Nemotron-3-Embed-1B-BF16: Self-Hosting & Deployment Guide
Nemotron-3-Embed-1B-BF16 is an open-weight embedding model that turns text into vectors for search and retrieval.
What is Nemotron-3-Embed-1B-BF16?
Nemotron-3-Embed-1B-BF16 is an open-weight embedding model that turns text into vectors for search and retrieval, published by NVIDIA with open weights at nvidia/Nemotron-3-Embed-1B-BF16. LLM API does not route this model today, so there is no endpoint or code sample for it here. What this page does give you is the self-hosting picture: published weight sizes, the VRAM each quantisation needs and the commands to serve it yourself. Every figure is read from the public repository and refreshed weekly. At import it had 563,593 downloads on Hugging Face in the previous 30 days, under the other licence.
How to run Nemotron-3-Embed-1B-BF16 yourself
Open weights (other), 1.1B parameters. Below: how much memory the weights need at each quantization, which hardware fits them, and the commands to serve it.
Nemotron-3-Embed-1B-BF16 VRAM requirements
| Precision / quantization | Weights | Figure | Source |
|---|---|---|---|
| BF16 (full precision) | 2.3 GB | Estimate | 1.1B params × 2 bytes |
| MLX 4-bit | 0.64 GB | File size | mlx-community/Nemotron-3-Embed-1B-BF16-4bit |
File size = actual download size on Hugging Face. Estimate = parameter count × bytes per weight. Weights only — the KV cache for long contexts needs extra memory; the fit tables below add 15% headroom as a rule of thumb.
GPU requirements: deploy Nemotron-3-Embed-1B-BF16 on your hardware
| Hardware | BF16 (full precision) | MLX 4-bit |
|---|---|---|
| NVIDIA H200141 GB | 1× | 1× |
| NVIDIA H10080 GB | 1× | 1× |
| NVIDIA A100 80GB80 GB | 1× | 1× |
| NVIDIA L40S48 GB | 1× | 1× |
GPUs needed per variant.
| Hardware | MLX 4-bit |
|---|---|
| Mac, 512 GBM3 Ultra | Fits |
| Mac, 192 GBM2 Ultra | Fits |
| Mac, 128 GBM4 Max / M3 Max | Fits |
| Mac, 64 GBM4 Pro / Max | Fits |
Assumes ~75% of unified memory is usable by the GPU.
| Hardware | BF16 (full precision) | MLX 4-bit |
|---|---|---|
| AWS p5.48xlarge8× H100 · 640 GB | Fits | Fits |
| AWS p4de.24xlarge8× A100 80GB · 640 GB | Fits | Fits |
| AWS g6e.12xlarge4× L40S · 192 GB | Fits | Fits |
| Google Cloud a3-highgpu-8g8× H100 · 640 GB | Fits | Fits |
| Azure ND H100 v58× H100 · 640 GB | Fits | Fits |
Whole-instance GPU memory; public instance specs.
Not on LLM.API yet
We do not route this model through our API at the moment. Browse the models you can call today — most workloads have a close match already live.
Not available on LLM API yet
We do not route this model through our API at the moment, so there is no endpoint or code snippet for it yet. Browse the models you can call today — most workloads have a close match already live.
Browse available modelsWhen to Use — When NOT to Use
Use it if...
- You need vectors for semantic search, clustering or RAG retrieval (Nemotron-3-Embed-1B-BF16)
- You want to keep documents inside your own network while indexing them
- The weights are published openly, so you can pin one checkpoint and keep vectors reproducible
- You are comparing self-hosting cost against a managed embedding endpoint
Avoid if...
- LLM API does not route this model today, so there is no endpoint here to call
- You want zero operations — serving, scaling and upgrades are yours to run
- You need text generation — this model returns vectors or scores, not answers
- You already have an index built with a different model: vectors are not interchangeable
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