Why look beyond Cerebras

Cerebras Systems offers a unique approach to AI acceleration with its Wafer-Scale Engine (WSE) and CS-2 system, designed for training extremely large models and high-performance scientific computing. The architecture integrates a massive number of cores on a single silicon wafer, aiming to minimize communication latency. This specialization, while beneficial for specific workloads, can present considerations for organizations with diverse AI requirements or those seeking more flexible deployment options.

Moving beyond Cerebras might be necessitated by a need for broader hardware compatibility, integration with existing cloud infrastructure, or a preference for more established software ecosystems. Organizations might also seek alternatives that offer a more granular scaling model, pay-as-you-go cloud services, or a wider range of pre-built AI services for various applications beyond deep learning training. The high specialization of Cerebras systems means that the software stack and operational expertise required are also specific, prompting some to consider platforms with a more generalized or widely adopted developer experience.

Top alternatives ranked

  1. 1. NVIDIA — GPU-accelerated computing for AI and HPC

    NVIDIA is a dominant provider of graphics processing units (GPUs) that have become a de facto standard for AI and high-performance computing (HPC) workloads. Their ecosystem includes hardware like the A100 and H100 Tensor Core GPUs, as well as a comprehensive software stack with CUDA, cuDNN, and various AI frameworks. NVIDIA's platforms are widely adopted across research institutions, enterprises, and cloud providers, supporting a broad spectrum of AI tasks from training large language models to real-time inference and scientific simulations. The flexibility of GPU clusters allows for scalable deployments, and the extensive developer community contributes to a rich set of tools and resources. NVIDIA's approach provides a balance of raw computational power and a mature, widely supported software environment.

    • Best for: General-purpose AI training and inference, HPC, cloud-based AI infrastructure, diverse deep learning workloads.

    See our in-depth profile on NVIDIA.

    Learn more at NVIDIA's official website.

  2. 2. Graphcore — Intelligent Processing Units for AI compute

    Graphcore develops Intelligence Processing Units (IPUs), a class of silicon designed specifically for AI compute. Their Bow IPU and IPU-POD systems aim to provide high performance and efficiency for machine intelligence workloads by employing a highly parallel architecture with in-processor memory. Graphcore's technology is designed to accelerate AI model training and inference, particularly for graph-based neural networks and other emerging AI algorithms. The company emphasizes a software-first approach, with its Poplar SDK providing a programming environment optimized for the IPU architecture. Graphcore targets enterprise and cloud customers seeking dedicated AI acceleration that differs from traditional GPU or CPU paradigms, focusing on maximizing throughput and minimizing latency for specific AI tasks.

    • Best for: Specialized AI model training, graph neural networks, high-performance AI inference, alternative AI hardware exploration.

    See our in-depth profile on Graphcore.

    Learn more at Graphcore's official website.

  3. 3. Groq — Low-latency AI inference engine

    Groq specializes in high-performance, low-latency AI inference through its Language Processing Unit (LPU) hardware architecture. Unlike general-purpose accelerators, Groq's design prioritizes deterministic latency and high throughput for specific AI workloads, particularly large language models (LLMs). The LPU architecture aims to simplify the compiler's task, leading to predictable performance and efficient resource utilization. Groq's systems are positioned for real-time AI applications where responsiveness is critical, such as conversational AI, search, and autonomous systems. Their focus on inference differentiates them from many training-centric hardware providers, offering a specialized solution for deploying trained models with minimal delay.

    • Best for: Real-time AI inference, low-latency large language model deployment, edge AI applications, high-throughput inference scenarios.

    See our in-depth profile on Groq.

    Learn more at Groq's official website.

