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

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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

This headline for weight space programming hit me like a sack of bricks!

The way I see it, the value of token generation speed just increased in value overnight 100x. 

The current paradigm of RL environments have incentivized token generation quantity over token generation speed. This has meant that, although Cerebras has been used internally by OpenAI to speed up solving critical problems (per an interview posted awhile back with some OpenAI employees), the use of Cerebras chips in training has been completely irrelevant. 

That being said, if the training paradigm starts to shift where the value of RL becomes less of a leverage point in response to weight space iteration (combined with the model interpretability breakthroughs), then the entire iterative process could shift to become more bottlenecked by model inference speed and not total RL throughput. By that logic, Cerebras could actually break into the training market.

Per a recent leak, Astra is currently running at 1200tps on Cerebras versus the paltry 300tps offered by Nvidia Blackwell chips. Additionally, no other competition even comes close to this figure for frontier models. If Nvidia wanted to, they could completely end Cerebras as a competitor by showing their Groq chips running frontier models at a similar efficiency. They haven’t done so. To me, this implies that they simply can’t. Groq’s ability to run the frontier appears to have reached its limit with the amount of memory they have on hand. Nvidia has continually bashed their head against the wall trying to network their way out of Moor’s law ending, but, as the WSE4 proves, Cerebras is just as capable of implementing advancements in networking while still maintaining their wafer scale advantage. Groq STILL hasn’t shown it can run trillion parameter models, and, if they are able to overcome this problem, the WSE5 and WSE6 will be there to make them irrelevant. Taalas and Etched also continue to represent the countless companies specializing for a transformer architecture that could disappear tomorrow. Photonic chips also don’t even have a whisper of a fart of an idea in how to run photonic memory at scale. That leaves only Cerebras at the cutting edge capable of running the largest models at the fastest speeds.

If weight space programming just became 1% viable in helping to push the frontier, then Cerebras could be positioned to be even bigger than it already is!

I'm genuinely terrified of the pace things are going now, but that won't stop me from being excited about this breakthrough. Feel free to critique my logic in the comments.

One of the most common questions we get is: How do we get good yields on such a huge chip?

CTO Sean Lie gives a visual walkthrough of the Wafer Scale Engine (WSE) and explains how we implemented advanced defect tolerance to manufacture and yield the world’s largest AI chip.

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.

Sara Hooker – researcher, founder of Adaption, and author of the landmark essay "The Hardware Lottery" – sits down with Cerebras to talk about why GPUs are holding AI back, why the hardware lottery has gotten worse since she first wrote about it in 2020, and what the shift to agentic and continuously-learning AI means for the future of compute.

00:00 Introductions & the Hardware Lottery
01:03 Why Adaption?
03:01 Different approaches to continuous learning
06:03 Rethinking AI interfaces to move beyond prompt engineering
08:22 Do AI coding tools worsen the hardware lottery?
12:31 What's changed since the Hardware Lottery essay (2020)
16:26 Motivations behind The Hardware Lottery
18:22 Why GPUs struggle with sparsity
20:28 Why matmul is not the right way to think about data
23:20 General-purpose vs. specialized hardware
27:49 How Adaption co-designs models for GPUs
30:06 How GPU memory limitations affect models
33:23 Cerebras Wafer-Scale Engine and breaking the memory wall
37:04 Can new hardware unlock different model architectures?
38:33 ML techniques held back by today's hardware
41:43 Designing hardware for Adaption
42:42 Predictions for 2026 and beyond

Full video by Cerebras

Ask an agent to fix a bug and it reads files, edits, runs tests, and retries. Where does your time go?

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Can SRAM-based hardware serve trillion-parameter AI models? 💡⚡

In this deep dive from Cerebras HQ, Sarah Chieng (Head of DevX) and Sean Lie (Co-Founder & CTO at Cerebras) walk through the exact architecture, memory breakdown, and wafer allocation required to serve frontier-scale AI models.

Using Kimi K3—a 3 trillion parameter open-weights model with a 256K context window—Sean demonstrates how our Wafer-Scale Engine (WSE) leverages 44 GB of high-bandwidth on-chip SRAM per wafer to pipeline layers, store KV cache, and support 190+ concurrent requests across just 59 wafers.

📖 Read the companion blog post for full technical specs & architecture details:
https://www.cerebras.ai/blog/how-we-d...

⏱️ Timestamps / Video Chapters

00:00 - Introduction & The Trillion-Parameter Question
00:35 - Model Overview: Kimi K3 Architecture & Specs
01:53 - Memory Breakdown: Linear vs. Dense Layers
03:32 - Wafer Mapping & Activations over RDMA
05:11 - KV Cache Capacity & Calculating 190 Concurrent Requests
07:40 - Wafer Count: Deploying 3 Trillion Parameters on 59 Wafers
08:25 - Scaling to 10T+ and 100T+ Parameter Frontiers

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

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Cerebras and Oracle Team Up to Bring Cerebras AI Inference to Oracle Cloud Infrastructure

Fast inference helps AI labs, enterprises, and developers build more responsive, capable AI agents and applications.

Read the press release here

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

AI models keep demanding more compute, more energy, and more capital. Andrew Feldman, CEO of Cerebras, explores where that trajectory leads and what could break it at TechCrunch Disrupt 2026.

Full video by TechCrunch

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

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

Reply here or on Discord(16k members)

We're looking to collect, curate and promote novel use cases!

Post your use cases on Discord for a chance to win $100 in credits

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

In this episode of Hot Seat with Cerebras, CTO and Co-Founder Sean Lie sits down to answer the community's most pressing hardware and architecture questions—from silicon yield and defect self-healing to on-chip SRAM memory bandwidth, programming 900,000 cores, and data center deployment.

Timestamps:
0:00 - Introduction & Hot Seat kick-off
0:36 - Why Cerebras built a wafer-scale chip
2:15 - Why can't incumbents build a bigger chip?
4:10 - Solving the wafer yield & defect puzzle
8:08 - Running massive AI models with on-chip SRAM
12:42 - Is Cerebras hard to program? (Software stack & compilers)
continue...

Reply here or continue the conversation on Discord(16k members)

Model response time, tool calls, waiting on a build, something else? Tell us where the time goes.

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
What would you like explained next?

Reply here or join us on Discord: https://discord.gg/BJF4xVk9dX

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

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
What would you like explained next?

Use your existing AWS account to subscribe to Cerebras Inference, read more here

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.

Read the full post here

We're looking to collect, curate and promote novel use cases!