SpikingBrain 1.0, developed by the Chinese Academy of Sciences, is a neuromorphic AI model that mimics brain neurons.
It achieves up to 26.5× faster first-token generation and 100× speed on ultra-long tasks while using only 2% of traditional training data.
By firing neurons selectively instead of applying global attention, it reaches linear scaling.
Strategically, it runs entirely on Chinese MetaX chips, avoiding NVIDIA dependence and aiming to democratize AI access.
I compared it with Akida 2.0 and found that SpikingBrain is stronger for long texts, but Akida is significantly better in all other areas and, above all, significantly more energy-efficient.
Nevertheless, I find it exciting that China is pursuing an alternative to NVIDIA GPUs and isn't aiming for a copy of traditional GPUs, but rather relies on spiking technology.
🧠 'Brain-Like' AI Breakthrough: 100x Faster Than Traditional Systems The SpikingBrain 1.0 from the Chinese Academy of Sciences isn't your typical LLM upgrade. It's a neuromorphic AI model that mimics how actual brain neurons fire - and it's 26.5x faster on first-token generation for...
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