CHIPS
Regular
Something fascinating is happening inside the world’s data centers.
For more than a decade, GPUs have powered the AI revolution.
But as models grow to trillions of parameters and power costs surge, we’re hitting physical limits — and the race is on to reimagine what computing hardware should look like.
What’s emerging is nothing short of a quiet revolution — new architectures that might finally challenge the GPU’s monopoly on AI compute.
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Cerebras Systems
Building wafer-scale engines — chips the size of dinner plates that can train massive models without the pain of GPU clusters. One chip, one model, no fragmentation.
Groq
Taking a radical approach with a deterministic Language Processing Unit (LPU) designed for ultra-low-latency inference. Already powering sovereign AI projects in the Middle East.
Graphcore & SambaNova
Re-architecting dataflow itself — bringing compute and memory closer together for huge efficiency gains in model training.
Lightmatter
Computing with light instead of electricity.
Its photonic chips use optical interconnects to link processors at fiber-optic speeds — a potential game-changer for hyperscale AI clusters.
BrainChip
Taking inspiration from biology, with neuromorphic chips that mimic spiking neurons to run AI at milliwatt power levels — perfect for edge AI.
Etched
Going all-in on specialization — a transformer-only ASIC that, if its claims hold, could replace hundreds of GPUs for a fraction of the cost.
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Each of these players is betting on a different principle —
wafer-scale integration, deterministic execution, optical communication, or brain-like computation —
but they share one goal: break free from the GPU bottleneck.
It’s becoming clear that the future of AI compute will be heterogeneous.
GPUs will stay the workhorses, but they’ll soon be joined by optical, neuromorphic, and specialized accelerators — turning tomorrow’s data centers into orchestras of silicon.
New AI computing architectures challenge GPU dominance | Apurv Nigam posted on the topic | LinkedIn
Something fascinating is happening inside the world’s data centers. For more than a decade, GPUs have powered the AI revolution. But as models grow to trillions of parameters and power costs surge, we’re hitting physical limits — and the race is on to reimagine what computing hardware should...
