Deploying this model locally is quickest when done via a simple curl command.
Use the instructions provided below to complete the setup.
No manual effort needed; the setup auto-ingests the large data.
To save you time, the system will automatically determine efficient resource allocation.
🔐 Hash sum: 15f5a1ea0458d11a9b025a11a3bcd541 | 📅 Last update: 2026-06-26
- CPU: modern architecture (Zen 3 / Alder Lake minimum)
- RAM: 32 GB highly recommended for 26B+ GGUF models
- Disk Space:70 GB free space for full FP16 weights storage
- GPU: modern architecture (Ada Lovelace / Ampere minimum)
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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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https://sitasudtrasporti.it/category/automation/
