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sid sheth

T2 · manager / operator

30-year semiconductor veteran; started career at Intel, previously built a $3B business at Inphi (sold to Marvell). Founded D-Matrix in 2019 to build inference-specific accelerators; recently raised $275M at a $2B valuation targeting enterprise AI workloads.

2 calls·2 names·100% bull·last heard 10 months ago·The Information
track record

no scored calls yet — needs a stated position or a categorical verdict, with a matured window vs SPY

top calls

highest conviction · one per company
1sthigh conviction
$DMATRIXDmatrix

Dmatrix CEO claims inference chip beats Nvidia on efficiency

Dmatrix's inference-optimized chip delivers 10x speed, 3x lower cost and 3-5x better energy efficiency than GPUs by avoiding high-end process nodes and HBM memory, targeting enterprise sub-100B parameter models where Nvidia is less optimized.

The Information2025-11episode →
2ndmedium conviction
$D-MATRIXD-Matrix

D-Matrix raises $275M at $2B to challenge Nvidia on inference efficiency

D-Matrix targets inference workloads for enterprise smaller models (<100B params) with a dedicated accelerator architecture delivering 10x speed, 3x cost and energy efficiency versus GPUs, while avoiding supply-constrained HBM and advanced packaging by using older process nodes.

The Information2025-11episode →

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  • $DMATRIX
  • $D-MATRIX

recurring themes

  • AI Hardware & Chip Architecture1
2 total
$DMATRIX
Dmatrix
HIGHsid sheth·The Information·10 months ago·NVIDIA Challenger on Why Inference is the Next Multi-Trillion Dollar AI Market
Dmatrix CEO claims inference chip beats Nvidia on efficiency
Dmatrix's inference-optimized chip delivers 10x speed, 3x lower cost and 3-5x better energy efficiency than GPUs by avoiding high-end process nodes and HBM memory, targeting enterprise sub-100B parameter models where Nvidia is less optimized.
"the 10x uh you know faster 3x you know energy efficient you know 3x cost efficient"
2:11
$D-MATRIX
D-Matrix
MEDsid sheth·The Information·10 months ago·Former Twitter CEO Building AI Web Infrastructure, Waymo’s Freeway Expansion | Nov 14, 2025
D-Matrix raises $275M at $2B to challenge Nvidia on inference efficiency
D-Matrix targets inference workloads for enterprise smaller models (<100B params) with a dedicated accelerator architecture delivering 10x speed, 3x cost and energy efficiency versus GPUs, while avoiding supply-constrained HBM and advanced packaging by using older process nodes.
"Datrix is focused on inference, right? So we don't focus on training which is the other piece of AI computing which you know Nvidia GPUs are extremely good at. ... The whole premi…"
22:23
8
AI Hardware & Chip Architecturetailwind
Inference-specific accelerators can beat GPUs on efficiency by avoiding supply-chain bottlenecks
Dedicated inference chips using mature process nodes and commodity memory can achieve order-of-magnitude efficiency gains for enterprise-scale models (<100B params) because architectural specialization outweighs process-node leadership, while sidestepping HBM and CoWoS shortages that constrain GPU scaling.