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

Just follow the guidelines provided below.

The setup auto-downloads all needed files (several GBs).

The smart installation system will instantly find the perfect configuration.

🗂 Hash: 7117f273e41706d6e26ada2090fc5e5a • Last Updated: 2026-06-28 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants Graphics: TensorRT-LLM / vLLM inference engine compatible chip

Hermes-4-14B-AWQ-4bit is a **large language model** featuring **14 billion parameters** and optimized for both research and commercial deployment. Built on the latest transformer architecture, it leverages **AWQ (Activation-aware Weight Quantization)** to achieve a compact **4-bit** representation without sacrificing performance. The reduced memory footprint enables faster **inference speed** on consumer‑grade hardware while maintaining high **accuracy** on benchmarks. A dedicated fine‑tuning pipeline allows developers to adapt the model for specialized tasks such as code generation, dialogue, and summarization. Below is a quick overview of its core specifications:

Parameter Count	14 B
Quantization	4‑bit AWQ

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