Cerebras delivers the world’s fastest AI infrastructure
TL;DR
The Cerebras CS-3 system is 21x faster, 1/3 lower cost, and 1/3 lower power than Nvidia’s flagship DGX B200 Blackwell GPU—making previously impractical use cases a reality, including 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 keeps that traffic on-chip, with dramatically higher memory bandwidth than a GPU’s “high-bandwidth” memory (HBM) and GPU interconnect, so both output speed and end-to-end latency are significantly faster.
In a third-party report by SemiAnalysis (Source), a competitive analysis was performed on the Nvidia B200 and AMD MI325X. Using the data from this article, we benchmarked the Cerebras CS-3 at over 21x faster inference than Nvidia’s flagship Blackwell B200 GPU, running the Llama 3 70B model in a reasoning scenario with 1024 input and 4096 output segment lengths.
The 21x Cerebras inference performance advantage over the Nvidia Blackwell B200 GPU is based on end-to-end (E2E) latency, calculated as the sum of time-to-first-token + (output sequence length x time between output tokens). This metric better approximates real-world wait times because it incorporates all sources of latency when processing requests.
Benchmarks showing a similar inference performance advantage for Cerebras CS-3 over the Nvidia Blackwell B200 GPU are also available for two other models: OpenAI’s gpt-oss-120B and Meta’s Llama 4 Maverick. In independent benchmarks by Artificial Analysis, Cerebras CS-3 achieved 2,700+ tokens/second on gpt-oss-120B versus only 900 tokens/second on Nvidia’s Blackwell B200 GPU, and only 580 tokens/second when processing 10 parallel requests (see below chart). Similarly, for Meta’s Llama 4 Maverick, Cerebras achieves 2,500+ tokens/second, versus only 1,000 tokens/second on Nvidia’s Blackwell B200 GPU (see below chart).
The Cerebras inference performance advantage is expected to be even larger in real-world scenarios, since Cerebras performance measurements are running in production, whereas the Nvidia Blackwell B200 GPU figures are a benchmark-optimized configuration where throughput is not disclosed.
Since the Blackwell B200 GPU is not broadly available, we also benchmarked Cerebras output speed against today’s fastest GPU-based providers of models from OpenAI (oss-gpt model family), Meta (Llama model family), and Alibaba (Qwen model family). Cerebras inference performance towers above today’s fastest Nvidia GPU (see below chart)—a performance lead that carries forward against B200 for at least three models that have been benchmarked, and very likely for more models to come.
To further corroborate these performance benchmarks, real-time end-user inference performance for Cerebras and Nvidia is available by looking at live throughput measurements (i.e., output speed in tokens/second) on OpenRouter.ai, a leading multi-vendor LLM API provider.
Price-Performance: Advantage Cerebras
A common misconception is that a wafer-scale chip must be more costly than a cluster of GPUs due to wafer yield challenges and the sophisticated technology required to power and cool such a large microprocessor. In practice, the Cerebras wafer-scale engine delivers significantly better price-performance than even Nvidia’s flagship Blackwell DGX B200 GPU.
Using the independent benchmark from SemiAnalysis introduced in the performance section, Cerebras CS-3 is 32% lower cost than Nvidia’s flagship Blackwell B200 GPU, and delivers results 21x faster. This results in a massive price-performance advantage for Cerebras CS-3 over the Nvidia Blackwell B200 GPU.
The Cerebras price-performance leadership vs Nvidia GPU also translates to lower end customer pricing to serve frontier AI models, with Cerebras per token pricing being lower than the top proprietary model LLM API providers including Anthropic, OpenAI, and Google Gemini.
Power Efficiency: Advantage Cerebras
Power consumption is increasingly important, given the demand for power-hungry AI compute and increasingly scarce power sources. With growing focus on environmentally friendly and renewable power, reducing power per unit of work becomes even more critical.
The Cerebras wafer-scale engine delivers significantly lower power consumption for the same work than Nvidia’s flagship Blackwell DGX B200 GPU, at lower cost and significantly higher speed.
Using the independent benchmark from SemiAnalysis introduced earlier, Cerebras CS-3 is 32% lower cost than Nvidia’s flagship Blackwell B200 GPU and delivers results 21x faster—a major price-performance advantage that also translates to lower energy per token.
Accuracy: Advantage Cerebras
Model accuracy (or model “intelligence”) is the ability to generate a correct answer to a user prompt and is therefore critical for any AI application.
Both Cerebras and Nvidia serve frontier AI models—which have the highest accuracy scores on independent benchmarks—and do so at full bit precision. These include models from OpenAI, Meta, Alibaba, DeepSeek and more.
