# syv-ai/HyperQwen

<https://repo.masters-masters.com/en/repos/syv-ai/HyperQwen>

[Home](https://repo.masters-masters.com/en) › [syv-ai](https://repo.masters-masters.com/en/owners/syv-ai) › [HyperQwen](https://repo.masters-masters.com/en/repos/syv-ai/HyperQwen)

syv-ai/HyperQwen has 2,532 stars on GitHub. Its RepoMasters card: RC tier. Serve large Qwen models fast on the GPUs you actually own. Qwen3.8-27B on a single…

| Field | Value |
| --- | --- |
| Owner | [syv-ai](https://repo.masters-masters.com/en/owners/syv-ai) |
| Stars | 2,532 |
| Forks | 322 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | Apache-2.0 |
| Created | 2026 |
| Topics | kv-cache, llm-inference, local-llm, quantization, qwen, qwen3, rtx-3090, speculative-decoding, vllm, consumer-gpu |
| Tier | [RC (2,000 to 4,999 ★)](https://repo.masters-masters.com/en/ranks/blockbuster) |
| View on GitHub | [syv-ai/HyperQwen](https://github.com/syv-ai/HyperQwen) |

## Repository

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.

The tier for repos with 2,000 to 4,999 ★. Published odds per card pulled: 3%. Rarity: Epic.

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