Nvidia: Llama Nemotron Embed 1B v2

Quickstart

Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to nvidia/llama-nemotron-embed-1b-v2.

import os
import requests

base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "nvidia/llama-nemotron-embed-1b-v2"

response = requests.post(
    f"{base_url}/chat/completions",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": model_id,
        "messages": [{"role": "user", "content": "Hello!"}],
        "stream": False,
    },
    timeout=60,
)
response.raise_for_status()
print(response.json())

Use the familiar OpenAI-style chat request format to make model integration easier.

Llama Nemotron Embed 1B v2 API Pricing

Performance

Measured on the Moduck gateway across live traffic, hour by hour over the last 24 hours. Time to first token is how long before anything appears; throughput is how fast the answer is written after that; end-to-end latency is the whole round trip. Figures are medians unless the card says otherwise.

Availability

Uptime is the share of time at least one provider serving this model responded. Availability is stricter: the share of requests actually served. A provider that answers but fails part of the traffic lowers availability without lowering uptime. Both cover the last three days.

Llama Nemotron Embed 1B v2 Compare models

Model specifications

Input

text

Output

text

Context window

8,192

Maximum output

2,048

Release date

2025-10-16

Attachments

No

Reasoning

No

Tool calling

No

Temperature setting

No

Open weights

Yes

Frequently asked questions

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