Home ﹥ Hot News > Artificial Intelligence > Computing > XRM Stack Overview (XRM-HUB + EOS Bridge + XRM-SSD) 2026-02-07

Dollarchip Technology Inc. (Taiwan-based patent tech R&D & Transfer Company)

Problem Solved & Core Technical Differentiation
The XRM stack addresses key bottlenecks in modern AI infrastructure:

• Compute fragmentation & high inference/training costs — XRM-HUB aggregates diverse resources (ASICs, GPUs, CPUs) into a unified, intelligently scheduled platform with LPCC compression (9x–15x cognitive state reduction, 89% memory savings) and dedicated ASIC acceleration for AI workloads.
  
  
• Inefficient LLM inference & lack of specialized hardware compatibility — EOS Bridge provides an execution environment bridge optimized for Etched Sohu ASIC, delivering unified ISA (42% less data movement), deterministic latency, hardware sparse support (INT4), and significant gains: ~48% faster inference vs NVIDIA H100, 35% better training efficiency, 64% energy savings, and 44% 3-year TCO reduction (~$541 per 1M tokens vs ~$975 on NVIDIA).
  
  
• Massive data movement, PCIe bottlenecks, energy waste, & ransomware vulnerability — XRM-SSD introduces cognitive storage with AI reasoning directly in the SSD/Flash controller, reducing data transfers by up to 99%, enabling on-SSD filtering/inference (only Top-10 results sent), millisecond ransomware detection via Shannon Entropy monitoring (instant read-only lock), and 50%+ edge efficiency gains.
  
  

Core differentiation: A full end-to-end stack (compute orchestration → specialized inference bridge → intelligent storage) that minimizes data movement, unifies heterogeneous hardware, and embeds cognition/security at the storage layer — all while targeting compatibility with emerging ASICs like Etched Sohu.

Current Stage All three components are at MVP / proof-of-concept demonstration level:

• Built as interactive demos/pitch sites on Manus.space.
• Performance numbers are based on simulations (e.g., QEMU emulation, Tachyum Prodigy simulator, ideal conditions, MLPerf-style benchmarks).
• XRM-SSD v0.3.1 includes testing lab, SMART analysis, live hardware viz, and Gen5/UALink synergy simulation. No public evidence of pilot users, production deployments, real silicon tape-out, or large-scale customer deployments yet.

Target Customers / Partners Primary focus: Strategic partnerships, licensing, or acquisition by large AI players, especially those building or using specialized inference hardware.

• Etched — Direct compatibility via EOS Bridge for Sohu ecosystem.
• NVIDIA — As a potential complement/optimizer for hybrid GPU + ASIC + storage stacks to reduce TCO and improve efficiency.
• OpenAI — For cost reduction in large-scale inference/training and edge/on-prem RAG deployments. Secondary: Telecom operators (compute monetization + TCO savings), cloud data centers, edge/IoT (smart cities, factories), regulated industries needing compliance/security (WORM, ransomware protection).

Revenue Model & Go-to-Market Plan Dollarchip's core business is patent technology R&D consulting + customized patent transfer / licensing.

• Revenue streams:
• IP / patent licensing or exclusive transfer (full stack bundle).
• Custom development + milestone payments + royalties (e.g., per SoC/SSD shipped).
• Hardware premium: 30–50% markup on XRM-Ready SSDs.
• Annual subscription for features (e.g., ransomware protection).
  
  
• Go-to-market:
• Pitch demos via manus.space sites + company website (dollarchip.com.tw).
• Direct outreach to strategic players (NVIDIA, OpenAI, Etched, telecoms) for PoC validation → partnership / acquisition discussions.
• Focus on patent transfer deals rather than building own production/sales channels.
  Contact: polo@dollarchip.com.tw (primary), may@dollarchip.com.tw,
               philipp@dollarchip.com.tw.

This positions the XRM stack as an early-stage, high-potential IP bundle for AI infrastructure optimization, particularly appealing to companies seeking cost/performance edges in inference and edge computing.