Research and development of ternary precision and hyperbolic embedding architectures for high-throughput, drastically cost-reduced enterprise AI deployment.
Founding Engineer & Research Lead
Core Frameworks: 1.58-bit Ternary Networks & Non-Euclidean LLM Scaling
Current Large Language Models rely heavily on 16-bit decimal parameters. This architecture demands complex, floating-point matrix multiplication at every layer, driving up power consumption, inference latency, and hardware constraints.
We are engineering a foundational shift in model architecture, focusing on extreme 1.58-bit quantization and non-Euclidean data mapping. The objective is to reduce infrastructure COGS (Cost of Goods Sold) by over 70%, severing reliance on expensive hyperscaler hardware.
By forcing weights into ternary states, heavy matrix multiplication is entirely replaced by integer addition. This drastically lowers computing latency and enables highly energy-efficient throughput.
Memory limits are the largest bottleneck in AI. Ternary precision mathematically shrinks a massive foundation model's memory footprint by up to 10x, enabling massive intelligence to be hosted on mid-tier hardware.
Recent research proves that a 1.58-bit LLM matches its FP16 counterparts in both perplexity and end-task capabilities, maintaining strict coherence in coding and language tasks without suffering cognitive degradation.
Human language, code semantics, and knowledge bases are not flat; they are inherently hierarchical and tree-like. However, standard LLMs map data in flat Euclidean space. Hyperbolic space expands exponentially, perfectly matching the capacity required for complex hierarchical structures.
Standard networks lack corresponding hyperbolic neural network layers, limiting representational power. By eventually mapping our 1.58-bit quantized weights onto hyperbolic manifolds, we aim to drastically reduce the required embedding dimensions, achieving unprecedented data density in smaller parameter boundaries.
Replacing $40,000 H100 servers with mid-tier, locally hosted GPU arrays allows our inference backend to operate at a fraction of the cost of standard enterprise API providers.
Hyperbolic space allows us to cram significantly deeper domain logic into a highly constrained 1.58-bit parameter boundary, generating smarter outputs from cheaper hardware.
Removing the FP16 compute bottleneck allows robust, sovereign AI capabilities to be deployed natively without relying on foreign cloud monopolies or constrained hardware supply chains.
Distillation Validation
Executing dual-loss knowledge distillation to transfer cognitive routing from FP16 teachers into 1.58-bit ternary frameworks.
Continual Pre-Training
Securing the pipeline through a multi-billion token warmup phase to cross the absolute coherence threshold for commercial API readiness.
GPU Cluster Access
Seeking compute grants and incubation support for a multi-node distributed training run to finalize the native foundation model.