SambaNova vs. Cerebras: The Ultimate AI Inference Comparison

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. Understanding the nuances between their offerings is crucial for developers, enterprise decision-makers, and researchers aiming to optimize their AI applications.

The following comparison examines key differentiators: inference speed, hardware architecture, cloud AI offerings, and power efficiency. SambaNova emerges as the superior choice for scalable and efficient AI inference solutions.

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:

Three-Tier Memory System for Optimized Performance

The SN40L employs a sophisticated three-tier memory hierarchy:

• • • On-Package High Bandwidth Memory (HBM): Delivers high-speed data throughput, essential for handling large datasets.
    • Off-Package DDR DRAM: Offers substantial storage capacity for extensive datasets and models.
    • On-Chip Distributed SRAM: Provides rapid access to frequently used data, minimizing latency.

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's Reconfigurable Dataflow Unit 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 SN40L's standout features is its ability to scale and manage very large models such as Llama 3.1 405B 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.

These features position the SambaNova SN40L as a versatile and powerful AI accelerator capable of meeting the rigorous demands of contemporary AI applications while maintaining efficiency and scalability.

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. However certain limitations affect its versatility and efficiency in diverse AI workloads.

Unprecedented 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.
    • 44GB 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 needs partitioning large models across multiple chips, complicating scalability and increasing system complexity.
    • Single-User Optimization: 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, necessitating advanced cooling solutions that can elevate operational costs and infrastructure requirements.

SambaNova Cloud: for Developers & Enterprise AI

The SambaNova Cloud offers fully managed AI inference with API access, allowing developers to fine-tune and 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 SambaNova Cloud:

• 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 8B, 70B, and 405B, and DeepSeek R1 671B, 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.

Power Efficiency & Cost Considerations

SambaNova – Cost-Optimized Scalability

The SambaNova platform’s compact design, utilizing just 16 chips, reduces the datacenter footprint and associated operational costs, making it particularly suitable for large-scale AI deployments.

Key aspects contributing to its cost-optimized scalability include:

Advanced Memory Architecture for Power Efficiency

The SN40L integrates a three-tier memory system comprising:

• • • • On-Package High Bandwidth Memory (HBM): Delivers high-speed data throughput essential for handling large datasets.
      • Off-Package DDR DRAM: Offers substantial storage capacity for extensive datasets and models.
      • On-Chip Distributed SRAM: Provides rapid access to frequently used data, minimizing latency.

This hierarchical memory design ensures efficient data management, optimizing power usage by reducing the need for constant data movement, which is both time-consuming and energy-intensive.

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 exceeding 256,000 tokens, 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 inference reduces operational costs, making it a cost-effective solution for enterprises.
    • Reduced Power Consumption: The SambaNova platform only consumes between 8-15kW per rack for inference, with an average power usage of 10kW.

Cerebras – High Power, High Performance

The Cerebras Wafer-Scale Engine 3 (WSE-3) demonstrates a balance between exceptional computational speed and significant power requirements, influenced by its unique design and cooling infrastructure.

Exceptional Computational Speed with Increased Power Draw

The WSE-3 is engineered for rapid processing, featuring:

• • • 900,000 Compute Cores: This extensive core count facilitates parallel processing, enhancing computational speed.

However, this high-performance capability is accompanied by increased power consumption. The CS-3 system, which houses the WSE-3, operates at a peak sustained power of 23 kW.

Complex Liquid-Cooled Wafer-Scale Design

The WSE-3's architecture necessitates a sophisticated cooling system:

• • • Wafer-Scale Integration: The expansive chip size requires innovative packaging to effectively manage power delivery and thermal dissipation.
    • Liquid Cooling Mechanism: A closed internal water loop provides uniform cooling across the wafer, ensuring optimal operating temperatures.

This liquid-cooled design introduces complexity in system setup and maintenance, demanding specialized infrastructure to support the cooling requirements.

In summary, while the Cerebras WSE-3 delivers high performance, it has higher power consumption per chip. The liquid-cooled wafer-scale design needs a more complex setup, potentially increasing infrastructure costs and operational complexities. These are essential factors that organizations must consider when integrating this technology into their operations.

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, such as DeepSeek R1 671B delivering 250 tokens/s 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 pretrained 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 a cost-effective choice for enterprises seeking to deploy AI at scale.

The SambaNova Cloud 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.