Neural Compute Architecture // Gen-V
Cestus utilizes a custom-baked 5090 kernel, allowing for latency-free weights processing. No overhead. No bottlenecks.
By offloading tensor operations to the Cestus local cluster, training cycles are reduced from days to microseconds.
Parallelized bus architecture enabling 128GB of HBM3e shared memory. Seamless model swapping without VRAM flushing.
Architecture visualized through pure vector coordinate mapping.
[ NEURAL NETWORK ACTIVE ]
1000+ nodes synchronized. Sharded model weights across decentralized infrastructure. Zero downtime.
Sub-millisecond API responses. GPU clusters auto-scaling based on demand. Production-ready AI.
Neural architecture search running 24/7. Hyperparameter tuning via genetic algorithms. Self-improving models.
Zero-knowledge proofs for model verification. Federated learning preserves data privacy. Military-grade encryption.
Elastic compute grid. Automatic shard rebalancing. From 10 to 10,000 nodes in seconds.
Optimizing Neural Paths
Optimizing latent space manifold geometry for zero-latency weights processing. Bridging the gap between silicon and neural networks through pure vector coordinate mapping.
Real-time processing with optimized latent space manifold geometry.
Superposed compute states across a sapphire crystalline lattice.
Zero-latency photonic data transfer at 400Gbps per channel.