Rising memory use during a long OBS stream is a symptom to investigate, not proof of a memory leak. First identify whether system RAM or GPU VRAM is increasing, then note when it starts and whether it tracks idle time, sources, scene changes, output restarts or reconnects.
A useful test changes one condition at a time and repeats the session that produced the problem. That helps distinguish persistent memory growth from rendering or encoding strain and network trouble, which can feel similar during an overnight broadcast but need different investigations.
Identify whether RAM or VRAM is growing
Start by naming the resource, rather than writing down only “OBS memory”. System RAM and GPU VRAM are separate pools. Your operating system’s process monitor can show OBS’s system-memory use; GPU monitoring tools can show VRAM use, though the exact display depends on your operating system and graphics hardware. Record both if you can, along with total system memory pressure and the time.
A rise in system RAM may relate to OBS, a plugin, a browser source, another application, or ordinary caching. A VRAM increase may involve graphics resources used by a scene, an output configuration, a driver, or another application using the GPU. Those are candidate areas to test, not diagnoses. A single reading, or a graph that trends upward once, cannot tell you which explanation is right.
Keep the OBS process and the whole computer in view. If OBS’s system-memory use stays steady while total system memory changes, another process may be responsible. If overall GPU memory rises, check whether other GPU-using applications are open. Write down what is actually increasing rather than treating every busy resource as an OBS leak.
Make a small log with a baseline at launch and timestamped readings during the session. Alongside the values, note the OBS version, operating system, graphics hardware and driver, encoder, active plugins, scene collection, current scene, and whether stream output is active. This does not need to be a formal report; a note on your phone or a simple spreadsheet is enough, provided you can compare like with like.
Also record the visible symptom separately. Is memory rising without an obvious change in output? Are frames late or skipped? Does the stream disconnect? A viewer may describe all three as “OBS is getting stuck”, but each observation points to a different test. For a broad view of the moving parts in a persistent broadcast, what cloud playout means is useful context; it does not replace identifying the resource on the computer you are testing.
Record when the increase begins
The most useful question is not just how high a reading gets, but what was happening when its slope changed. Note the time OBS opened, stream start, scene changes, browser-source activity, output stops and starts, reconnects, and any time the stream was left running without intervention. If an overnight increase appears only after several hours, a short desk test may not reproduce it.
Try to reproduce the same conditions that preceded the increase. If the original session ran a particular scene overnight and experienced a reconnect, a test that streams a different scene for a few minutes is not comparable. Keep the same scene collection, output settings and source activity for the baseline run, then alter only one category in the next run. There is no universal test duration: run long enough to include the trigger you observed.
An increase that starts soon after one browser overlay is displayed suggests a test: hide that source and compare the trend. A change that appears after output stop/start cycles suggests testing those cycles separately. Neither pattern proves a cause. The important evidence is whether the pattern recurs when you repeat the same condition and changes when you remove it.
Write down the shape of the trend in plain language: flat, gradual rise, jump after an event, or rise followed by a fall. Mark whether memory drops when a source is hidden or output stops. A saved screenshot can help, but attach the time and condition; a graph without context is difficult to interpret later. Keep your observations separate from guesses, for example, “VRAM rose after the second reconnect” rather than “the encoder leaked memory”.
Check behaviour while OBS is idle
Compare an idle period with an active-output period. Open the same scene collection and leave the relevant scene visible, but stop stream output for a controlled observation. Then run output under otherwise similar conditions. Note RAM and VRAM at the start and through each period, and record whether OBS is previewing the scene, whether sources are animating, and what other applications are running.
“Idle” needs a clear definition. OBS may still render a preview or update an animated browser source when it is not streaming. If you want to test a quieter state, document whether the preview is disabled and whether the source remains active. Do not compare a busy animated scene with a completely different empty scene and attribute every difference to stream output.
