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Cerebras Launches World’s Fastest deep R1 inference
SUNNYVALE — Cerebras Systems,a pioneer in accelerating generative AI,announced record-breaking performance for Deep-R1-Distill-Llama-70B inference,achieving more than 1,500 tokens per second – 57 times faster than GPU-based solutions. This unprecedented speed enables instant reasoning capabilities for one of the industry’s most sophisticated open-weight models, running entirely on …Source
Cerebras News: Cerebras Launches World’s Fastest Deep R1 Distill …
cerebras has partnered with deep to enhance AI inference capabilities,leveraging its CS-2 systems and Wafer-Scale Engine (WSE) technology to accelerate Deep’s large language models. This collaboration aims to optimize model training and deployment efficiency, offering a scalable option to customary GPU-based infrastructure.
Cerebras launches world’s fastest Deep R1 Distill Llama 70B …
Cerebras Systems, the pioneer in accelerating generative AI, has announced a record-breaking performance for Deep-R1-Distill-Llama-70B inference, achieving more than 1,500 tokens per second - 57 times faster than GPU-bas… CEO Andrew Feldman.AI computer pioneer Cerebras systems has been “crushed” with demand to run Deep’s R1 large language model, says company co-founder and CEO Andrew Feldman.
“We are thinking about how to meet the demand; it’s big,” Feldman told me in an interview via Zoom last week.
Deep R1 is heralded by some as a watershed moment for artificial intelligence because the cost of pre-training the model can be as little as one-tenth that of dominant models such as OpenAI’s GPTo1 while having results as good or better.
The impact of Deep on the economics of AI is notable, Feldman indicated. But the more profound result is that it will spur even larger AI systems.
Also: Perplexity lets you try Deep R1 without the security risk but it’s still censored.Source
Cerebras Accelerates Deep Inference with Unprecedented Speed
In the rapidly evolving landscape of artificial intelligence, the demand for compute power is skyrocketing. Numerous AI cloud services have rushed to offer Deep inference, including industry giants like Amazon’s AWS and innovative firms such as Cerebras. Though, Cerebras stands out with its remarkable speed, achieving output 57 times faster than other Deep service providers.According to Cerebras Systems, running inference on their CS-3 computers significantly outperforms other Deep service providers. In a comparative demo, a reasoning problem solved by Deep on Cerebras’s machine took just 1.5 seconds, whereas the same task on OpenAI’s o1 mini required a full 22 seconds.”This speed can’t be achieved with any number of GPUs,” stated Cerebras Systems.
The challenge with hosting Deep lies in its high computational demands during inference.Models like Deep, including OpenAI’s GPT-4, require multiple inference passes through all parameters for each word of input, consuming substantial compute resources. “A basic GPT model does one inference pass through all the parameters for every word” of input at the prompt, explained Cerebras Systems. “These reasoning models, or chain-of-thought models, do that many times” for each word, “and so they use a great deal more compute at inference time.”
Cerebras tackled this challenge by following a standard procedure for companies wanting to run Deep inference: downloading the R1 neural parameters (or weights) from Hugging Face and using them to train a smaller open-source model, in this case, Meta Platforms’s Llama 70B, to create a “distillation” of R1. “we were able to do that extremely quickly, and we were able to produce results that are just plain faster than everybody else — not by a little bit, by a lot,” said Cerebras Systems.
Key Points Comparison
| Feature | Cerebras Systems | Other Deep Providers |
|—————————|————————–|—————————-|
| Speed | 57x faster | Standard speed |
| Inference Time | 1.5 seconds | 22 seconds |
| Compute Resource usage | efficient | High |
| Model distillation | Llama 70B | None |
Conclusion
Cerebras’s innovative approach to AI inference has set a new benchmark in the industry. By optimizing compute resources and achieving unparalleled speed, Cerebras has demonstrated that it is possible to deliver timely results even for the most demanding AI models. This breakthrough not only enhances the user experience but also opens new possibilities for the future of AI.
