CPU-first — the vGPU virtualization thesis
RTX 5090-class performance from a Ryzen 7 is not a benchmark, it is a business model.
The GPU tax
A single H100 costs the same as five years of a full-stack engineer's salary in Kathmandu. Deploying frontier AI at national scale on that cost curve is a non-starter for every emerging economy.
SILL — the vGPU runtime
SILL virtualizes GPU-class compute from commodity CPU cores using AVX-512/AVX2/NEON SIMD, INT4 quantization, sparse attention, speculative decoding, and kernel-level scheduling. The result: RTX-5090-class token throughput on a Ryzen 7.
Why the cost curve is the strategic axis
Sovereignty is worthless if only rich nations can afford it. SILL flips the cost curve. That is the pragmatic path to sovereign SI for the ninety percent of the world that cannot buy a GPU cluster.
Why we use different words
Where the industry says LLM, we say CLLM. Where it says vector database, we say NEXUS. Where it says Chain-of-Thought, we say PRISM proof-tree. Where it says RAG, we say LATTICE. Where it says trillion-token dataset, we say Smart Router Dataset. Different words because different architecture. Read the whitepaper.