TL;DR
The Cerebras CS-3 outperforms Groq’s LPU-based solution across almost all key metrics, delivering ~6x higher inference speeds on frontier LLMs, enabling more generation in the same amount of time, with higher accuracy and lower power consumption – at similar cost. With Cerebras, developers can build the fastest and most intelligent conversational AI, real-time code generation, instant reasoning, and agentic applications.
Performance: Advantage Cerebras
Today’s large language models are bottlenecked by slow GPU inference. With complex reasoning and agentic models, for example, it can take 20–30 minutes to generate an answer. The simple reason: low effective memory bandwidth. LLM generation is limited by how fast you can move model weights from memory to compute for each token.
Cerebras and Groq both achieve faster LLM inference than Nvidia GPUs by addressing this memory bandwidth bottleneck. Cerebras’ wafer-scale engine stores the entire model in on-chip SRAM with ultra-high bandwidth (~21 PB/s), so tokens are generated without shuttling weights back and forth from external HBM, which slows GPUs.
Groq streams models through a dataflow pipeline across many lightweight cores with very low control overhead. While the Groq LPU architecture is a step up from slow GPU performance, it is in distant second place to Cerebras. This is fundamentally due to Cerebras’ wafer-scale architecture, which leverages a 21+ Petabytes of SRAM next to compute and effectively enabling pipelined parallelism for extremely low inference latency, at a much larger and more efficient scale than Groq.
Independent benchmarks by Artificial Analysis shows Cerebras >6x Groq on identical models: oss-gpt-120B at ~3,000 tokens/s vs ~493 tokens/s; Llama 4 Maverick and Llama 3.3 70B at >2,500 tokens/s vs ~497 tokens/s and ~403 tokens/s on Groq, respectively. Groq does not serve large parameter models from Alibaba’s popular Qwen model family, so we are unable to compare performance there.
The Cerebras performance advantage is also clear when looking at end-to-end response time, a measure of the time it takes from prompt until the user gets an answer. As seen in the below chart, when looking at those same three popular models, Cerebras is up to 5x faster than Groq.
Price-Performance: Advantage Cerebras
Cerebras is also the clear leader in price-performance, delivering up to a 6x price-performance advantage over Groq. In the below chart, Cerebras has a large performance advantage, and is priced similarly to Groq or up to ~50% higher, assuming a 3:1 input : output token ratio.
In aggregate, higher per-device throughput yields better total cost efficiency at scale; Groq often needs many LPUs in parallel for large models, reducing both performance and cost efficiency..
Energy Efficiency: Advantage Cerebras
At ~27 kW and ~125 PFLOPS, CS-3 achieves about 3x higher compute-per-watt than an 8-GPU DGX. Keeping weights in on-chip SRAM minimizes off-chip traffic, lowering joules per token and improving efficiency under real workloads.
By contrast, a Groq LPU draws ~375 W, and while it is more efficient than GPU, large models require many LPUs networked together, pushing rack-level power into the hundreds of kW and eroding efficiency at scale relative to Cerebras.
Bottom line: CS-3 delivers higher energy efficiency with fewer devices, yielding lower energy per token than multi-chip Groq clusters.
Accuracy: Advantage Cerebras
Groq is optimized to run 8-bit models but not 16-bit models fully-native in hardware, which is why full 16-bit bit precision models run significantly slower on their architecture. Therefore, most models running on Groq trade off accuracy for speed through quantization down to 8-bit precision. By contrast, Cerebras supports 16-bit precision natively in hardware, ensuring maximum response speed and accuracy for use cases that need it.
Cerebras also deploys more large frontier models than Groq, delivering better accuracy, and Cerebras’ higher output speed enables running more chain-of-thought or reasoning iterations in the same wall-clock time, which improves response accuracy for complex tasks.
For the most up to date list of supported models, visit these pages:
https://console.groq.com/docs/models
https://inference-docs.cerebras.ai/models/overview
Training/Optimization: Advantage Cerebras
Cerebras supports both training and inference, whereas Groq’s LPU is an inference-only engine, giving Cerebras the advantage when it comes to model development and/or customization, ultimately delivering better results and getting more out of the infrastructure.
Reliability & Scale: Advantage Cerebras
Given that each Groq chip only has 230MB of SRAM, hundreds of chips are needed to support a 70B parameter model and thousands for a 400B parameter model, resulting in complex clusters using Groq’s proprietary networking fabric to stitch all the LPUs together. This setup reduces efficiency and introduces numerous potential points of failure.
Cerebras is engineered for enterprise-grade uptime and easy scaling, with a single device able to serve much larger models than the Groq LPU and seamless, near-limitless scalability, with fewer points of failure and simpler maintenance.
In practice, customers report that Groq throttles long before hitting its stated limits, leading to performance inconsistencies. Cerebras provides predictable, high-performance inference without slowdowns, as demonstrated by 99%+ uptime on its transparent system status tracker (status.cerebras.ai).
Availability: Advantage Cerebras
The CS-3 is broadly available today. Customers can deploy Cerebras on-premises or access it via multiple clouds and services (Meta, Vercel, Hugging Face, OpenRouter, etc.).
Groq’s LPU is available on-prem with GroqRack systems, through GroqCloud, and Hugging Face.
While both companies offer cloud access and enterprise support, Cerebras currently has a wider set of deployment options and partners, giving it an edge in availability.
Ease of Use: Even
Both Cerebras and Groq emphasize developer-friendly interfaces. Each provides OpenAI-compatible APIs and pre-built endpoints for popular models. In practice, integrating either platform involves only a few code changes (e.g. switching endpoints or API keys), and top models are pre-optimized for both. As a result, ease of adoption is similar on both: users report spinning up tests in under a minute on either system with minimal configuration. The learning curve and toolchains are comparable, so neither has a strong advantage here.
Data Privacy: Even
Cerebras and Groq both offer on-premises deployments as well as cloud regions in trusted geographies. Both support enterprise security standards and can enforce zero-data-retention policies per customer requirements. In short, organizations handling sensitive data will find similar privacy guarantees on Cerebras and Groq platforms. Neither system forces data into third-party clouds without control, so data privacy considerations do not distinguish them.
Learn more about each company’s privacy policy at trust.cerebras.ai and trust.groq.ai.
Ecosystem: Even
Compared to incumbent NVIDIA, both Cerebras and Groq have smaller, but faster growing ecosystems. Both providers have ~20 key ecosystem integrations and counting. On a case-by-case basis, one provider or the other may have an advantage, depending on specific customer requirements, but both are at a similar ecosystem maturity stage and are expanding their partnerships.
To see if your preferred ecosystem partner is integrated with either Cerebras or Groq, see the complete integrations list for both companies here:
https://inference-docs.cerebras.ai/resources/integrations
https://console.groq.com/docs/integrations
Conclusion: Advantage Cerebras
The Cerebras CS-3 outperforms Groq’s LPU-based solution across almost all key metrics, delivering ~6x higher inference speeds on frontier LLMs, enabling more generation in the same amount of time, with higher accuracy and lower power consumption – at similar cost. Only Cerebras offers customers the ability to train or optimize models. The Cerebras wafer-scale architecture also yields significant reliability and scaling efficiencies compared to Groq’s many-chip approach, and is more broadly available through multiple providers. Both providers score evenly when it comes to ease of use, data privacy, and ecosystem support. Net-net, Cerebras enables the fastest and most intelligent conversational AI, real-time code generation, instant reasoning, and agentic applications.