Cerebras
@cerebras
The world's fastest AI inference and training.
Try the latest open models at:
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Pretraining teaches a model to predict. Reinforcement learning teaches it to act.
@jeffreygwang of @OpenAI explains how the two paradigms turn next-token prediction into models that can reason, use tools and complete useful tasks.
Big Chip Club with @cerebras // @alyciazcary
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Not every agent task needs a frontier model.
@Thom_Wolf (Co-founder of Hugging Face) on routing work to smaller open models for repeatable sub-tasks, evaluation, and cost control, matching compute to the value of the work.
Join us in the Token Billionaires Lounge by @cerebras and @aiDotEngineer.
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Is the GPU reaching its limits for AI? At @RaiseSummit, @cerebras makes the case for a different approach to AI chips.
Agents are only as good as the context they can access.
@jerryjliu0 on production document workflows: structure the data, retrieve broadly, then zoom in only where deeper analysis is needed.
Join us in the Token Billionaires Lounge, presented by
@cerebras and @aiDotEngineer
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Open models are being asked to do more in smaller footprints.
@thorwebdev on the tradeoffs behind open, on-device AI, and how model size, deployment location, and product goals shape what developers can build.
Join us in the Token Billionaires Lounge by @cerebras and @aiDotEngineer.
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AI should reduce workflow friction, not add another chatbot.
@sarahmsachs (Head of AI @ Notion) on moving from isolated assistance to durable agent workflows built around a shared system of record.
From the Token Billionaires Lounge, presented by @cerebras and @aiDotEngineer.
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“The codebase is part of the prompt.”
@dexhorthy on why coding loops amplify existing patterns, and why better verifiers, human review, and deliberate codebase gardening matter more as generation gets faster.
Join us in the Token Billionaires Lounge, presented by @cerebras and @aiDotEngineer.
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From training his first neural net in high school to working at OpenAI.
@jeffreygwang followed a five-year trail through Andrew Ng’s course, early GAN papers + OpenAI research.
By the time ChatGPT arrived, “it all pointed toward OpenAI.”
Presented with @cerebras // @alyciazcary
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The hard part of agent loops is not starting them!
It’s knowing when to move up a layer, and when to come back down. @swyx explains the operator skill behind useful loops: automate repeatable work, watch for failure, drop into the problem when reliability breaks, then climb back up once the system is sound.
That flexibility (not token volume for its own sake) is the key to productive loops.
Join us in the Token Billionaires Lounge, presented by
@cerebras and @aiDotEngineer
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the new age of 'personalized shopping'
I made a clone of aritzia where I'm the model on every piece of clothing
Buillt with gpt-5.6-sol standard...but just you wait for gpt-5.6-sol ultrafast (750 tok/s) running on @cerebras
coming soon 👀
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Tons of folks braved the hurricane to make it to the Gemma 4 Cafe Compute in NYC hosted by @cerebras and @datadoghq 🗽☕🌪️
Thanks everyone for making the trip to experience Gemma on Cerebras 🚀
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Winners have been dm'ed! Happy Learning 🤓
We just launched a completely free 1.5 hour course teaching the basics of inference, multi-agent workflows, and hardware.
The first 20 U.S./D.C. participants age 18+ to complete our free Deep Learning course by July 20 will receive a free 1 month Codex Pro plan.
No purchase necessary. OpenAI terms apply. While supplies last. Reply in comment with your completion certificate to qualify.
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Extremely fast multimodal inference!
Damage Scout uses Gemma 4 on @cerebras running at an impressive 2,300+ toks/s! It analyzes rental car walkaround videos and generates annotated damage reports with box coordinates in <6 seconds.
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Korea is building the future of AI—fast.
During ICML, we joined our partners at Upstage in Seoul to talk about what ultra-fast inference unlocks. Solar 31B runs at up to 2,000 tokens/sec on the Cerebras Wafer-Scale Engine.
Thanks to everyone who joined us!
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Voice AI without the wait! ⏱️
Thanks to Hugging Face and Cerebras, developers can now use the Gemma 4 31B model as the brain for voice AI at ultra-fast inference speeds. Add it to a fully open-source, cascaded speech-to-speech stack that can be used to power existing voice apps! 🗣️
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We just launched a completely free 1.5 hour course teaching the basics of inference, multi-agent workflows, and hardware.
The first 20 U.S./D.C. participants age 18+ to complete our free Deep Learning course by July 20 will receive a free 1 month Codex Pro plan.
No purchase necessary. OpenAI terms apply. While supplies last. Reply in comment with your completion certificate to qualify.
Show more
Fast inference makes a new class of real-time LLM applications possible.
In our new short course, Fast LLM Inference with Cerebras, built in partnership with @Cerebras and taught by @zhennydez, @duerr_seb, and @MilksandMatcha, you'll build them on the Wafer-Scale Engine, where a model's weights sit on-chip and tokens come out several times faster than a typical GPU setup.
You'll build a webpage that personalizes itself as users interact with it, assemble a multi-tool workflow that analyzes market signals in one response, and adopt habits for cleaner agentic coding with Codex.
Enroll for free:
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Sometimes the best DX is deleting the README.
@andrewqu (@vercel) built npx skills add so one skill could install across Claude Code, Codex, OpenCode, and more.
That tiny CLI became the flywheel for Vercel’s Skills Marketplace.
Join us in the Token Billionaires Lounge, presented by @cerebras and @aiDotEngineer
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