Tensordyne targets AI inference market with logarithmic math and Juniper-derived rack architecture
The race to serve AI inference faster and cheaper is exposing the hard limits of conventional chip architecture. As demand for real-time AI responses accelerates, the industry's standard response - stacking more high-bandwidth memory onto power-hungry silicon - is running into a wall, and logarithmic math may be the foundational rethink that breaks through it. ] The post Tensordyne targets AI inference market with logarithmic math and Juniper-derived rack architecture appeared first on SiliconANGLE.
Key Takeaways
- Discover how utilizing logarithmic math under the hood eliminates multiplier circuits to power low latency enterprise AI inference deployment.
UPDATED 18:47 EDT / JULY 08 2026 AI Tensordyne targets AI inference market with logarithmic math and Juniper-derived rack architecture by Kelly Knight The race to serve AI inference faster and cheaper is exposing the hard limits of conventional chip architecture.
- "From a user point of view, from an SDK point of view, you don't even notice it.
- in the AI inference infrastructure market.
) Logarithmic math eliminates multiplier circuits to shrink power and footprint The key insight behind Tensordyne's approach is eliminating the most expensive circuits on an AI chip.
- "If you want to multiply two numbers, suddenly what you do is you only add the two exponents of the two numbers.
That transformation from a logarithmic representation to a normal one is normally so expensive, but we found a way to do this extremely accurately and still very cheap.
- " Here's the complete video interview, part of SiliconANGLE's and theCUBE's coverage of the RAISE Summit : (* Disclosure: TheCUBE is a paid media partner for the RAISE Summit event.
Stats & Key Facts
- #That density means four pods fit in a single standard rack, enabling customers to serve the largest frontier models at more than 1,000 tokens per second per user without requiring multiple racks or a secondary high-speed networking provider, Backhus said.
Discover how utilizing logarithmic math under the hood eliminates multiplier circuits to power low latency enterprise AI inference deployment. UPDATED 18:47 EDT / JULY 08 2026 AI Tensordyne targets AI inference market with logarithmic math and Juniper-derived rack architecture by Kelly Knight The race to serve AI inference faster and cheaper is exposing the hard limits of conventional chip architecture. As demand for real-time AI responses accelerates , the industry's standard response - stacking more high-bandwidth memory onto power-hungry silicon - is running into a wall, and logarithmic math may be the foundational rethink that breaks through it.
Fresh out of stealth and with its first chip now in production at Taiwan Semiconductor Manufacturing Co. is positioning itself to challenge the AI inference market by rearchitecting the math inside the silicon - not just the chip itself - according to Gilles Backhus (pictured), co-founder of Tensordyne. "Our logarithmic math - it's completely under the hood," Backhus said.
" Backhus spoke with theCUBE's John Furrier at the RAISE Summit , during an exclusive broadcast on theCUBE, SiliconANGLE Media's livestreaming studio. They discussed how Tensordyne's cross-continental co-design model, logarithmic math innovation and Juniper Networks-derived rack architecture are positioning the company as a direct challenger to Nvidia Corp. in the AI inference infrastructure market.
For more details please read the original article at SiliconANGLE AI.
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