More in our blog: https://
cerebras.ai/blog/blackwell
-vs-cerebras
…
COMPUTING
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Blackwell versus Cerebras: AI Hardware Processor Comparison Analysis
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Accelerate GPU Training in Colab with TPU Runtime and steps_per_execution
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If you're using Colab and you feel like training your model on GPU is slow, switch to the TPU runtime and tune the "steps_per_execution" parameter in model.compile() (higher = more work being done on device before moving back to host RAM) Can often see a 4-5x speedup.
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Groq Expands AI Infrastructure in Kazakhstan with LPU Deployment
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Kazakhstan is building serious AI infrastructure.
We’ve signed an MOU to deploy Groq LPUs at massive scale in new high-capacity data centers and support thousands of local developers already building on Groq. -
E-fuse Limitation Impact on BF16 to FP32 Accumulation Speed
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I think this is the e-fuse issue isn't it, which is artificially limiting the bf16->fp32 accum speed? So strictly speaking "same chip" is correct, if a bit misleading?…
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Cool Algorithm Trick for AI IoT and Digital Transformation
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Cool Algorithm Trick ✨@InterestingSTEM #AI #IoT #5G #DigitalTransformation #AI #IoT #CES2026
— Harold Sinnott #MWC26 (@HaroldSinnott) 6 novembre 2025
pic.twitter.com/BcUuevLP9FCool Algorithm Trick @InterestingSTEM #AI #IoT #5G #DigitalTransformation #AI #IoT #CES2026
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Kimi K2 Thinking: Breakthrough Open Source Model with 1T Parameters
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Las cifras reportadas por el nuevo Kimi K2 Thinking son un barbaridad para un modelo open source! Además cuantizado a INT4 para mayor velocidad de inferencia y arquitectura tipo MoE. Eso sí, 1T de parámetros con 32B activos, no apto para mucho de nuestros equipos
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Smarter Spaces Hackathon: Edge AI Project Opportunities
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Got an idea for an edge AI project? We’re kicking off Smarter Spaces soon; a new #hackathon in the Axelera Community for builders and makers working with Metis and @orangepixunlong
. Share your project idea and you could get access to free hardware, support, and visibility -
Jamba Reasoning 3B: Lightest Model Running on Just 2.25 GiB RAM
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How much RAM do you need to run tiny models? Jamba Reasoning 3B runs on just 2.25 GiB, the lightest among small models like Qwen (
@Alibaba_Cloud
), Llama (
@Meta
), Granite (
@IBM
), and Gemma (
@GoogleDeepMind
). Try Jamba Reasoning 3B yourself: https://
huggingface.co/collections/ai
21labs/jamba-reasoning-3b
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IoT and Cyber-Physical Systems Transform Predictive Maintenance
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By merging IoT connectivity with cyber-physical systems, maintenance shifts toward predictive models that reduce downtime, cut costs, improve efficiency, stabilize quality, and guide strategies with reliable data for sustainable long-term operations. Microblog by @antgrasso
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Young Programmer Learning p5js, Scratch, and Python Skills
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My daughter does a *lot* of p5js (many hours every week), and still kinda likes scratch. She's been doing a bit of python in Solveit. She hasn't been into the robotics stuff for a few years now — guess she takes after me in liking software. 🙂