  4. 4. Google Vertex AI — Unified MLOps platform with integrated AI accelerators

    Google Vertex AI is a managed machine learning platform that covers the entire ML lifecycle, from data preparation and model training to deployment and monitoring. While it is a software platform, it provides access to Google Cloud's underlying AI infrastructure, including NVIDIA GPUs and Google's own Tensor Processing Units (TPUs). Vertex AI integrates various Google services and pre-trained models, allowing developers to build, deploy, and scale AI applications without managing infrastructure. Its emphasis is on MLOps, providing tools for experiment tracking, model versioning, and continuous integration/delivery for ML. For organizations seeking a comprehensive, cloud-native AI development environment with access to diverse hardware options, Vertex AI offers a flexible and scalable solution.

    • Best for: End-to-end ML lifecycle management, integrating generative AI models, custom model training and deployment, large-scale data processing in the cloud.

    See our in-depth profile on Google Vertex AI.

    Learn more at Google Vertex AI documentation.

  5. 5. Azure OpenAI Service — Enterprise-grade access to OpenAI models within Azure

    Azure OpenAI Service provides organizations with secure and scalable access to OpenAI's powerful language models, including GPT-3.5, GPT-4, and DALL-E, directly within the Microsoft Azure ecosystem. This service combines the capabilities of OpenAI's models with Azure's enterprise-grade security, compliance, and regional availability. It enables developers to integrate advanced AI capabilities into their applications, fine-tune models with their own data, and deploy them with Azure's infrastructure. While not a direct hardware alternative, it offers a managed platform for consuming cutting-edge AI models, abstracting away the underlying hardware complexities. For enterprises focused on leveraging large language models without deep infrastructure management, Azure OpenAI Service provides a robust solution.

    • Best for: Integrating OpenAI models into enterprise applications, building secure AI solutions within Azure, leveraging pre-trained LLMs, custom model fine-tuning with enterprise data.

    See our in-depth profile on Azure OpenAI Service.

    Learn more at Azure OpenAI Service overview.

  6. 6. OpenAI Enterprise — Custom, high-performance API access for large organizations

    OpenAI Enterprise offers a version of OpenAI's API tailored for large organizations, providing enhanced performance, dedicated instances, and extended context windows for their flagship models like GPT-4. This offering is designed for enterprises with significant AI workloads and stringent requirements for data privacy, security, and throughput. It allows organizations to leverage OpenAI's cutting-edge models for custom applications, internal knowledge management, and large-scale automation, with direct support from OpenAI. Similar to Azure OpenAI Service, it focuses on providing access to advanced AI models as a service rather than raw hardware. The enterprise offering addresses the unique needs of large-scale deployments, including fine-tuning models on proprietary datasets and ensuring compliance.

    • Best for: Large-scale enterprise AI deployments, custom model training and fine-tuning, enhanced data privacy and security needs, high-volume API access to advanced LLMs.

    See our in-depth profile on OpenAI Enterprise.

    Learn more at OpenAI Platform documentation.

  7. 7. Anthropic Enterprise (Claude for Work) — Secure, ethical large language models for business

    Anthropic Enterprise, also known as Claude for Work, provides secure and scalable access to Anthropic's Claude family of large language models for business applications. Anthropic emphasizes developing AI systems that are helpful, harmless, and honest, with a focus on constitutional AI and safety. Their enterprise offering provides access to models with large context windows, enabling processing of extensive documents and complex queries. It is designed for organizations prioritizing responsible AI development, data security, and the ability to integrate advanced conversational AI into their workflows. Like OpenAI Enterprise and Azure OpenAI Service, it offers AI capabilities as a service, allowing businesses to focus on application development rather than infrastructure management.

    • Best for: Secure enterprise-grade AI, large language model deployment with an emphasis on safety and ethics, internal knowledge management, coding assistance.

    See our in-depth profile on Anthropic Enterprise.

    Learn more at Anthropic documentation.

