SambaNova vs. Groq: The AI Inference Face-Off

As the business value of AI inference becomes more tangible across industries, organizations are eager to tap into the potential for automation, personalization, and predictive insights. For enterprises and data centers already benefitting from real-time processing, understanding the advantages of each system can help future-proof their infrastructure.

SambaNova and Groq have emerged as leading contenders — each offering unique architectures, performance advantages, and specialized capabilities for high-speed artificial intelligence (AI) inference. Both companies demonstrate significantly better inference performance than commonly used GPU-based offerings, such as the H200, but it’s important to understand how they compare to each other from a performance, efficiency, and scalability perspective.

This blog explores the key differences between SambaNova and Groq, as well as specific considerations for developers and business leaders when evaluating AI inference platforms.

AI inference speed comparison

Both companies offer platforms powered by specialized AI processors. They each offer on-premises rack systems and cloud platforms — but that is where the similarities end. Groq positions its chip as an affordable alternative to traditional GPUs but fails to deliver high performance on large models and requires more and more chips to scale. 

SambaNova has designed a chips-to-model intelligence platform from the ground up specifically for the unique demands of AI applications. This holistic approach balances performance, efficiency, and scalability — ideally suited for AI inference applications.

TPS performance between SambaNova and Groq

When evaluating AI inference performance, tokens-per-second (TPS) is one of the most critical benchmarks. TPS measures the speed at which an AI system generates and processes tokens as it relates to response times, real-time applications, and overall efficiency.

Compared to traditional GPUs, Groq and SambaNova both deliver fast inference, which is one of the fundamental requirements for agentic AI applications. But to run ultra-large models or multiple models with long context lengths, efficiency of TPS per rack becomes a central consideration. While Groq requires tens of racks, consuming huge amounts of power to run each model instance, SambaNova requires only one rack running at an average of 10 kW.

Figure 1 shows the higher output speed SambaNova delivers over Groq for Meta Llama 4 Maverick.

Reasoning is being unlocked by test-time compute (TTC), which further drives the importance of token generation speed. The best models today are all reasoning models (e.g., DeepSeek-R1, OpenAI GPT-OSS 120B) generating many more tokens.

Better efficiency for bigger models

Today, models are trending to be both larger and sparser. With these new larger models, inference speeds are key to effective AI development. For example, if you compare Llama 2 in 2023 with DeepSeek-R1 today, you will find the model size has grown more than 8X from 70B to 671B. While dense models were the norm in 2023, today a fine grained Mixture of Experts (MoE) sits around 128+ experts. 

Running on an AI inference processor that can take advantage of sparse models can offer significant performance and accuracy gains even though the models are larger. If the processor cannot effectively take advantage of model sparsity, performance can be negatively impacted. 

SambaNova can run large and sparse MoE models like DeepSeek, and support many models on a single node. Groq, on the other hand, struggles with big models and requires several systems to run a single model.  

Figure 2 illustrates the enormous rack demands for Groq to run a single model that only requires one SambaNova rack.

Performance comparison running the Meta Llama 4 Maverick model

[1] Reference: https://artificialanalysis.ai/models/llama-4-maverick/providers. SambaNova number based on internal benchmarking. [2] Based on a reasonable assumption regarding how much memory needs to be available for model and data. [3] Reference:: https://semianalysis.com/2024/02/21/groq-inference-tokenomics-speed-but/

AI inference chip comparison – SambaNova SN40L vs. Groq LPU

Groq developed its own specialized processor called the Language Processing Unit (LPU). Built on a tensor streaming processor architecture, the LPU was designed to handle AI inference tasks with greater efficiency and speed than traditional GPUs. This enables Groq to support single-model, high-speed tasks, but LPU memory constraints mean that significantly more hardware is required to handle larger workloads. Groq cannot efficiently handle large models and multi-model workloads, limiting their scalability.

In contrast, SambaNova supports both high-speed and large-scale AI applications, making it a more versatile and future-proof solution. The SambaNova Reconfigurable Dataflow Unit (RDU), the SN40L, delivers high TPS while maintaining scalability and efficiency. It is designed for enterprise AI applications, allowing organizations to scale seamlessly without facing memory constraints like Groq's LPU-based architecture.

SN40L optimized for performance and efficiency

SambaNova’s fourth-generation RDU, the SN40L, is the only AI accelerator with a tightly coupled three-tier memory system. Built for large-scale AI inference, the tiers include:

• High Bandwidth Memory (HBM) 
• Double Data Rate Dynamic Random-Access Memory (DDR DRAM) 
• Large distributed on-die Static Random-Access Memory (SRAM) 

On-chip pipeline parallelism enables higher operational intensity for activations — the key to overcoming the memory wall. The RDU architecture is particularly important because it optimizes workload processing and lowers power consumption, ensuring higher efficiency. 

LPU memory constraints

Groq’s LPU is an SRAM-only architecture that focuses on linear algebra compute and simplifies the multi-chip computation paradigm. Using a programmable assembly line architecture, LPU allows the AI inference technology to use a generic, model-independent compiler for a software-first approach controlling inference steps.

