In the rapidly evolving AI landscape, two companies have emerged as leaders in AI inference hardware: SambaNova and Cerebras.
Both are pioneering advancements to accelerate AI workloads, offering alternatives to traditional GPU-based systems such as NVIDIA. While both offer significant increases in inference performance compared to GPUs, they differ greatly in how they achieve those results. Understanding the nuances between their offerings is crucial for developers, enterprise decision-makers, and researchers aiming to optimize their AI applications.
The SambaNova platform can run both large numbers of models and large models on a single system. In contrast, Cerebras, which relies on SRAM only, is forced to commit large volumes of hardware to run even a single small model. While it is capable of running that single model with high performance, the inherent inefficiency that requires massive volumes of hardware make it impractical to implement at scale.
Physical racks required to power a model
The physical system requirements of Cerebras to run models limits it to a cloud-based deployment. It is impractical for most organizations to use that platform in an on-premises environment.
SambaNova SN40L: A flexible and scalable AI accelerator
The SambaNova SN40L Reconfigurable Dataflow Unit (RDU) is a cutting-edge AI accelerator, engineered to meet the demands of large-scale AI inference with enterprise-level scalability. Its design encompasses several innovative features that enhance performance, efficiency, and flexibility.
The SN40L employs a sophisticated three-tier memory hierarchy:
Numbers are per SN40L chip, each SambaRack system has 16 chips for 24 TBs of DDR DRAM to power the largest models on a single system.
This architecture ensures that data is efficiently managed and accessed, balancing speed and power consumption effectively.
Reconfigurable Dataflow Unit (RDU) for high efficiency
The SN40L dataflow architecture allows for dynamic adaptation to various AI workloads. This flexibility minimizes bottlenecks and maximizes computational efficiency, ensuring that resources are optimally utilized across diverse tasks.
Scalability for massive parameter models
One of the standout features of SN40L is its ability to scale and manage very large models, such as DeepSeek-R1 671B405B and DeepSeek-R1 671B. This scalability surpasses many competitors and addresses the increasing complexity of modern AI models. For example, Cerebras architecture faces challenges due to its rigid wafer-scale constraints.
Cerebras WSE-3: A massive wafer-scale chip with high bandwidth
The Cerebras Wafer-Scale Engine 3 (WSE-3) is physically the largest AI accelerator currently shipping. However, its physical size is not a meaningful metric and certain limitations affect its versatility and efficiency in diverse AI workloads.
Scale and computational power
The WSE-3 is distinguished by its massive architecture:
- 4 trillion transistors and 900,000 compute cores: Enables it to handle complex computations with high speed.
- 44 GB on-chip SRAM with 21 PB/s bandwidth: Facilitates rapid data access, crucial for high-performance AI tasks.
Architectural limitations impacting flexibility
Despite its strengths, the WSE-3's design presents certain constraints:
- Absence of off-chip memory integration: Lack of external memory interfaces, such as HBM or DRAM, requires partitioning large models across multiple chips, complicating scalability and increasing system complexity.
- Tokenomics: The architecture is tailored for strong single-user TPS but may limit efficiency in multi-user enterprise environments requiring simultaneous diverse workloads.
- High power consumption and specialized cooling: The wafer-scale integration leads to significant power demands, requiring advanced liquid cooling solutions that increase costs and infrastructure requirements for data centers.
SambaCloud: for Developers & Enterprise AI
The SambaCloud offers fully managed AI inference with API access, allowing developers to deploy models without requiring specialized hardware. It supports large-scale AI applications and real-time inference workloads. The platform integrates seamlessly with enterprise AI workflows, enabling developers to expedite the deployment of AI solutions.
Key features of SambaCloud:
- High-performance inference: Delivers performance inference, enabling rapid processing of complex models.
- Comprehensive model access: Access to state-of-the-art models, including Llama 3 variants, Llama 4 Maverick, DeepSeek R1 671B, and OpenAI GPT-OSS 120B, caters to diverse application needs.
- Scalability: The SambaNova platform is capable of powering both large numbers of models and very large models on a single system. This enables users to take advantage of the best models for their workloads, to build agentic applications using multiple models, and to easily scale resources to meet any requirement.
- Developer-friendly platform: Designed to support developers in efficiently building and deploying AI applications, offering resources such as starter kits and technical documentation.
- Enterprise-grade solutions: Provides scalable solutions beyond individual developers – suitable for enterprise-level AI deployments.
SambaNova – Cost-Optimized Scalability
The SambaNova platform’s compact design, utilizing just 16 chips, reduces the data center footprint and associated operational costs, making it particularly suitable for large-scale AI deployments.
Key aspects contributing to its cost-optimized scalability include:
Suitability for Large-Scale Deployments
The SN40L's architecture is tailored to balance performance and efficiency, addressing the challenges of scaling AI applications:
- Handling massive models: Capable of serving the largest open-source models; facilitates the deployment of complex AI models without compromising performance.
- Enhanced sequence lengths: Supports sequence lengths, enabling the processing of extensive data sequences in a single pass, which is crucial for applications like natural language processing.
- Lower Total Cost of Ownership (TCO): Efficiency in running large language model (LLM) inference reduces operational costs, making it a cost-effective solution for enterprises.
- Reduced power consumption: The SambaNova platform only consumes an average of 10 kW of power.
Why SambaNova is the superior option
SambaNova's SN40L chip offers a flexible and scalable solution for AI inference with a memory architecture that supports large models efficiently.
The SambaNova platform is the only one that offers high performance inference with the best and largest open-source models on a single system. This enables SambaNova to offer users access to high-speed inference, on the latest models, using a platform that can scale to meet the needs of any environment.
Developers and organizations with pre-trained models can bring their own checkpoints to SambaNova and take advantage of high-speed inference. As their needs grow to use other models, and combination of models in agentic workflows, SambaNova is the choice for scalability and flexibility.
Its compact design and lower power consumption make it the most efficient choice for enterprises seeking to deploy AI at scale.
The SambaCloud platform further enhances its appeal by providing accessible and managed AI services, streamlining the development and deployment process for developers and enterprises.
In contrast, while Cerebras' WSE-3 presents impressive specifications, its scalability limitations and higher power requirements position SambaNova as the more versatile and practical choice for diverse AI inference needs.