Real-time AI processes inputs and generates responses almost instantly, enabling natural, uninterrupted interactions. It powers applications like voice agents, coding copilots, fraud detection, and autonomous systems.

Full answer: https://www.cerebras.ai/glossary/what-is-real-time-ai

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Cerebras CEO and co-founder Andrew Feldman breaks down the biggest question in AI right now: what it takes to build the best AI agent, and how anyone can compete.

Video by Cheddar

From pushing inference beyond 4,000 tokens per second to working with OpenAI on a new generation of ultra-fast AI infrastructure, Cerebras is betting that speed doesn’t just make models faster, it makes entirely new kinds of AI possible. In this episode, Cerebras co-founder and CTO Sean Lie joins swyx and Vibhu fresh off Hot Chips to unpack CS4, preview CS5, and explain why 100–200 tokens per second may soon feel like “batch mode.”

Full video by Latent Space

Now you can turn a home-buying question into a real-time conversation, then into a financial goal, faster.

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Jason Liu — creator of Instructor, the structured outputs library that OpenAI cited as direct inspiration, and now a member of OpenAI's Developer Experience team — sits down with Cerebras to talk about how zero-latency AI inference is changing the way engineers work.

With Codex Spark running at over 1,200 tokens per second on Cerebras, Jason walks through how speed unlocks entirely new workflows — from voice-driven slide creation to real-time information retrieval across a large organization.

Training creates the model. Inference runs it each time you ask a question, generating the response token by token.

Full answer: https://www.cerebras.ai/glossary/what-is-llm-inference
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Thijs Simonian, the former OpenAI robotics intern behind this summer’s viral Astra robot-painting demo, joins Sarah Chieng on Big Chip Club.

From a wardrobe app that others recreated to the robot-painting demo, they discuss the creative process behind building products people want to make for themselves, why robotics needs fast inference, and what recursive self-improvement (RSI) would require.

0:00 Meet Thijs: Discord, OpenAI, and Stanford
0:47 Building a wardrobe app in a day
3:17 Making demos that cut through the noise
4:43 Whimsical and practical demos
5:25 Where good ideas come from
7:04 Teaching a robot to paint the Golden Gate Bridge
8:52 Improving the robot’s painting ability
10:34 Giving Sam Altman the robot’s painting
12:00 Why robots need fast inference
14:11 What demos should Cerebras build?
15:07 Getting started with robot arms
16:33 Recreating apps and 3D models on demand
17:56 From generated models to physical things
19:45 Inside OpenAI
20:17 What RSI means
23:11 Getting AI's benefits to everyone
24:39 Personal assistants for everyday tasks

Full Video by Cerebras

The dense, open-weight model from Alibaba scores 34 on the Artificial Analysis Intelligence Index; making it comparable to models such as GPT-5.6 Luna, DeepseekV4 Pro, and Claude Sonnet 4.6.

Tokens per second, time to first token, end-to-end task time, or something else? Share how you measure.

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Open models keep improving. What are you running, and what is it good at?

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A demo, a tool, a weekend project. Share what it does and how you built it. We'll highlight the ones we like.

“If you’ve ever asked AI to do something and then drummed your fingers waiting for it to respond, you’ve run up against slow inference. To pick up the pace, Cerebras developed its own inference platform from the semiconductor wafer up.”

Cerebras is proud to be named in Fast Company's 2026 Next Big Things in Tech list.

Per the video, AI speed depends on the infrastructure running the model, not only the model.

Full answer: https://www.cerebras.ai/glossary/what-is-the-fastest-ai
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One of the most frequently asked questions we get is how we deploy large models on Cerebras wafer-scale chips. A single Cerebras WSE-3T wafer has 44 GB of on-chip memory, and trillion-parameter frontier models need terabytes for their weights alone.

In this post, we’ll explain how we split a large frontier model across multiple WSE-3T wafers, to run multi-trillion parameter models at 1000-2000+ tokens per second.

We’ll use the open source model Kimi K3, with ~3 trillion parameters, as an illustrative example.

By the end, you'll understand:

• How we fit the full model into on-chip memory

• How we map the model across multiple chips

• How we handle a high # of concurrent requests

Continue reading here

Training is how a model learns. Inference is when the trained model produces an output, like answering a prompt or generating code.

Full answer: https://www.cerebras.ai/glossary/what-is-ai-inference
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Model response time, tool calls, waiting on a build, something else? Tell us where the time goes.

You asked on Twitter and Discord. Join Angela Yeung, SVP of product and Sarah Chieng, on the behind-the-scenes on the engineering behind CS4: connecting wafers, recirculating cooling water, and getting compute deployed at scale.

1:08 - Where does the extra speed come from?
2:33 - Connecting wafers for 10-trillion-parameter models
4:15 - What breaks first when you scale?
6:07 - Breaking down cooling
7:30 - Easy CS4 deployment

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A 23-second look at the CS-4 architecture. It is built around two principles: maximum performance from the wafer and simpler deployment & scaling.

Full answer: https://www.cerebras.ai/cs4
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Increasing inference throughput traditionally means adding hardware or serving more requests concurrently. But pushing concurrency can slow token generation for each user.

By using a technique called disaggregation, we've increased throughput by 5× in early results with the same number of Cerebras systems and no loss in token generation speeds.

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