While capable of delivering faster inference performance than commonly used GPUs, one of the primary drawbacks to Groq’s design is an over-reliance on expensive SRAM. With a limited amount of SRAM per chip, today’s large models simply cannot run on a single Groq system. In fact, large numbers of systems are required to power even mid-size models, resulting in large-scale deployments that are costly to acquire, difficult to manage, and power intensive to operate.  

Comparison of SambaNova and Groq chips

Data center footprint comparison

Each LPU chip has a silicon area of 725 mm2 with the workload spread across many networked chips. Running Llama 3.3 70B on the LPU at 250 tokens/sec requires hundreds of chips because each LPU has only 230 MiB of memory. They are using 576 chips for the 70B model; the compute roofline on that configuration is 432 int8 POPs that fits into nine racks.

In contrast, SambaNova's current 70B inference configuration uses just 16 SN40L chips with a combination of tensor parallelism across chips and pipeline parallelism within each chip. Each SN40L chip consists of two logic dies, SRAM, HBM, and direct-attached DDR DRAM. The 16 chips are interconnected with a peer-to-peer network and offer a compute roofline of 10.2 bf16 PFLOPS.

Figure 3 illustrates the vast difference in data center footprint between SambaNova and Groq needed to serve the same Llama 3 70B model with bleeding-edge performance.

Cloud AI offerings compared

SambaCloud – Faster AI inference on the largest models

Both SambaCloud and GroqCloud offer high performance on a wide variety of smaller models. SambaCloud delivers significantly faster AI inference on an ever-growing selection of large models. Additionally, the infrastructure required to power each company’s cloud solution is vastly different.

The real advantage of SambaCloud lies in its efficiency. By only needing one rack to achieve high performance using standard air-cooling reduces the data center footprint, lowers power consumption, and minimizes the environmental impact. This efficiency and simplicity of the SambaNova architecture gives enterprises the flexibility to move easily on-premises. With multi-model flexibility, fine-tuned checkpoint integration, and real-time AI automation, SambaCloud is an ideal solution for businesses looking to deploy AI at scale.

Features like function calling, JSON mode, and multimodal inputs with text and images establish SambaCloud as a full-stack AI solution for streaming, non-streaming, and asynchronous text generation. It also features speech-reasoning capabilities for real-time reasoning, transcriptions, and translations.

GroqCloud – Limited scalability for large models

GroqCloud is also a full-stack, cloud-based platform for developers and businesses, but he LPU-based infrastructure struggles with multi-model hosting and comprehensive AI workflow automation. As models get larger, Groq requires more and more chips to run the models, which drags down efficiency. 

Although GroqCloud offers an OpenAI-compatible API to simplify migration for developers, its poor performance on larger models and inefficiencies make it less viable for enterprise-scale AI deployments. 

Power efficiency and cost considerations

SambaNova – Cost-optimized scalability

SambaNova's dataflow architecture optimizes power usage, making it more energy-efficient for large-scale deployments. The hierarchical memory design provides an advantage in efficient data management and power usage by reducing the need for constant data movement, which is energy-intensive and time-consuming. Its compact design uses just 16 chips, reducing operational costs and data center footprint — perfect for large-scale AI deployments.

The SN40L chip balances performance and efficiency, addressing common challenges faced while scaling AI applications including:

• Massive models: Easily handles models with up to 5 trillion parameters and aids in deploying complex AI models without compromising performance.
• Enhanced sequence lengths: Capable of supporting long sequence lengths.
• Lower Total Cost of Ownership (TCO): A cost-effective solution for enterprises, SN40L is highly efficient at running Large Language Model (LLM) inference with a small footprint, reducing operational costs.
• Reduced power consumption: The SambaNova platform consumes between 8 kW to 15 kW per rack for inference tasks at an average power use of 10 kW.

Groq – Poor efficiency, higher costs

The scalability challenges of Groq and high per-chip power draw lead to higher energy consumption and operational costs, making it a less sustainable choice for long-term AI inference needs. Although a better option than NVIDIA’s H100 GPUs for AI inference, Groq lacks SambaNova’s advanced memory architecture that delivers a high standard of efficiency. The power and cooling demands of even leading-edge GPUs make them impractical for most data centers.

The Groq architecture requires more chips and rack space for larger models, increasing the data center footprint and overall power consumption. Further, while Groq does offer on-premises solutions, the massive footprint required to run models on their architecture makes this type of deployment difficult. 

The SambaNova advantage

While Groq offers an alternative to traditional GPUs, it lacks the scalability, efficiency and enterprise-readiness that SambaNova provides for AI inference. The SN40L chip supports ultra-large models like DeepSeek-R1 671B, OpenAI GPT-OSS 120B, and Llama 4 Maverick for superior power efficiency, multi-model flexibility, and workflow automation. 

Enhanced enterprise capabilities make SambaNova the best choice for organizations seeking a future-proof, scalable AI solution.