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EDSFF 4C to PCIE GEN 5 M.2 SSD Quad Port Adapter
EDSFF 4C to PCIE GEN 5 M.2 SSD Quad Port Adapter
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| Part # | 4C-2449-M2 |
| Bar Code (UPC) | 611851928445 |
| Specifications | ‌Electrical Characteristics PCIe Generation: Gen 5 (32 GT/s per lane) Signal Integrity: Supports PCIe Gen 5 re-timing or re-driving circuitry (may include onboard redriver/retimer ICs such as Texas Instruments DS160PR series or equivalent) Power Input: 3.3V and 12V via EDSFF 4C backplane connector Per-M.2 Slot Power Delivery: Up to 25W per slot (configurable via SMBus/I²C power management) Total Adapter Power Envelope: ~100W maximum under full load ESD Protection: Per PCIe CEM and EDSFF specification requirements Mechanical Specifications ParameterSpecificationPCB Layers8–12 layer, impedance-controlledEDSFF ConnectorMolex/Amphenol EDSFF 4C edge connectorM.2 ConnectorStandard M-Key x4 PCIe, right-angle or vertical mountOperating Temperature0°C to 70°C (commercial); -5°C to 85°C (industrial variant)Storage Temperature-40°C to 105°CHumidity5% to 95% non-condensingDimensionsPer EDSFF E3.S 1T or E1.L mechanical envelope (SNIA EDSFF spec) Management & Ancillary Interfaces SMBus / I²C: Onboard power sequencing, thermal monitoring, and VPD (Vital Product Data) EEPROM access PCIe Bifurcation: Host platform must support x16 bifurcated as 4×4; no onboard PCIe switch required (direct connect topology) LED Indicators: Per-slot NVMe activity and fault indicators, routed to backplane LED connector Thermal Management: Passive heatspreader or provision for active cooling via EDSFF chassis airflow; no onboard fan SGPIO / UART: Optional sideband for drive status signaling to enclosure management controller (EMC) Compliance & Standards SNIA EDSFF Specification (SFF-TA-1007 / SFF-TA-1008) PCIe Base Specification 5.0 NVMe Base Specification 2.0 M.2 (NGFF) Specification, Revision 3.0 RoHS 2 / REACH compliant UL 94V-0 PCB material Typical Use Cases Hyperscale / Cloud Storage: Dense NVMe-oF (NVMe over Fabrics) storage nodes All-Flash Arrays (AFA): High-throughput, low-latency primary storage AI/ML Training Infrastructure: Fast dataset ingestion requiring sustained sequential bandwidth Edge Data Centers: High-density compute nodes with EDSFF-based storage bays |