Specifications
1.6T Modules
DR8 and 2xFR4
800G Modules
DR4, FR4 and LR4
Signaling
200G/lane single-lambda PAM4 (100 GBd EML)
Optical Laser
200G PAM4 EML lasers, O-band
CWDM Wavelengths
1271 / 1291 / 1311 / 1331 nm
LWDM Wavelengths
1295.5 / 1300.0 / 1304.5 / 1309.1 nm
Form Factors
OSFP and QSFP-DD
Cable Types
Optical modules, AOC active optical cable, DAC/ACC copper
PIC Options
EML, Silicon Photonics and InP PIC variants for 1.6T DR8
Switch Platforms
Enables 51.2T and 102.4T switch platforms for accelerated AI compute
Demo
800G OSFP112 live switch-traffic demo; 800G LPO/LRO
Additional Portfolio
10/25G/100G tunable and 25/50G PON transceivers
Market Position
Ranked 9th globally among optical transceiver makers (2023); 3rd/4th for 400G shipments
Vendor
Source Photonics, West Hills, California
Applications
Hyperscale and AI datacenter scale-out, GPU cluster fabric, DCI
Overview
Source Photonics' 1.6T and 800G PAM4 transceiver families use single-lambda 200G PAM4 EML technology to deliver high-bandwidth, low-latency links for AI and hyperscale data centers. The 1.6T series spans DR8 and 2xFR4 modules plus DAC/ACC copper, while the 800G series covers DR4, FR4 and LR4 in OSFP or QSFP-DD form factors with multiple connector and interface options.
The modules were demonstrated live at OFC with an 800G OSFP112 switch-traffic demo and 800G LPO/LRO variants, positioning the family as the interconnect building block for next-generation 51.2T and 102.4T switch platforms that feed accelerated AI compute clusters.
Key Benefits
200G/lane single-lambda PAM4 doubles per-lane bandwidth for 1.6T and 800G links. Multiple PIC and laser options (EML, SiPh, InP) give design flexibility. Full cable ecosystem including AOC and DAC/ACC simplifies AI fabric deployment. Proven 400G heritage with top-tier shipment rankings.
Applications
AI and hyperscale data center fabric, GPU cluster scale-out, 51.2T/102.4T switch platforms, DCI, and 800G/1.6T leaf-spine interconnect.
Request a Quote — SOURCE PHOTONICS 1.6T AND 800G PAM4 TRANSCEIVERS — 200G/LANE SINGLE-LAMBDA MODULES FOR AI
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