Warp – Run the 313B GLM-5.3-Flash on a MacBook with 8GB RAM

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A few months ago, I created the WARP engine (formerly WASTE) to run Kimi K3, the complete 2.78-trillion-parameter model, on macOS. GLM-5.3-Flash shares many architectural similarities with Kimi K3, so I added support for it as well. It requires as little as 5.14 GB of RAM to run, and on a 64 GB MacBook Pro M5 Pro it reaches about 3.32 tok/s, or 3.86 tok/s on longer runs. More memory means a larger expert cache, while higher storage and memory bandwidth can further improve performance. The project is completely open-source and free to use: https://github.com/sqliteai/warp

Visit product View on Hacker NewsAug 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos, model · Missing: agents, agent, cursor

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: created, para · Missing: supports, reddit linkedin, podcasting

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TrustMRRLess likely to generate early MRR · Strong signals: month, para · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

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BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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