Side-by-side

Feature Cerebras NVIDIA Graphcore Groq Google Vertex AI Azure OpenAI Service OpenAI Enterprise Anthropic Enterprise
Core Offering Wafer-scale AI accelerators GPU hardware & software IPU hardware & software LPU inference hardware Managed MLOps platform OpenAI models in Azure OpenAI models direct Anthropic models direct
Primary Focus Large-scale AI training General AI/HPC AI training/inference Low-latency AI inference End-to-end ML lifecycle Enterprise LLM integration Enterprise LLM access Ethical LLM integration
Hardware Type Wafer-Scale Engine (WSE) GPUs (A100, H100) IPUs (Bow IPU) LPUs Cloud GPUs/TPUs Azure infrastructure OpenAI infrastructure Anthropic infrastructure
Deployment Model On-premise, dedicated cloud On-premise, cloud On-premise, cloud On-premise, cloud Cloud service Cloud service Cloud API service Cloud API service
Software Ecosystem Proprietary compiler/runtime CUDA, cuDNN, frameworks Poplar SDK GroqWare SDK TensorFlow, PyTorch, Scikit-learn Azure ML, Python SDK Python, Node.js SDKs Python, TypeScript SDKs
Key Differentiator Wafer-scale parallelism, minimal latency Broad adoption, mature ecosystem In-processor memory, AI-specific design Deterministic low-latency inference Unified MLOps, diverse hardware access Azure security/compliance, OpenAI models Direct access to advanced OpenAI models Safety-focused LLMs, large context
Best For Extreme-scale deep learning Versatile AI/HPC workloads Graph neural networks, specific AI models Real-time LLM inference Managed ML development Secure enterprise LLM use High-volume custom LLM applications Responsible LLM deployment

How to pick

Selecting an alternative to Cerebras depends on your organization's specific AI objectives, existing infrastructure, budget constraints, and technical expertise. Consider the following decision framework:

If your primary need is raw computational power for diverse AI workloads:

  • NVIDIA: If you require a highly flexible and widely supported platform for general-purpose AI training, inference, and HPC. NVIDIA's GPUs and extensive software ecosystem are a mature choice for most deep learning applications.
  • Graphcore: If you are exploring specialized hardware for AI, particularly for graph-based neural networks or unique AI algorithms, and are willing to adapt to a different architecture than traditional GPUs.

If your focus is on extremely low-latency AI inference:

  • Groq: If real-time responsiveness for applications like large language models or autonomous systems is paramount. Groq's LPU architecture is purpose-built for high-throughput, low-latency inference.

If you prefer a managed cloud-based AI development and deployment environment:

  • Google Vertex AI: If you need a comprehensive MLOps platform that integrates data science tools, model lifecycle management, and access to cloud-based GPUs and TPUs. This is ideal for organizations building and deploying custom ML models at scale within a cloud ecosystem.

If your main objective is to leverage advanced large language models (LLMs) as a service:

  • Azure OpenAI Service: If your organization is already on Azure and prioritizes enterprise-grade security, compliance, and integration with Microsoft services while using OpenAI's models.
  • OpenAI Enterprise: If you require direct, high-performance access to OpenAI's flagship models with dedicated resources and advanced features for large-scale, custom enterprise applications.
  • Anthropic Enterprise: If your organization places a strong emphasis on responsible AI development, safety, and ethical considerations, and needs access to powerful, large-context LLMs for business applications.

Consider your team's expertise:

  • Platforms like NVIDIA, Google Vertex AI, Azure OpenAI Service, and OpenAI/Anthropic Enterprise generally offer more accessible and widely documented developer experiences compared to highly specialized hardware like Cerebras or Graphcore, which may require specific expertise in their proprietary software stacks.

Evaluate your scaling needs:

  • Cloud platforms (Google Vertex AI, Azure OpenAI Service, OpenAI/Anthropic Enterprise) offer elastic scaling and pay-as-you-go models, suitable for fluctuating workloads.
  • Hardware alternatives (NVIDIA, Graphcore, Groq) provide dedicated performance but require upfront investment and infrastructure management, often preferred for consistent, large-scale, or privacy-sensitive on-premise deployments.