nvidia/Orchestrator-8B

NVIDIA Orchestrator is purpose-built for agent workflows and complex task sequencing. It excels in planning, structured reasoning, autonomous execution, and coordinating multiple tools or APIs. With deep GPU-level optimization, it delivers superior throughput and low latency in enterprise automation scenarios.

NVIDIA Chat 16384 Tokens
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api_example.sh

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "nvidia/Orchestrator-8B",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 0.4,
  "max_tokens": 4096,
  "stream": true,
  "top_p": 1
}'

Technical Specifications

Model Architecture & Performance

Model Size 7B params
Context Length 16384 Tokens
Quantization fp16
Tokens/Second 386
Architecture Optimized Transformer (TensorRT-LLM enhanced)
Precision bfloat16
License NVIDIA Open Model License
Release Date 2025
Developers NVIDIA

Pricing

Pay-per-use, no commitments

Input Tokens $0.00021/1K Tokens
Output Tokens $0.00025/1K Tokens

API Reference

Complete parameter documentation

Parameter Type Default Description
stream boolean true Enable streaming responses for real-time output.
temperature number 0.4 Controls creativity and randomness. Lower values recommended for deterministic task execution.
max_tokens number 4096 Maximum number of tokens the model can generate.
top_p number 1 Controls nucleus sampling for more predictable output.

Explore the full request and response schema in our external API documentation

Performance

Strengths & considerations

Strengths Considerations
Highly optimized for NVIDIA GPU inference
Superior multi-step reasoning and tool orchestration
Supports structured outputs for automation pipelines
Ideal for building agents that interact with APIs, databases, and tools
Requires GPU acceleration for optimal performance
Not intended for creative writing or open-ended generation
Performance depends on system-level optimization (TensorRT-LLM recommended)

Use cases

Recommended applications for this model

AI agents for enterprise automation
Tool and API orchestration
RAG + workflow pipelines
Long-context reasoning
DevOps automation and observability agents
Data extraction & structured decision making

Enterprise
Platform Integration

Docker

Docker Support

Official Docker images for containerized deployments

Kubernetes

Kubernetes Ready

Production-grade KBS manifests and Helm charts

SDK

SDK Libraries

Official SDKs for Python, Javascript, Go, and Java

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AI Agents Team

Agent Systems & Orchestration