Slack Desktop App Consumes Excessive RAM and CPU on Laptops
Slack's Electron-based desktop application is notorious for high memory and CPU consumption, degrading performance on laptops especially when multiple workspaces are open. This forces users to close Slack to reclaim system resources or accept reduced responsiveness across other applications. The issue has persisted for years and is a known architectural constraint of the Electron framework.
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Similar Problems
surfaced semanticallySlack High Memory Usage and Unreliable Notifications Reduce Reliability
Slack consumes excessive system memory and notification delivery is inconsistent, causing users to miss messages or experience sluggish performance. The Electron-based desktop client is a known resource bottleneck that Slack has not fully resolved. Teams dependent on Slack for real-time communication cannot tolerate missed notifications from a tool positioned as their primary channel.
Slack Desktop App Consumes Excessive Memory and Feels Sluggish
Users report the Slack desktop client uses a large amount of system memory and performs sluggishly during normal use. This is a common complaint about Electron-based messaging clients affecting daily productivity.
Slack Desktop Client Too Resource-Intensive on macOS
The Slack desktop app on macOS consumes excessive CPU and memory, causing system slowdowns during normal use. The Electron-based architecture is the root cause — a structural constraint not easily patched. Enterprise users running Slack alongside other heavy tools feel the impact most acutely.
Slack consumes excessive RAM and slows laptops during huddles
Users report Slack uses a large amount of RAM and noticeably slows their laptop, especially while in a huddle call. This is a widely-experienced performance problem given Slack's broad daily-active-user base.
Slack Desktop Client Lags When Switching Between Conversations
The Slack PC client exhibits noticeable lag when users switch between channels or DMs during high-activity periods, breaking the fast-scanning flow that knowledge workers rely on. The performance degradation is particularly disruptive during busy project phases when quick context-switching is critical. This points to rendering or state management inefficiency in the desktop client.
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