Throwback to last year's hackathon with @cline where 1000+ hackers signed up to play around with the latest model on Cerebras, among them @archimagos who wrote about his experience. Missed out? Join us tomorrow on Discord for our GLM 4.7 hackathon, with $5000 and Cerebras Code
@cerebras
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Cerebras Scaling Law: Faster Inference Enables Smarter AI
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Read more https://
cerebras.ai/blog/the-cereb
ras-scaling-law-faster-inference-is-smarter-ai
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Cerebras Speed Converts to Intelligence Through Test-Time Compute
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If you think Cerebras is just about speed, you do not understand Cerebras. Just as mass can be converted to energy, speed can be converted to intelligence. It's the natural consequence of test-time compute scaling.
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Speed Boosts Intelligence: Cerebras Scaling Law Insights
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speed => intelligence https://
cerebras.ai/blog/the-cereb
ras-scaling-law-faster-inference-is-smarter-ai
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OpenAI Partners with Cerebras for Advanced AI Computing
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OpenAICerebras https://
openai.com/index/cerebras
-partnership/
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AI-Powered Fact Checker Combats Misinformation Online
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72% of people don't trust the internet. AI generated false information is polluting even trusted sources. It's impossible to sort through what is real. With this cookbook, you can build a fact checker that scans web-pages and verifies every claim. All at the speed of light,
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Cerebras Software Optimizations Enable 20x Faster LLM Inference
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Everyone talks about our hardware @Cerebras. Few notice the software.
— Cerebras (@cerebras) 12 janvier 2026
Ryan Loney breaks down the hidden optimizations powering 20× faster LLM inference than GPUs, speculative decoding, token reuse, and why we’re just getting started.
Watch the full story here pic.twitter.com/JFZz7Y5SubEveryone talks about our hardware @Cerebras
. Few notice the software. Ryan Loney breaks down the hidden optimizations powering 20× faster LLM inference than GPUs, speculative decoding, token reuse, and why we’re just getting started. Watch the full story here -
Model Quality Focus Before Wide Deployment Strategy
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focus and utilization – we want to get one model at the best quality of service before going wide
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Open Router Deployment and Prompt Caching Soon
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yes soon on open router.
heard on prompt caching -
Wafer Memory Optimization for LLM Inference Parallelization
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lots of wafers = lots of mem for kv cache
multiple users at a time overlapping the pipeline