Source URL: https://simonwillison.net/2024/Aug/28/cerebras-inference/#atom-everything
Source: Simon Willison’s Weblog
Title: Cerebras Inference: AI at Instant Speed
Feedly Summary: Cerebras Inference: AI at Instant Speed
New hosted API for Llama running at absurdly high speeds: “1,800 tokens per second for Llama3.1 8B and 450 tokens per second for Llama3.1 70B".
How are they running so fast? Custom hardware. Their WSE-3 is 57x physically larger than an NVIDIA H100, and has 4 trillion transistors, 900,000 cores and 44GB of memory all on one enormous chip.
Their live chat demo just returned me a response at 1,833 tokens/second. Their API currently has a waitlist.
Via Hacker News
Tags: performance, generative-ai, llama, ai, llms
AI Summary and Description: Yes
Summary: The text discusses Cerebras’ new hosted API for the Llama model, highlighting its impressive performance in AI inference powered by custom hardware. This development is particularly relevant for professionals in AI and cloud computing due to its implications for model efficiency and the future of AI deployments.
Detailed Description: This text presents significant insights into how advancements in hardware can boost the performance of AI models, specifically focusing on the speeds achieved by Cerebras’ Llama API.
– **High-Speed Inference**:
– The hosted API for Llama achieves remarkable performance with speeds of 1,800 tokens per second for Llama3.1 8B and 450 tokens per second for Llama3.1 70B. This introduces a competitive edge for applications requiring real-time processing and response.
– **Custom Hardware Architecture**:
– Cerebras’ WSE-3 chip, which is pivotal to the performance boost, is notably large, measuring 57 times the size of an NVIDIA H100. This size enables a massive transistor count—4 trillion transistors and 900,000 cores.
– The chip’s architecture allows for 44GB of memory to be integrated directly, which enhances its capability to handle extensive computations necessary for AI.
– **Live Demonstrations**:
– The practical application of this technology is demonstrated through a live chat that yielded a response rate of 1,833 tokens/second, showcasing the chip’s capabilities in real-world scenarios.
– **Market Implications**:
– The API is currently in high demand, indicated by a waitlist, emphasizing the growing interest in and need for highly efficient AI solutions in cloud computing and application development.
Overall, this text highlights how infrastructural advances can lead to transformational progress in AI performance, making it a crucial point of interest for security, compliance, and operational professionals working with AI technologies. The integration of such advanced hardware within the cloud architecture also raises considerations for efficiency and infrastructure security in AI deployments moving forward.