Why look beyond Graphcore

Graphcore specializes in Intelligence Processing Units (IPUs), a class of parallel processors optimized for machine learning workloads. While their IPUs and the Poplar SDK are designed for high-performance AI training and inference, organizations may consider alternatives for several reasons. One primary factor is hardware ecosystem maturity and breadth. General-purpose GPUs, particularly from NVIDIA, have established a dominant market position, offering a wide array of compatible software frameworks, libraries, and developer tools, alongside extensive cloud and on-premise deployment options NVIDIA Data Center AI. This broad ecosystem can simplify integration and reduce the learning curve for teams already familiar with GPU-based development.

Cost and accessibility are also considerations. While Graphcore offers custom enterprise pricing, the upfront investment and specialized nature of IPU hardware may not align with all budget constraints or project scales. Cloud providers offer flexible, pay-as-you-go access to various AI accelerators, including GPUs and custom silicon, allowing businesses to scale compute resources dynamically without significant capital expenditure Google Cloud AI Platform. Furthermore, some alternatives focus on specific niches, such as extreme-scale AI training with wafer-scale engines or highly energy-efficient edge AI solutions, providing specialized performance advantages for particular use cases that diverge from Graphcore's core offering.

Top alternatives ranked

1. NVIDIA — Dominant provider of GPU technology for AI

NVIDIA is a leading developer of graphics processing units (GPUs) and related software platforms critical for AI and deep learning. Their GPUs, such as the A100 and H100, are widely adopted for training large-scale neural networks and accelerating AI inference across various industries. NVIDIA's ecosystem includes CUDA, a parallel computing platform, and cuDNN, a GPU-accelerated library for deep neural networks, which together provide a comprehensive environment for AI development NVIDIA AI and Deep Learning. The company also offers specialized platforms like NVIDIA HGX for data center AI and NVIDIA Jetson for edge AI, catering to a broad spectrum of deployment scenarios from supercomputers to embedded devices. Their strong market presence, extensive developer community, and continuous innovation in AI hardware and software make them a primary alternative for organizations seeking robust and scalable AI infrastructure.

Best for:

  • Large-scale deep learning model training
  • High-performance AI inference in data centers
  • Developing and deploying AI across diverse hardware (edge to cloud)
  • Organizations leveraging an established AI software ecosystem

View the NVIDIA profile.

2. Intel — Broad portfolio of processors and AI accelerators

Intel offers a diverse range of hardware solutions for AI, extending beyond their traditional CPUs to include specialized AI accelerators. Their portfolio features Intel Xeon Scalable processors, which incorporate AI acceleration capabilities like Intel Deep Learning Boost, and dedicated AI hardware such as Intel Gaudi AI accelerators (formerly Habana Labs) for deep learning training and inference Intel AI Overview. Intel also provides the OpenVINO Toolkit, an open-source toolkit for optimizing and deploying AI inference models across various Intel hardware, including CPUs, GPUs, and VPUs. This broad approach allows Intel to cater to different performance, power, and cost requirements, from edge devices to enterprise data centers. Their long-standing presence in the enterprise hardware market and commitment to open standards provide a familiar and flexible environment for many organizations exploring AI solutions.

Best for:

  • General-purpose AI workloads on standard server infrastructure
  • Optimizing AI inference on edge devices and client PCs
  • Organizations seeking diverse hardware options for AI deployment
  • Integrating AI with existing Intel-based IT infrastructure

View the Intel profile.

3. Cerebras Systems — Wafer-scale AI compute for extreme performance

Cerebras Systems specializes in high-performance AI compute with its Wafer-Scale Engine (WSE) technology. The WSE is a single chip the size of an entire silicon wafer, designed to deliver unprecedented compute density and memory bandwidth for deep learning workloads. Their flagship product, the Cerebras CS-2 system, integrates the WSE-2 to accelerate training of the largest and most complex AI models, often reducing training times from months to days or hours Cerebras Technology. This unique approach eliminates many memory and communication bottlenecks found in multi-chip GPU clusters, making it particularly suitable for research institutions and enterprises pushing the boundaries of AI model size and complexity. Cerebras focuses on providing a complete system solution, including hardware and a software stack, optimized for large-scale AI.

