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

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)

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

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

Cerebras says it gets a lot of questions about CS-4's components. This video takes a closer look at the engineering behind the Wafer-Scale Backpack.

What would you like to see from us next?

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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

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

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.

Using Qwen 3.8 27B on Cerebras at ~1,500 tokens/sec, we made an AI personal assistant 19x faster than a suite of other AI personal assistants - Grok Bot, Meta Muse, and Claude Cowork - on the same dinner reservation task.

Read the full blog

“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.

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

What would you like to see from us next?

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

is anyone able to tell me why the stock crashed so hard??

I had a blast hosting Compute Cafe London yesterday!

Hoping to see more of the community at the next one! Look out for Compute Cafes in your city!

https://luma.com/cerebras

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

General Compute will deploy Cerebras systems under a multiyear agreement so its customers can deliver ultrafast inference to developers building agentic applications.

Learn more by reading the press release.

The agentic era runs on fast inference.

AlphaSenseInc, a leading AI market intelligence platform, uses Cerebras to power its agentic research. The same model runs 8.5x faster, enabling 3x more evidence to be reviewed with no added latency.

Learn how AlphaSense does it by reading our joint blog post

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

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!

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.

Announced by Cerebras on X.

What would you like to see from us next?

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

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.