NVIDIA Nemotron 3 Nano with 30B parameters
Nemotron 3 Nano 30B delivers enterprise-grade performance optimized for complex reasoning and extended context processing. With a 128,000-token context window, this model excels at analyzing lengthy documentation and maintaining coherence across multi-turn conversations. Developers can integrate it immediately via our standard chat completion endpoint, requiring no additional configuration beyond your existing API key. The FP16 quantization ensures high precision during inference, making it suitable for technical tasks requiring accurate code generation or data extraction. While operating on a 3x credit multiplier, the starter tier access allows teams to prototype efficiently before scaling production workloads.
Designed for production environments, this proprietary model offers robust bilingual proficiency, handling both English and Arabic queries with high fidelity. This makes it an ideal choice for diverse applications requiring nuanced language understanding without sacrificing technical accuracy. Unlike open-weight alternatives, Nemotron 3 Nano provides consistent output stability backed by commercial licensing terms that protect intellectual property. Whether building customer support agents or research pipelines, the model balances cost and capability effectively. Teams can deploy confident solutions knowing the architecture supports rigorous enterprise standards while remaining accessible through our unified platform interface.
from openai import OpenAI
client = OpenAI(
base_url="https://llmapi.resayil.io/v1/",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="nemotron-3-nano:30b",
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(response.choices[0].message.content)import anthropic
client = anthropic.Anthropic(
base_url="https://llmapi.resayil.io/v1",
api_key="YOUR_API_KEY"
)
message = client.messages.create(
model="nemotron-3-nano:30b",
max_tokens=1024,
messages=[
{"role": "user", "content": "Hello!"}
]
)
print(message.content[0].text)const response = await fetch(
"https://llmapi.resayil.io/v1/chat/completions",
{
method: "POST",
headers: {
"Content-Type": "application/json",
"Authorization": "Bearer YOUR_API_KEY"
},
body: JSON.stringify({
model: "nemotron-3-nano:30b",
messages: [
{ role: "user", content: "Hello!" }
]
})
}
);
const data = await response.json();
console.log(data.choices[0].message.content);curl https://llmapi.resayil.io/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "nemotron-3-nano:30b",
"messages": [
{"role": "user", "content": "Hello!"}
]
}'