If memory continues to climb with output stopped, that weakens the idea that active encoding alone explains the behaviour, but it does not identify the responsible component. If it rises only during output, repeat the comparison before drawing a conclusion. A single run can be affected by background processes, source timing or conditions you have not controlled.
If you find that managing a computer continuously is itself the operational problem, keep that separate from this diagnosis. A file-based broadcast can be run without leaving your computer switched on using StreamNeo, which removes the need to keep a local OBS session open for that workflow; it does not establish what caused memory growth in your OBS setup.
Isolate sources and scene changes
Scene complexity is a sensible place to investigate, but test it methodically. OBS’s encoding performance troubleshooting guide discusses how GPU workload and complex scenes can constrain rendering and encoding. It recommends considering costly sources, browser sources, filters and large scene collections. That is guidance about workload, not evidence that a particular source has a memory leak.
Begin with reversible changes. If a scene contains a browser overlay, hide it for one test. In another run, disable a filter or an unused source category. Keep the other sources and output conditions unchanged. If an increase stops under a simpler scene, restore the original, then repeat the change to see whether the relationship holds. Avoid changing several items at once: you may reduce strain without learning which change mattered.
Browser sources deserve a controlled test because a page can include animation or continue updating when you are not looking at it. The OBS performance guide suggests reducing browser-source workload, including dimensions, and using a static image or media source where that suits the content. For a devotional channel, for example, test a fixed title card in place of a moving web overlay while keeping the audio and output settings the same. If you use a browser source for a live clock or announcements, record what it does when hidden; do not assume hiding it stops every kind of activity.
Filters and transitions also belong in the test plan, but do not treat them as suspects simply because they are present. A filter may add rendering work; a transition can change workload at a scene switch. Compare a run with the relevant filter or transition enabled and another with it disabled, while noting any rendering lag separately from memory. If scenes switch on a schedule, include that schedule in both runs.
Plugins and scene collections are further variables. Note which plugins are active and whether the problem persists in a minimal test collection containing only the sources needed to reproduce the stream. Do not remove or update everything before making a baseline record. If a plugin appears relevant, check its current documentation and report the repeatable steps to its maintainer rather than assuming OBS itself is responsible.
A 24/7 channel may have a lot of scenes even if it uses only a few at a time. Keep a copy of the collection before removing unused scenes, and test a focused collection against the original. If you are building a continuous music workflow from scratch, the OBS lo-fi radio setup guide can help you think through the sources and scene structure before comparing performance.
Compare output restarts and reconnects
A stream that reconnects is not necessarily suffering a memory leak. A network interruption can cause dropped frames or a disconnect while memory remains stable. OBS’s stream connection troubleshooting guide treats dropped frames and intermittent disconnections as connection or ingest stability problems. Record those symptoms independently from RAM and VRAM.
If your log shows a pattern of memory increase after you stop and restart output, reproduce that pattern deliberately and compare it with a session of similar duration without output restarts. Use the same scene, output configuration and sources. Note the memory reading before and after each event, and whether the change is in system RAM or VRAM. If your stream reconnects automatically, record the reconnect time and what the resource does afterwards.
There is a narrow OBS Studio issue report, issue #13656, describing NVENC VRAM surface-pool retention after output stop/start cycles. It is a report about a particular condition, not a general diagnosis of NVENC, OBS or long YouTube streams. Check the live issue for its current status and version context before using it to guide a test. Similar timing in your own log justifies a comparison; it does not prove that you have the same defect.
Do not create unnecessary disconnects on a live channel merely to test a theory. Use a planned test stream or a time when an interruption is acceptable, and preserve the conditions that matter. If output restart cycles are part of the normal workflow, record them in a controlled run. If the same memory pattern cannot be reproduced, mark the connection as uncertain rather than treating it as the explanation.
