How to Install llama-nemotron-embed-1b-v2

Deploying this model locally is quickest when done via Docker.

Follow the step-by-step instructions below.

The setup file includes an intelligent feature that instantly optimizes all configurations for your hardware profile.

💾 File hash: 5ab2e076cedabae47e05c28024db2588 (Update date: 2026-06-27)



  • CPU: 8-core / 16-thread recommended for orchestration
  • RAM: 64 GB to avoid OOM crashes on large contexts
  • Disk Space:70 GB free space for full FP16 weights storage
  • Graphics: 12 GB VRAM minimum required for basic quantization

The **Llama-Nemotron-Embed-1B-v2** is a compact, open‑source embedding model that leverages the proven Llama architecture while focusing on efficient text representation. It delivers *state‑of‑the‑art* performance on semantic similarity tasks despite its modest **1 B** parameter count, making it ideal for edge devices and low‑resource environments. The model supports up to **2048** token context length and produces **768‑dimensional** embeddings, which balance granularity with computational efficiency. Training was performed on a diverse, **web‑scale corpus**, enabling robust understanding of multiple languages and domains without sacrificing inference speed. A quick comparison in the table below highlights how its **parameter efficiency** and **embedding quality** stack up against similar open models.

Parameters1 B
Embedding Dim768
Context Length2048 tokens
Training DataWeb‑scale corpus
Model Size (approx.)2 GB
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