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.

What it solves

HyperQwen provides a high-performance serving environment for large Qwen models on consumer-grade GPUs. It addresses the challenge of running large-scale language models efficiently on hardware like the RTX 3090, offering significantly faster token generation speeds and support for very long context windows (up to 240k tokens) compared to standard serving setups.

How it works

It functions as a specialized wrapper and patch series for the vLLM inference engine. The project includes a model-preparation pipeline that performs requantization, calibrates draft vocabularies, and implements speculative decoding techniques. Users can choose between different modes—such as single-user chat or high-concurrency batch processing—via environment variables, allowing the system to optimize memory usage and throughput based on the specific workload.

Who it’s for

This project is for developers, researchers, and AI enthusiasts who want to run large Qwen models locally on consumer hardware without sacrificing performance. It is particularly useful for those building API backends, RAG pipelines, or coding assistants who need high tokens-per-second throughput or extended context capabilities.

Highlights

  • Optimized for consumer GPUs (e.g., RTX 3090) with specific performance tuning for different hardware architectures.
  • Supports high-throughput batch serving (up to 1,035 tok/s aggregate) and low-latency single-user modes.
  • Enables extended context windows (up to 240k tokens) using specialized KV cache techniques.
  • Provides an OpenAI-compatible API, making it easy to integrate with existing LLM tools and agents.
  • Includes a robust benchmarking harness to help users validate performance on their specific hardware.

Related

  • Dispatch
    Introducing Qwen: A Comprehensive LLM and LMM Framework

    Qwen is a project towards AGI consisting of a series of multilingual large language models (LLMs) and large multimodal models (LMMs), including open-source versions ranging from 1.8B to 72B parameters.

  • Dispatch
    Qwen2.5-1M Release: Open‑Source 7B and 14B Models with 1M‑Token Context and vLLM‑Based Inference Framework

    Qwen releases open‑source Qwen2.5‑7B‑Instruct‑1M and Qwen2.5‑14B‑Instruct‑1M models that support up to 1 million token contexts, accompanied by an optimized vLLM‑based inference framework that delivers 3×–7× faster prefill and retains short‑task performance comparable to GPT‑4o‑mini.

  • Dispatch
    Qwen 3.8 27B Release: High-Performance Local LLM with Reasoning Trade-offs

    Qwen 3.8 27B is an Apache 2 licensed, vision-capable 27B parameter model that delivers frontier-level reasoning and coding capabilities on consumer hardware, though its default 'xhigh' reasoning setting often leads to excessive overthinking.

  • Dispatch
    Qwen2.5-Turbo 1M Token Context Length Release

    Qwen has released Qwen2.5-Turbo, which extends the model's context window to 1 million tokens while significantly improving inference speed and maintaining competitive performance on short-sequence tasks.

  • Project
    kvmem/kvmem

    KVMem is an inference management tool that enables long-context LLM execution by tiering KV cache data across GPU VRAM, system RAM, and SSDs, allowing models to handle massive conversation histories on consumer hardware.