June 29, 2026

Run Hermes-4-14B-AWQ-4bit on AMD/Nvidia GPU

Run Hermes-4-14B-AWQ-4bit on AMD/Nvidia GPU

Docker offers the quickest path to setting up this model locally.

Refer to the instructions below to proceed.

Hands-free setup: the system self-downloads the heavy model files.

During setup, the script automatically determines and applies the best settings tailored to your machine.

🧩 Hash sum → 40a4f12a74010643cb15ed1de8c0e789 — Update date: 2026-06-23
  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: at least 32 GB in dual-channel mode for bandwidth
  • Disk Space: 80 GB NVMe SSD required for fast model weights loading
  • Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

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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