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About the Provider

NVIDIA is a global leader in AI computing and accelerated hardware, known for its GPUs and enterprise AI platforms. Through its NeMo and research initiatives, NVIDIA develops models like Nemotron Orchestrator to enable advanced reasoning, tool orchestration, and scalable AI workflows for developers and enterprises.

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Model Quickstart

This section helps you quickly get started with the nvidia/Orchestrator-8B model on the Qubrid AI inferencing platform. To use this model, you need:
  • A valid Qubrid API key
  • Access to the Qubrid inference API
  • Basic knowledge of making API requests in your preferred language
Once authenticated with your API key, you can send inference requests to the nvidia/Orchestrator-8B model and receive responses based on your input prompts. Below are example placeholders showing how the model can be accessed using different programming environments.
You can choose the one that best fits your workflow. 
import requests
import json
from pprint import pprint

url = "https://platform.qubrid.com/api/v1/qubridai/chat/completions"

headers = {
  "Authorization": "Bearer <QUBRID_API_KEY>",
  "Content-Type": "application/json"
}
data = {
  "model": "nvidia/Orchestrator-8B",
  "messages": [
      {
          "role": "user",
          "content": "Explain quantum computing to a 5 year old."
      }
  ],
  "temperature": 0.7,
  "max_tokens": 4096,   
  "stream": True,
  "top_p": 0.8
}
response = requests.post(
  url,
  headers=headers,
  json=data, 
)
content_type = response.headers.get("Content-Type", "")
if "application/json" in content_type:
  pprint(response.json())
else:
  for line in response.iter_lines(decode_unicode=True):
      if not line:
          continue
      if line.startswith("data:"):
          payload = line.replace("data:", "").strip()
          if payload == "[DONE]":
              break
          try:
              chunk = json.loads(payload)
              pprint(chunk)
          except json.JSONDecodeError:
              print("Raw chunk:", payload)

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Model Overview

Nemotron Orchestrator 8B is a state-of-the-art 8B parameter orchestration model designed to solve complex, multi-turn agentic tasks. It works by coordinating a diverse set of expert models and tools rather than acting as a single monolithic model. On the Humanity’s Last Exam (HLE) benchmark, Orchestrator-8B achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being approximately 2.5× more efficient.

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Model at a Glance

FeatureDetails
Model IDnvidia/Orchestrator-8B
ProviderNVIDIA
ArchitectureOptimized Transformer (TensorRT-LLM enhanced)
Context Length16384 Tokens
Model Size7B params
Parameters4
Training DataOrchestration datasets, workflow sequences, tool-use datasets, enterprise task simulations
Base ModelQwen3-8B

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When to use?

You should consider using Nemotron Orchestrator 8B if::
  • You are working on complex, multi-turn agentic tasks
  • You need a model that can coordinate multiple tools and expert models
  • You want higher accuracy at lower computational cost
  • You are conducting research or development focused on orchestration and reasoning
  • You plan to fine-tune the model for specific tasks
  • You need a model that can generalize to unseen tools and pricing setups

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Inference Parameters

Parameter NameTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output.
Temperaturenumber0.4Controls creativity and randomness; lower values are recommended for deterministic tasks.
Max Tokensnumber4096Maximum number of tokens the model can generate.
Top Pnumber1Controls nucleus sampling for more predictable output.

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Key Features

  • Intelligent Orchestration Capable of managing heterogeneous toolsets, including basic tools such as search and code execution, as well as other LLMs (both specialized and generalist).
  • Efficiency Delivers higher accuracy at significantly lower computational cost compared to monolithic frontier models
  • Robust Generalization Demonstrates the ability to generalize to unseen tools and pricing configurations.

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Benchmark Performance

  • Achieves 37.1% on the Humanity’s Last Exam (HLE) benchmark
  • Outperforms:GPT-5 , Claude Opus 4.1 , Qwen3-235B-A22B

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Limitations

  • Scalability The model has not been tested at larger sizes (greater than 8B parameters), and it is unclear whether performance and efficiency advantages would persist at that scale.
  • Coverage: The model has not been evaluated across broader domains such as code generation or web interaction, so its generalization beyond the studied reasoning tasks remains unverified.

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Summary

Nemotron Orchestrator 8B is a state-of-the-art 8B parameter orchestration model designed for complex, multi-turn agentic tasks.
  • It coordinates multiple expert models and tools to solve problems efficiently.
  • On the Humanity’s Last Exam benchmark, it outperforms GPT-5 while being approximately 2.5× more efficient.
  • The model delivers higher accuracy at lower computational cost than monolithic frontier models.
  • It is intended for research and development use only.