OSS Radar
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… Why it matters: useful plumbing for shipping LLM-powered features.
GitHub description: 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.
Stars
2k
Last 24h
+33est.
Last 7 days
+234est.
Forks
273
Star history (last 1 snapshot)
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[](https://ossradar.net/repo/syv-ai/HyperQwen)<a href="https://ossradar.net/repo/syv-ai/HyperQwen"><img src="https://ossradar.net/badge/syv-ai/HyperQwen" alt="Trending on OSS Radar"></a>About HyperQwen
syv-ai/HyperQwen is a Python project in the LLM tooling category, created 59 days ago. It has 2k GitHub stars and 273 forks, and gained +234 stars over the last 7 days (early estimate), ranking #151 of 167 LLM tooling repos tracked by OSS Radar this week.
See more trending LLM tooling repos or the top AI repos this week.
- Language
- Python
- Category
- LLM tooling
- Created
- Aug 15, 2026
- Last push
- Oct 11, 2026
- consumer-gpu
- cuda
- inference-optimization
- kv-cache
- llm-inference
- local-llm
- quantization
- qwen
- qwen3
- rtx-3090
- speculative-decoding
- vllm
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