nGraph · 0 reviews

nGraph

End-to-end deep learning compiler accelerating AI model deployment across any hardware

Machine Learning Software

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About nGraph

nGraph is a comprehensive end-to-end deep learning compiler designed to accelerate both inference and training workloads across heterogeneous hardware platforms. It acts as an intermediate representation layer between popular deep learning frameworks (TensorFlow, PyTorch, MXNet) and diverse hardware targets (CPUs, GPUs, TPUs, specialized accelerators), enabling seamless model optimization without requiring changes to existing code. The compiler performs advanced graph-level optimizations including operator fusion, memory layout optimization, and precision tuning to maximize performance. By decoupling frameworks from hardware implementations, nGraph reduces time-to-market for AI solutions while improving model efficiency. AiDOOS enhances nGraph deployment through governance frameworks, multi-tenancy support, and enterprise scaling capabilities. Organizations leverage nGraph to standardize AI infrastructure, reduce deployment complexity, and achieve consistent performance across distributed environments, enabling rapid innovation cycles and cost-effective AI operations at enterprise scale.

Challenges It Solves

Complex framework-to-hardware compatibility issues slowing AI model deployment timelines
Suboptimal inference and training performance requiring expensive hardware upgrades
Fragmented toolchains increasing operational overhead and reducing development velocity
Difficulty achieving consistent performance across diverse hardware and cloud environments

Faster model-to-production deployment cycles

Improved inference and training throughput

Reduced infrastructure costs through optimization

Use Cases

Production Inference Optimization

Deploy trained models to production with minimal latency and maximum throughput. nGraph optimizes model execution for inference-specific workloads across edge devices and cloud servers.

48% Reduced inference latency by up to 50 percent

Distributed Training Acceleration

Accelerate large-scale model training across distributed GPU and TPU clusters. Compiler automatically optimizes distributed execution patterns and communication overhead.

52% Improved training throughput and reduced training time

Edge Device Deployment

Deploy AI models to resource-constrained edge devices with optimized binary compilation. Reduce model size and memory footprint while maintaining accuracy.

35% Enabled efficient edge AI deployment

Hardware-Agnostic CI/CD Pipelines

Build unified CI/CD pipelines that deploy models across heterogeneous hardware without recompilation. Standardize AI infrastructure across development, staging and production environments.

42% Streamlined deployment across diverse hardware

Pricing

Pricing available on request

nGraph pricing is customized based on your team size, integrations, and requirements. AiDOOS will get you a scoped proposal — for free.

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Key Features

Universal Framework Support

Seamlessly integrate with TensorFlow, PyTorch, MXNet and other frameworks

Deploy models without framework-specific rewriting or rework

Cross-Hardware Compilation

Compile to CPUs, GPUs, TPUs and specialized accelerators

Single codebase targets multiple hardware platforms efficiently

Advanced Graph Optimization

Automatic operator fusion, memory optimization and precision tuning

Achieve 30-50% performance improvements through compiler optimizations

Inference & Training Acceleration

Optimize both inference latency and training throughput

Reduce inference latency and training time simultaneously

Hardware-Agnostic Abstraction

Write once, deploy across diverse hardware ecosystems

Eliminate hardware lock-in and improve deployment flexibility

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Enterprise Readiness

Model Compilation Integrity

Secure Framework Integration

Hardware Execution Isolation

Audit and Compliance Logging

Precision and Quantization Controls

Integrations

7 total apps

Native integration enables TensorFlow models to leverage nGraph compilation for optimized inference and training

PyTorch models compile through nGraph intermediate representation for cross-platform optimization

Direct framework integration allowing MXNet models to utilize compiler optimizations

Open Neural Network Exchange format support enables framework-agnostic model interchange and optimization

Containerized deployment and orchestration of nGraph-optimized inference services

Integration with Intel's optimization toolkit for enhanced inference performance

Native support for major cloud providers enabling optimized model deployment at scale

AiDOOS Managed Deployment

Deploy nGraph in

AiDOOS handles setup, CRM integration, SSO config, and user provisioning. Your team goes live — not your IT department.

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Deployments

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Adoption rate

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Post-deploy sat.

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Time to value

Prerequisites

Configuration Options

Virtual Delivery Center · A new delivery category

A Virtual Delivery Center for nGraph

Pre-vetted experts and AI agents in the loop, assembled as a delivery pod. Pay in Delivery Units — universal pricing across roles, seniority, and tech stacks. No hiring, no contracting, no procurement cycle.

Plans from $2,000 — Starter Pack, 10 Delivery Units, 90 days
Refundable on unused Delivery Units, anytime — no questions asked
Re-delivery guarantee on acceptance miss
Pre-flight delivery sizing — you see the plan before you commit

Get a delivery plan for nGraph What’s a Virtual Delivery Center?

How a Virtual Delivery Center delivers nGraph

Outcome-based delivery via AiDOOS’s VDC model. Why VDC vs traditional consulting? →

Outcome-Based

Pay for results, not hours

Milestone-Driven

Clear deliverables at each phase

Expert Network

Access to certified specialists

Implementation Timeline

Discover

Requirements & assessment

Integrate

Setup & data migration

Validate

Testing & security audit

Rollout

Deployment & training

Optimize

Performance tuning

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Frequently Asked Questions

What deep learning frameworks does nGraph support?

nGraph supports TensorFlow, PyTorch, Apache MXNet and any framework implementing the ONNX standard. AiDOOS extends support through managed framework integration and version compatibility management.

Can nGraph optimize models for multiple hardware platforms simultaneously?

Yes. nGraph compiles models to an intermediate representation, enabling compilation to CPUs, GPUs, TPUs and specialized accelerators from a single source. AiDOOS provides multi-target compilation orchestration for enterprise environments.

What performance improvements should we expect?

Performance gains typically range from 30-50% depending on model architecture and hardware. Inference optimizations often exceed training optimizations. AiDOOS provides performance benchmarking and profiling services for your specific workloads.

How does nGraph integrate with existing CI/CD pipelines?

nGraph provides APIs and CLI tools for seamless integration into automated deployment pipelines. AiDOOS offers managed pipeline services with governance, versioning and multi-environment deployment orchestration.

Is nGraph suitable for edge device deployment?

Yes. nGraph optimizes for resource-constrained environments, reducing model size and memory footprint. AiDOOS provides edge deployment management, versioning and update orchestration across distributed edge devices.

What security measures are in place for model compilation?

nGraph includes compilation integrity verification, isolated execution environments and comprehensive audit logging. AiDOOS adds encryption, access control and compliance frameworks for enterprise security requirements.