However, Cerebras has the accuracy advantage in the age of reasoning, where models are repeatedly called in a chain-of-thought process to generate more accurate responses. With a 21x performance advantage, Cerebras can reason or think 21x more for a fixed response time than the Nvidia Blackwell B200 GPU.
AlphaSense, a leading financial intelligence platform, demonstrated that they could process 100x more documents in half the time of today’s fastest GPU (read more), resulting in not only better accuracy but also a 50% faster response for stronger user engagement.
Model Optimization: Advantage Cerebras
If you want to further optimize a model with your proprietary dataset—or go a step further and pre-train your own state-of-the-art model—you can do so on both the Cerebras CS-3 and Nvidia Blackwell B200 GPU. Both companies also have experts ready to help accelerate your learning curve and time-to-trained model.
However, the advantage goes to Cerebras for training, with up to 10x faster time-to-train as a result of simplified scaling. GPU scaling is slow and costly, with dozens of engineers, complex networking, tens of thousands of lines of distributed systems code, and multiple bottlenecks. Cerebras enables easy scaling from 1 to 1,000 wafers with supercomputer performance and the programmability of a single device.
There are many customer testimonials attesting to significant training speedups on Cerebras compared to Nvidia. Two examples:
- “The training speedup afforded by the Cerebras system enabled us to explore architecture variations, tokenization schemes, and hyperparameter settings in a way that would have been prohibitively time- and resource-intensive on a typical GPU cluster.” — Kim Branson, SVP and Head of AI, GlaxoSmithKline
- “Training which historically took over two weeks to run on a large cluster of GPUs was accomplished in just over two days on a single Cerebras system. This allows us to iterate more frequently and get much more accurate answers, orders of magnitude faster.” — Nick Brown, Head of AI, AstraZeneca
Reliability & Scale: Advantage Cerebras
Being able to deploy models at scale with a reliable provider is fundamental for today’s AI applications. Cerebras and Nvidia providers have proven the ability to deploy these technologies reliably at scale, both being battle-tested by leading hyperscalers and hundreds of enterprises, with trillions of tokens served and 99%+ uptime.
However, the advantage goes to Cerebras for easier scaling afforded by the wafer-scale architecture, which can scale up to 24T parameters on a single logical device with no distributed “glue” code required and with an order of magnitude less networking.
By contrast, with Nvidia it can take tens of thousands of lines of distributed systems code to make a cluster operational and requires thousands of networking connections to stitch together countless GPUs in a cluster.
Availability: Advantage Cerebras
The Cerebras CS-3 is broadly available today for deployment on-premises and through shared or private cloud deployments, including providers like Meta, Vercel, HuggingFace, OpenRouter, and more.
Nvidia’s DGX B200 Blackwell GPU is starting to become available through hyperscalers and leading OEMs, although supply is constrained, so there is queuing and uneven availability by region.
Ease of Use: Even
It’s easy to get started with either platform in under 30 seconds by changing a few lines of code to point to an endpoint and choose a model, because both Cerebras and Nvidia have OpenAI API–compatible stacks that are served by leading LLM providers.
Data Privacy: Even
Both Cerebras and Nvidia technologies are offered on-premises and/or through providers in trusted geographies with robust security and zero data retention policies, so this key consideration is even.
Ecosystem Integrations: Advantage Nvidia
The modern AI era was built from the ground up on Nvidia GPUs, so it is not surprising that Nvidia’s ecosystem support dwarfs all competitors, with the broadest set of models, tool integrations, providers, and developer support.
As of today, Nvidia has the advantage, but the Cerebras ecosystem is quickly closing the gap, with fast-growing support for top open models, tool integrations, providers, and developer support (see below chart).
Conclusion: Advantage Cerebras
The Cerebras CS-3 delivers clear advantages over the Nvidia DGX B200 Blackwell GPU across nearly every dimension. It provides 21x faster inference, lower cost, and lower effective energy use, while also improving accuracy in reasoning tasks and offering 10x faster training through simplified scaling. Its wafer-scale architecture eliminates the need for complex distributed systems, making trillion-parameter models feasible on a single logical device.
Both platforms achieve enterprise-grade reliability, ease of use, and strong data privacy, while Nvidia maintains the lead in ecosystem breadth. However, for organizations seeking the lowest latency, highest throughput, and simplest scaling path to frontier AI, Cerebras stands out as the stronger choice. A hybrid approach—leveraging Nvidia where ecosystem support is essential and Cerebras where performance, cost-efficiency, and scale matter most—will maximize flexibility and value.