry. Do not assume lowering stream bitrate alone will restore VRAM; the reported failure pattern is specific to one issue report. |
When dropped frames point to the network
OBS’s connection guide frames dropped frames and intermittent disconnections as a network issue between the computer and the remote stream ingest server. It says that too many dropped frames may disconnect the stream. That guidance applies to dropped-frame and intermittent-disconnection symptoms; it does not turn an explicit allocation failure into a network problem. See the OBS stream connection troubleshooting guide (dated 2024-09-30).
If OBS remains open and its output disconnects without a memory allocation error, check whether the configured bitrate exceeds what the upload connection can sustain consistently, not just its best speed. The OBS guide suggests lowering bitrate to match stable upload, trying a different server or service where possible, checking VPN/security/network-optimization software and network drivers, and preferring wired networking over Wi-Fi. Dynamic bitrate is a congestion mitigation, not a root-cause fix, and it reduces video quality.
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OBS sends to the selected streaming service rather than operating its own streaming servers. The diagnostic implication is to examine the connection from your computer to that service’s ingest path—not to look for an OBS server setting as a universal cure.
How much weight to give memory-leak reports
OBS Studio GitHub issue #13656 is a recent user/developer report describing NVIDIA NVENC surface allocations that were not freed after output stop/start, including reconnects, eventually leading to CUDA_ERROR_OUT_OF_MEMORY. The reporter describes approximately 200 MiB accumulating per reconnect and a test setup with 4 GiB total VRAM. Those are measurements from that issue report, not typical usage, a general OBS requirement, or evidence that every user will reproduce it. The issue text does not establish that an official fix has shipped. Check the report’s current status before concluding that it explains your failure: OBS Studio issue #13656.
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A separate issue opened on 2023-12-07 described a leak in OBS 30.0.0-rc1 on Fedora 37 under a particular Stream Delay/RTMP disconnect reproduction. It illustrates why a leak report needs to be matched to its version and conditions; it is not evidence that the current stable OBS release has the same problem: OBS Studio issue #10240.
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Fix the cause you have evidence for
If system RAM is the signal
Use the logs and monitoring to identify whether OBS process memory grows with a particular scene, browser source, plugin, or other workload. Test a clean profile as a comparison and change one factor at a time. The available evidence does not support a universal RAM threshold or a guaranteed source-level fix, so do not replace memory solely because YouTube went offline.
If GPU memory and NVENC errors are the signal
Document the GPU, driver, OBS version, encoder, memory trend, and restart sequence. Compare the pattern with the specific issue report rather than assuming it applies. Avoid provoking repeated reconnects during a live event if memory appears to accumulate with each one.
If dropped frames and disconnects are the signal
Investigate stable upload capacity and the route to the selected ingest server. Test network changes individually—such as wired networking or temporarily checking VPN/security/network-optimization software—and retain OBS Stats and logs so you can tell whether dropped frames or disconnects improve. Lowering bitrate may help a connection that cannot sustain the configured rate, but it does not diagnose or repair a confirmed memory allocation failure.
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If rendering or encoding lag is the signal
Use OBS Stats, YouTube’s encoder status, and the local archive together. Test a less demanding workload or a different encoder setting one at a time, then compare the same counters and output quality. High CPU or render/encoding lag is a capacity clue, not proof of memory exhaustion.
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