syv-ai/HyperQwen
Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single 24 GB card with vLLM: 127 tok/s single-user (381 when the answer quotes the prompt), ~1,035 tok/s at 64 concurrent, 150k-262k context. vLLM patches, requant pipeline, benchmarks.
About the project
A ready-made setup for serving Qwen3.8-27B on a single 24 GB consumer RTX 3090 with vLLM: 150k context, OpenAI-compatible API, single-user and batch modes, plus patches, requant scripts and benchmarks.
Useful for
- Run a local OpenAI-compatible Qwen3.8-27B server on a single RTX 3090
- Pick batch mode for an API backend with dozens of concurrent requests
- Enable DFlash2 and prefix cache to speed up answers that quote the prompt
README summarized by DeepSeek V4.1 Flash. Details may be inaccurate.
Why it’s trending
- 32 stars so far today, about 38 expected by the end of the day.
- The repository is 34 days old and already has 1,497 stars.
- About 9 forks a day — people are taking the code.
- Recent forks include notable developers: @webclinic017 (348 followers).
Stars per day
Bars are daily stars, the line is the usual pace. Red marks spike days.
Numbers
- Total stars
- 1,497
- Stars in a day
- 38
- Forks
- 215
- Issues and pull requests
- 151
- Watchers
- 19
- Language
- Python
- License
- Apache-2.0
- Created
- August 15, 2026
- Last push
- September 18, 2026
Star trust
Growth looks organic: forks and discussion are in line with active projects, and stars arrive unevenly, the way people give them.
These are heuristics, not a verdict: we judge by the repository’s behavior, not by a list of stargazers.
Spotted in
- September 13, 2026Top new repositories this month: #48
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