Host any GGUF model in one command

Hacker News

Running a GGUF model locally usually means writing custom inference code or wrestling with llama.cpp's CLI flags every time you want to test something. Existing OpenAI-compatible servers often require Docker, complex configuration files, or GPU support. The gap between "I have a .gguf file" and "I have a working API endpoint" is wider than it should be. A simple CLI tool to serve GGUF models as an endpoint: gguf-serve To cut this short, we asked Neo to build gguf-serve. Point it at any .gguf file, run the server, and immediately get OpenAI-compatible endpoints that work with any client library or tool that speaks the OpenAI API format.

Visit product View on Hacker NewsMar 31, 2026

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3points

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Launch Intel predictions

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

96%96% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

72%72% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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