# Shard: One-click Local Execution of Qwen3.5 Inference Model with Automatic Hardware Adaptation

> Shard is a zero-configuration local large model launcher that supports the Qwen3.5-Claude-4.6-Opus-Reasoning-Distilled model family. It can automatically detect GPU, VRAM, and CPU configurations, generate optimal running parameters through benchmark tests, allowing users to run inference models locally without manual adjustments.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-06T02:41:33.000Z
- 最近活动: 2026-06-06T02:48:11.291Z
- 热度: 152.9
- 关键词: Shard, Qwen3.5, 本地大模型, llama.cpp, GPU 自动调优, 量化模型, OpenAI API, Windows, 推理模型
- 页面链接: https://www.zingnex.cn/en/forum/thread/shard-qwen3-5
- Canonical: https://www.zingnex.cn/forum/thread/shard-qwen3-5
- Markdown 来源: floors_fallback

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## Shard: A Zero-Configuration Solution for Local Execution of Qwen3.5 Inference Models

Shard is a zero-configuration local large model launcher designed for the Windows platform, supporting the Qwen3.5-Claude-4.6-Opus-Reasoning-Distilled model family. It can automatically detect hardware configurations (GPU, VRAM, CPU, etc.), generate optimal running parameters through benchmark tests, enable one-click installation and usage, and provide an OpenAI-compatible API, significantly lowering the technical barrier for local large model deployment, allowing users to run inference models efficiently without manual adjustments.

## Pain Points of Local Large Model Execution

In recent years, open-source large language models have developed rapidly. Developers want to run them locally for privacy protection and low latency, but face many challenges: manual configuration of inference engines like llama.cpp, understanding complex quantization parameters (e.g., Q4_K_M), adjusting GPU layer offloading values (-ngl), and balancing context length and memory usage. Users unfamiliar with underlying technologies are deterred, and even experienced developers need a lot of time to test and find optimal configurations.

## Detailed Explanation of Shard's Core Features

- **Automatic Hardware Detection**: Scan system hardware (OS version, CPU, memory, GPU, VRAM, CUDA version) via the `detect` command to provide basic data for optimization.
- **Intelligent Benchmarking and Configuration Generation**: The `recalc` command runs benchmark tests, dynamically searches for the optimal combination of GPU layer offloading values and context length, and generates 8 preset configurations covering 4K-256K contexts.
- **Intelligent Quantization Recommendation**: Recommend appropriate quantization levels based on hardware capacity to avoid downloading incompatible models.
- **Eight Preset Configurations**: Cover daily chat to extreme modes, supporting hot update switching.
- **OpenAI-Compatible API**: Provide a standard interface on the local port 8080, compatible with all OpenAI clients.

## Shard Installation and Usage Process

**Installation**: Run the PowerShell script to automatically complete CUDA-matched llama.cpp download, model selection, global command configuration, and environment variable setup.
**Typical Usage Flow**:
1. `shard detect` to view hardware detection results
2. `shard recalc` to run benchmark tests and generate optimized configurations
3. `shard` to start the service
**Management Commands**: `shard ls` to check status, `shard 3` to switch configuration, `shard model 9B` to switch model, `shard stop` to stop the service.

## Shard's Technical Highlights and Implementation Details

Shard's implementation focuses on user experience: abstracting complex configurations into simple commands while retaining flexibility; using dynamic search strategies in benchmark tests to reduce time; adopting a configuration file model isolation design; supporting hot switching mechanisms. In addition, the `shard opencode` command automatically generates OpenCode configurations and updates parameters with switching, providing a seamless experience.

## Shard's Application Scenarios and Notes

**Target Users**: Developers who don't want to dive into underlying configurations, users who frequently switch models/contexts, Windows users seeking out-of-the-box experience; NVIDIA GPU users can fully utilize performance, and CPU users are also supported with degradation.
**Notes**: Currently mainly for the Windows platform, with best support for NVIDIA GPUs; other hardware platforms may require additional configuration adjustments.

## Shard's Value and Summary

Shard represents the development direction of local large model deployment tools: minimizing the usage threshold while maintaining flexibility. Through automatic detection, intelligent tuning, and preset configurations, it allows users to focus on model usage rather than parameter tuning. For users who want to run Qwen3.5 inference models locally, Shard is a solution worth trying.