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Tensordyne vs RigBase

A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.

Tensordyne compared with RigBase
DetailTensordyne logo TensordyneRigBase logo RigBase
What it doesNext-generation AI inference systems for speed and efficiency Local LLM hardware benchmarks with measured tokens per second from real home AI rigs
StageStartup Not given
Founded2019 Not given
Based inUS Not given
Pricing modelNot given Free
PricingNot given Not given
Key features
  • Logarithmic math architecture for reduced computation
  • Multi-trillion parameter mixture of experts support
  • Real-time 4K video generation at 30FPS
  • Air-cooled design eliminating liquid cooling needs
  • Scale-up interconnect for linear model scaling
  • 608 PFLOPS of dense compute per rack
  • PyTorch, Triton, and vLLM software support
  • 16-bit precision for reduced hallucinations
  • 172 real home AI builds with measured performance data
  • Performance metrics for 31 current LLM models
  • Search by GPU, VRAM, budget and target model
  • VRAM and KV cache calculator
  • All results linked to original public sources
  • Comparison guides and hardware recommendations
What makes it different
  • Energy efficiency through proprietary logarithmic mathematics
  • Fastest inference performance available at lowest cost
  • Full-stack co-design from math to silicon to systems
  • Air-cooled form factor fitting standard datacenters
  • Proven silicon with customer validation
  • Real measured performance data from 172 actual home AI rigs, not marketing claims
  • Every benchmark result linked to its original public source for verification
  • Open data under Creative Commons license
  • Practical guides for choosing AI hardware by budget and model
Community votes00
DR (Domain Rating by Ahrefs)42 no change since last checkNot checked yet no change since last check
Trust Flow (Majestic)180
Citation Flow (Majestic)3620
Referring domains (Majestic)3772

3 details filled in for both companies.

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