For those interested in experiencing Cerebras’s cutting-edge inference service, you can try it here.
Further Reading
For an in-depth look at Deep’s capabilities, check out this article: I tested Deep’s R1 and V3 coding skills - and we’re not all doomed (yet).Certainly! Here’s a cleaned-up and organized version of the text:
A decade ago, Cerebras started its public inference service last August, demonstrating speeds much faster than most other providers for running generative AI. It claims to be “the world’s fastest AI inference provider.”
Aside from the distilled Llama model, Cerebras is not currently offering the full R1 in inference because doing so is cost-prohibitive for most customers.
“A 671-billion-parameter model is an expensive model to run,” says Feldman, referring to the full R1.”What we saw with Llama 405B was a huge amount of interest at the 70B node and much less at the 405B node because it was way more expensive.That’s where the market is right now.”
Cerebras does have some customers who pay for the full Llama 405B as “they find the added accuracy worth the added cost,” he said.
Cerebras is also betting that privacy and security are features it can use to its advantage.The initial enthusiasm for Deep was followed by numerous reports of concerns with the model’s handling of data.
“If you use their app, your data goes to China,” said Feldman of the Android and iOS native apps from Deep AI. “If you use us, the data is hosted in the US, we don’t store your weights or any of your information, all that stays in the US.”
Also:
Apple researchers reveal the secret sauce behind Deep AI
Additionally, there have been numerous security vulnerabilities that researchers have publicized.
Revolutionizing AI: Deep R1 and the race for Speed
In the rapidly evolving landscape of artificial intelligence, one name has been making waves: Deep R1. This cutting-edge large language model (LLM) has sparked both excitement and concern within the tech community. experts like Feldman have offered philosophical insights, noting that while the technology is advancing at an unprecedented pace, it is not yet perfect.
“Nobody’s seen anything like it,” Feldman remarked. “This industry is moving so fast. It’s getting better week over week, month over month.But is it perfect? No. Should you use an LLM to replace your common sense? You should not.”
security Concerns Emerge
Adding to the intrigue, a recent discovery by a security firm has shed light on potential vulnerabilities. According to a report, Deep R1 has “direct links” to Chinese goverment servers. This revelation has raised eyebrows and sparked conversations about data security and international AI regulations.
Cerebras’ Leap Forward
while the world grapples with the implications of Deep R1, another significant development has emerged. Cerebras, a leading AI hardware company, announced last Thursday that it has added support for running Le chat, an inference prompt developed by french AI startup Mistral. This move is part of a broader effort to enhance AI performance and efficiency.
Speed and Efficiency
One of the standout features of le Chat is its “Flash Answers” capability, which operates at a staggering 1,100 tokens per second. According to Cerebras, this makes Le Chat “10 times faster than popular models such as ChatGPT 4o, Sonnet 3.5, and Deep R1.” This remarkable speed positions Le Chat as the world’s fastest AI assistant,setting a new benchmark for the industry.
Comparative Analysis
To better understand the implications of these advancements, let’s break down the key features and performance metrics of these AI models in a comparative table:
| Model | Speed (Tokens per Second) | Key Features |
|———————|————————–|—————————————————|
| ChatGPT 4o | Not specified | General-purpose AI with broad capabilities |
| Sonnet 3.5 | Not specified | Known for robust natural language processing |
| Deep R1 | Not specified | Advanced language model with potential security concerns |
| Le Chat (Flash Answers) | 1,100 | Inference prompt with exceptional speed and efficiency |
The Future of AI
As AI continues to evolve, so to will the challenges and opportunities it presents. While models like Deep R1 and Le Chat push the boundaries of what is absolutely possible, they also highlight the need for vigilant oversight and ethical consideration. the race for faster, more efficient AI is on, and the world is watching.Stay tuned for more updates on the rapidly changing landscape of artificial intelligence. For now, it’s clear that the future is here, and it’s moving at lightning speed.