Best for:

  • Training extremely large and complex AI models
  • Minimizing AI model training time
  • Research and development of cutting-edge AI architectures
  • Organizations requiring single-node, high-density AI compute

View the Cerebras Systems profile.

4. Google AI — Cloud-based AI infrastructure and custom silicon

Google AI encompasses a wide array of AI services, platforms, and hardware within the Google Cloud ecosystem, alongside foundational research by Google DeepMind. For AI infrastructure, Google Cloud offers access to NVIDIA GPUs and its custom-designed Tensor Processing Units (TPUs) Google Cloud TPUs. TPUs are application-specific integrated circuits (ASICs) optimized for machine learning workloads, particularly for training large neural networks. Google AI provides various services, including Vertex AI, a managed machine learning platform that supports the entire ML lifecycle—from data preparation and model training to deployment and monitoring. This integrated approach, coupled with Google's expertise in large-scale distributed systems and open-source contributions like TensorFlow and JAX, makes it a strong alternative for cloud-native AI development and deployment.

Best for:

  • Cloud-native AI development and deployment
  • Leveraging specialized TPU hardware for deep learning
  • Integrated machine learning platform (Vertex AI)
  • Organizations within the Google Cloud ecosystem

View the Google AI profile.

5. AWS Inferentia and Trainium — Purpose-built AI accelerators for cloud workloads

Amazon Web Services (AWS) offers its own purpose-built AI accelerators, Inferentia and Trainium, designed to provide high-performance and cost-effective solutions for machine learning inference and training in the cloud. AWS Inferentia is optimized for high-throughput, low-latency inference workloads, ideal for deploying large language models and other deep learning models at scale AWS Inferentia. AWS Trainium is designed for deep learning training, offering high performance and efficiency for complex models. These custom chips are integrated into Amazon EC2 instances, providing users with flexible compute options within the AWS cloud environment. By offering specialized hardware alongside a comprehensive suite of AI/ML services like Amazon SageMaker, AWS provides an end-to-end cloud-based solution for AI development and deployment, leveraging its global infrastructure and scalability.

Best for:

  • Cost-optimized AI inference and training in the AWS cloud
  • Organizations with existing AWS infrastructure
  • Deploying large-scale deep learning models for inference
  • Developing and training complex AI models efficiently in the cloud

View the AWS Inferentia and Trainium profile.

6. Azure ND-series VMs — High-performance virtual machines with NVIDIA GPUs

Microsoft Azure provides a range of high-performance virtual machines, particularly the ND-series, which are equipped with NVIDIA GPUs, including the A100 and H100 Tensor Core GPUs Azure ND-series VMs. These VMs are designed for demanding AI workloads, offering significant compute power for deep learning training and inference. Azure's infrastructure scales to support large clusters of these GPU-accelerated VMs, enabling organizations to train massive models and run complex simulations. Complementing its hardware offerings, Azure provides a comprehensive AI platform, including Azure Machine Learning, which facilitates the end-to-end ML lifecycle from data preparation to model deployment. Azure's global reach, enterprise-grade security, and integration with other Microsoft services make it a compelling alternative for organizations seeking a robust cloud environment for AI.

Best for:

  • Cloud-based deep learning training and inference with NVIDIA GPUs
  • Organizations leveraging Microsoft Azure's cloud ecosystem
  • Scalable AI infrastructure for large-scale projects
  • Integrating AI with existing Microsoft enterprise solutions

View the Azure ND-series VMs profile.

7. Hugging Face — Platform for open-source AI models and compute

Hugging Face has emerged as a central hub for the open-source AI community, providing access to a vast repository of pre-trained models (e.g., Transformers), datasets, and tools. While not a hardware provider, Hugging Face offers a platform that facilitates the use of various compute backends for model training and inference. Their ecosystem, including libraries like Transformers and Accelerate, abstracts away much of the underlying hardware complexity, allowing developers to focus on model development Hugging Face Accelerate. Hugging Face also provides inference APIs and hosted solutions that run on various cloud providers' hardware, including GPUs, making advanced AI models accessible without directly managing infrastructure. For organizations prioritizing open-source flexibility and rapid prototyping, Hugging Face offers a strong software-centric alternative that can run on diverse hardware.

Best for:

  • Rapid prototyping and deployment of open-source AI models
  • Leveraging a vast community of pre-trained models and datasets
  • Abstracting hardware complexity for AI development
  • Researchers and developers focused on model innovation

View the Hugging Face profile.

