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

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.

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.

• 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.

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 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.

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 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.