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Deploy Hermes-4-14B-AWQ-4bit

The fastest way to get this model running locally is via Optional Features.

Make sure to follow the instructions below.

The setup auto-streams the model assets (expect a multi-GB download).

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

📄 Hash Value: a3761510fc54c51a8dbc86d93a5ac2a5 | 📆 Update: 2026-06-26



  • Processor: 4.0 GHz+ boost clock recommended for CPU inference
  • RAM: 32 GB or higher for smooth 32k context lengths
  • Disk: high-speed SSD 120 GB to cache model layers
  • 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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