Distinguish memory growth from rendering strain
Memory and performance are related but not interchangeable. A scene can make rendering work difficult without producing a steadily increasing memory trend. Conversely, memory can rise while frames continue to render normally. Keep the OBS Stats dock or log information available and record rendering lag and encoding lag separately from memory readings.
The OBS performance guide explains that GPU load and scene complexity can constrain rendering and encoding. Browser sources, filters and expensive sources may add workload. If you see rendering lag, test a simpler scene or reduce one expensive source, then compare the lag and memory trends independently. If encoding lag appears, note the encoder and output conditions; do not label it a leak merely because it occurs late in a long session.
Dropped frames need their own column in your notes. Distinguish a rendering or encoding symptom from dropped frames caused by connection instability. The OBS connection guide provides a separate path for investigating the latter. If memory is flat while dropped frames rise, that is evidence to investigate the connection rather than evidence of a memory leak. If memory rises as well, you may have two issues, or a shared trigger; test them separately.
A viewer may report buffering while your local preview looks smooth, or you may see a choppy preview while the broadcast remains connected. Capture what you observe on the local system and what the stream reports. Do not infer the cause from the viewer-facing symptom alone. This separation matters when the channel is a scheduled news loop or a devotional stream with bilingual titles: keeping the broadcast content simple can be sensible, but it does not replace measuring the specific failure.
Review findings and choose the next test
After each run, summarise the evidence in a compact comparison. “Growth followed the browser overlay in two comparable runs and did not appear when it was disabled” is stronger than “the browser source is bad”, but still describes an association rather than a universal cause. “Memory rose after reconnects once” is a lead to test again, not a conclusion. Use the same resource, event and observation interval when comparing runs.
| What you observe | What to compare next | What the observation does not prove |
|---|---|---|
| System RAM rises while output is stopped | OBS versus other processes; same scene with sources active and quiet | That OBS itself is the source of the growth |
| GPU VRAM rises after output restarts | A comparable run without restarts; record encoder and driver | That every NVENC or OBS setup has the reported issue |
| Rendering or encoding lag appears, memory is steady | Simplify one source, filter or scene category; compare Stats readings | That the slowdown is a memory leak |
| Dropped frames or disconnections appear, memory is steady | Follow the connection troubleshooting path and note event times | That local memory caused network trouble |
| Growth remains in a minimal scene | Repeat with plugins and other applications documented; preserve logs | That one specific component is responsible |
If one controlled change makes no reproducible difference, restore it before trying another. That avoids accumulating changes that make the setup harder to understand. If a minimal scene still shows a repeatable rise, keep the OBS logs and your event notes, then report the steps to OBS support or the relevant plugin or graphics-driver maintainer. Include versions and whether the affected resource is RAM or VRAM; that context is more useful than the label “memory leak” on its own.
Do not buy more memory, replace a GPU or install a cleaning utility as a generic fix for a rising graph. Those choices may be relevant only after you establish a particular hardware constraint, and they can obscure the original test. If the problem interrupts a live schedule, consider a documented fallback plan for the channel while keeping the diagnosis separate from the continuity decision.
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FAQ
Does rising OBS memory prove there is a leak?
No. It tells you that a resource reading changed, not why it changed. Identify RAM versus VRAM, record the timing and repeat the conditions before calling it a leak.
Why does VRAM increase after I reconnect?
A reconnect may be worth testing, particularly if the increase follows output stop/start cycles, but timing alone is not proof. OBS issue #13656 describes a specific NVENC VRAM-retention report; compare your own repeat runs and check the issue’s current status and version context.
Should I disable browser sources first?
They are a reasonable isolation variable because OBS identifies browser sources as potential workload contributors. Disable one category at a time, compare the same long-session trigger, and note rendering lag separately from memory.
Are dropped frames a memory-leak symptom?
Not by themselves. Dropped frames or intermittent disconnections can point to connection or ingest stability, while rendering and encoding lag have their own indicators. Record network symptoms separately and follow OBS’s connection guidance when those are the main problem.