Side-by-side

Feature Graphcore NVIDIA Intel Cerebras Systems Google AI (Cloud TPUs) AWS Inferentia/Trainium Azure ND-series VMs Hugging Face
Primary Hardware IPUs (Intelligence Processing Units) GPUs (A100, H100) CPUs, Gaudi AI Accelerators Wafer-Scale Engine (WSE) TPUs (Tensor Processing Units) Inferentia, Trainium ASICs NVIDIA GPUs (A100, H100) Vendor-agnostic (focus on software)
Deployment Model On-premise, partner cloud On-premise, Public Cloud On-premise, Public Cloud On-premise, partner cloud Public Cloud (Google Cloud) Public Cloud (AWS) Public Cloud (Azure) Cloud-hosted, self-hosted
Key Software Ecosystem Poplar SDK CUDA, cuDNN OpenVINO, oneAPI CS-2 Software Stack TensorFlow, JAX, PyTorch SageMaker, PyTorch, TensorFlow Azure ML, PyTorch, TensorFlow Transformers, Accelerate
Target Workloads Large-scale training/inference General-purpose deep learning Diverse AI (edge to cloud) Extreme-scale training Large-scale training/inference Cost-optimized inference/training Large-scale training/inference Model development, deployment
Scalability Focus IPU-based systems GPU clusters, distributed systems Diverse hardware integration Single-node, high-density Cloud-native, TPU Pods Cloud-native, EC2 instances Cloud-native, VM clusters Software-driven, flexible backends
Pricing Model Custom enterprise Hardware purchase, cloud usage Hardware purchase, cloud usage Custom enterprise Pay-as-you-go (cloud) Pay-as-you-go (cloud) Pay-as-you-go (cloud) Free (open-source), paid (hosted)

How to pick

Selecting an alternative to Graphcore involves evaluating your specific AI workload requirements, existing infrastructure, budget, and strategic priorities. Consider these decision points:

  1. Workload Type and Scale:
    • If your primary need is general-purpose deep learning training and inference across a wide range of models and frameworks, NVIDIA remains a robust choice due to its mature ecosystem and broad compatibility.
    • For organizations pushing the absolute limits of model size and training speed, where single-node performance is paramount, Cerebras Systems with its wafer-scale engine offers a specialized, high-density solution.
    • If you require cost-effective, high-throughput inference or efficient training specifically within a cloud environment, AWS Inferentia and Trainium or Google Cloud TPUs provide purpose-built hardware integrated into their respective cloud platforms.
  2. Deployment Environment:
    • For cloud-native strategies, Google AI, AWS Inferentia/Trainium, and Azure ND-series VMs offer scalable, managed infrastructure with pay-as-you-go models, reducing upfront capital expenditure.
    • If you prefer on-premise deployments or need hybrid cloud flexibility, NVIDIA and Intel provide hardware that can be deployed in private data centers or integrated into various cloud environments.
  3. Software Ecosystem and Developer Experience:
    • If your team is already heavily invested in CUDA-based development and the NVIDIA ecosystem, staying with NVIDIA often minimizes migration effort.
    • For developers prioritizing open-source models, rapid prototyping, and abstraction from hardware details, Hugging Face provides a powerful software layer that can run on various compute backends.
    • Organizations committed to Intel's broader enterprise hardware and software tools might find Intel's diverse AI portfolio and OpenVINO Toolkit a natural fit.
  4. Budget and Total Cost of Ownership (TCO):
    • Cloud options like Google AI, AWS, and Azure allow for flexible scaling and can convert capital expenditure into operational expenditure. Assess their pricing models against your anticipated usage.
    • Proprietary hardware solutions from NVIDIA, Intel, or Cerebras Systems involve significant upfront investment but can offer long-term cost benefits for consistent, high-volume workloads.
  5. Integration and Vendor Lock-in:
    • Consider how well the alternative integrates with your existing data pipelines, MLOps tools, and enterprise systems. Cloud providers often offer integrated platforms like Azure Machine Learning or Google Cloud Vertex AI.
    • Evaluate the degree of vendor lock-in. Open-source-centric approaches like those facilitated by Hugging Face can offer greater flexibility in switching underlying infrastructure.