Zettascale

Compute-in-memory (CIM) AI memory chips and inference racks that let data centers support more users and longer context at the same power.

Develops server systems that use quantum computing to accelerate AI training and inference

Series A · $210M · 2026-04

Analog in-memory computing AI inference chips based on RRAM memristors (NPU chips and IP) for low-power edge and endpoint AI devices.

R&D of general-purpose cloud AI chips (GPU/XPU), providing compute for LLM training and inference.

Cloud AI training and inference chips, AI accelerator cards and modules, intelligent computing systems and clusters, AI computing and programming software platforms

IPO · $1.3B · 2026-09

3D compute-in-memory (3D-CIM) AI inference chips

Series C · $293M · 2026-10

General-purpose reconfigurable RISC-V processor for physical AI (sensing, AI inference and real-time control on a single chip)

Seed · $18M · 2026-10

Photonic AI inference chip (DX-1/optical tensor processing unit) that uses SRAM plus photonic interconnects to bypass the HBM bottleneck, with accompanying optical networking and rack-scale systems

Series B · $532M · 2026-08 · Valuation $3.3B

Compute-in-memory AI inference chips and complete systems for data centers

Series B · $236M · 2026-05

Programmable ASICs and rack-scale systems for foundation model inference, using processor-memory co-design to cut energy use

Series A · 2026-09

Designs the AI inference chip Rebel100 along with companion servers and full racks, for telecom operators and data centers to run agent inference

Pre-IPO · $898M · 2026-03 · Valuation $2.3B

Develops near-memory AI inference chips and CXL computational memory products to reduce AI inference cost and power consumption

Series B · $185M · 2026-05 · Valuation $570M