Hi Bravo.
That poster Flectional who has been showing up on the crappers BRN threads lately sure seems to think Loihi 3 is real.
He comes across as a bit of a tech head having seen him post on WBT and 4DS over the past few years.
He posted on 8/2/26 quoting something called "SapienFusion - Feb 4 - 2026" as a source.
See excerpt of that post below...............
SapienFusion - Feb 4 - 2026
Intel Loihi 3
Intel’s neuromorphic journey began in 2017 with Loihi 1, continued through Loihi 2’s 2021 release, and culminates in Loihi 3’s January 2026 commercial availability, a processor that represents the most significant architectural departure from conventional computing since GPUs themselves emerged. This isn’t an incremental improvement. This is brain-inspired computing that finally delivers on decades-old promises.
Graded Spikes: Bridging Two Worlds
'Loihi 3’s critical innovation introduces 32-bit graded spikes—a bridge between traditional deep neural networks operating on continuous values and spiking neural networks communicating through discrete events.Earlier neuromorphic generations used binary on/off signaling. A neuron either fired or didn’t. This forced algorithms designed for conventional architectures to undergo complete rewriting. Converting a PyTorch model to a binary spiking neural network required redesigning activation functions, adjusting learning algorithms, tuning temporal dynamics, and accepting accuracy degradation. The result created high barriers to adoption, and most developers stayed with GPUs.Graded spikes solve this problem by encoding information into spike amplitudes across a 32-bit range. Each spike carries nuanced information—not just fire or don’t fire, but fire with this specific intensity. This enables mainstream AI workloads to run on neuromorphic hardware with dramatically reduced power while requiring minimal algorithmic adaptation. Developers can convert existing models with automated tools currently in development, maintain accuracy within 1-2% of original performance, and achieve neuromorphic efficiency without complete redesign. This technical bridge makes commercial viability possible.'
Event-Driven Computation at Scale
'The power efficiency advantage comes from temporal sparsity—the principle that most neurons remain inactive most of the time, processing only when relevant events occur.GPU processing a video stream at 30 frames per second processes all pixels with full computation for every frame, regardless of whether the scene changes. Frame 2 might be 95% identical to Frame 1, but the GPU performs full computation anyway. Frame 3 might be 97% identical to Frame 2, but again receives full computation. The result delivers massive redundant processing, consuming constant power.'
'Loihi 3 processing the same video stream activates neurons to establish a baseline during the initial scene, then only fires 5% of neurons to detect the changes in Frame 2 when 95% remains unchanged. Frame 3 triggers only 3% of neurons, when 97% stays static. Power consumption becomes proportional to actual information content rather than frame rate.For event-driven sensory data from neuromorphic cameras and event-based audio, Loihi 3 achieves theoretical 1,000× efficiency versus GPUs. This isn’t marketing hyperbole—it’s architectural mathematics. Temporal sparsity with 99% of neurons inactive delivers a 100× reduction. Spatial sparsity through local processing without global synchronization provides a 10× reduction. Combined, these factors multiply to 1,000× efficiency. Real-world performance varies by workload, but event-based applications routinely achieve 500-1,000× improvements.'
good to know where the competition is at - dyor
could always be wrong of course - all freely available in the public domain
