How to Run Qwen3.5-122B-A10B Locally via Ollama 2 with Native FP4

How to Run Qwen3.5-122B-A10B Locally via Ollama 2 with Native FP4

Deploying locally takes the least amount of time when executed through native OS tools.

Please follow the instructions listed below to get started.

The process automatically pulls down gigabytes of critical model assets.

The configuration wizard runs silently to set up the model for peak performance.

🧩 Hash sum → 0f61aed3862ce93b66e11ab775f1117c — Update date: 2026-07-02



  • CPU: multi-threading optimized for fast prompt processing
  • RAM: 48 GB needed to prevent memory swapping to disk
  • Disk: 150+ GB for high-context vector database storage
  • GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference

Qwen3.5-122B-A10B is a state‑of‑the‑art language model featuring 122 billion parameters and an A10B architecture. It leverages a massive web‑scale training corpus to achieve exceptional performance across a wide range of NLP tasks. The model incorporates advanced attention mechanisms and multi‑layer decoder stacks that enable deep contextual understanding and fluent generation. Benchmark evaluations place it among the top performers, delivering record‑breaking scores in reasoning, comprehension, and code synthesis. Its efficient A10B design balances computational demands with high‑quality output, making it suitable for both research and production environments. Ongoing fine‑tuning initiatives allow developers to customize the model for specialized domains while preserving its core capabilities.

Parameter Value
Model Name Qwen3.5-122B-A10B
Parameters 122 B
Architecture A10B
Training Data Web‑scale corpus
Key Features Advanced attention, multi‑layer decoder
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