Zero-Click Run jina-embeddings-v5-text-nano Locally via Ollama 2

Zero-Click Run jina-embeddings-v5-text-nano Locally via Ollama 2

To get this model running locally in no time, utilize the built-in WSL tools.

Use the instructions provided below to complete the setup.

No manual effort needed; the setup auto-ingests the large data.

The setup file includes a feature that instantly optimizes all configurations.

🔗 SHA sum: 9c0a0f0efc5581d40dc389afe1638836 | Updated: 2026-06-29



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: required: 16 GB absolute minimum for small models
  • Storage: extra room for future model updates and datasets
  • GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The jina-embeddings-v5-text-nano model delivers compact yet high‑quality text embeddings optimized for edge devices. With only 2 million parameters, it achieves competitive performance on semantic similarity tasks while maintaining a small memory footprint. Its inference latency is under 5 ms on typical CPUs, making it ideal for real‑time applications that require fast processing. The model supports multiple languages and preserves contextual nuances better than earlier nano‑sized alternatives. Key metrics are summarized in the following table:

Parameters 2 million
Size (MB) 7.8
Latency (ms) <5
Throughput (tokens/s) 2000
Supported Languages 30
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