Yingqgenj AI gij Cerebras: guenghvaek ML sangvenq

Danghseiz mojhingz ML hung youq ndaw ciye, aen sonfap caeuq suzgeiq loeglaeg, training caeuq inference nanzdaiz baenz aen saeh hung: gij heiq neiyi caeuq wangzloz faex. GPU kluster lai mwh aeu seizgan yawj gij suzgeiq gvaq PCIe caeuq InfiniBand.

Viq liux gaijcied gij nanzdaiz neix, Aething Inc. guh ok aen cienci Wafer-Scale Engine (WSE), cawxbaenz Cerebras caeuq GPU cienci hung.

Gij nanzdaiz neiyi ndaw kluster GPU

Youq ndaw GPU kluster, aen neiyi faen ok lai aen kaet. Mwh gyaeuj gradient paramitw aeu all-reduce wangzloz:

GPU kluster:
[GPU 0] <--- PCIe/NVLink ---> [GPU 1] <--- InfiniBand ---> [GPU 2] <--- PCIe/NVLink ---> [GPU 3]
  ▲                                                                                         ▲
  └──────── Wangzloz hek: Inter-Node Network Serialization & Memory Wall ───────────────────┘

Wafer-Scale cienci (Cerebras WSE):
┌───────────────────────────────────────────────────────────────────────────────────────────┐
│ 900.000+ AI sim youq ndaw aen silicon wafer hung                                          │
│ 44 GB SRAM on-chip | 21 Petabytes/s neiyi heiq                                            │
└───────────────────────────────────────────────────────────────────────────────────────────┘

Mwh guh training deep model, aen wangzloz hek guh hawj FLOPS doek daengz 30-40%.

Gij ndei cienci Wafer-Scale

Gyaeuj lai bak cien aen AI core youq ndaw aen wafer ndeu:

1. Neiyi heiq hung: 21 Petabytes/s neiyi heiq youq chip, mbouj aeu DDR caeuq HBM.
2. Latensi ceiq iq: Latensi doek daengz sub-microsecond, tensor parallelism mbouj hek.
3. Linear scaling: Training scaling baenz dinghdingh, mbouj aeu pipeline yak.

Gungcwngz caeuq enterprise workflow

Aething Inc. guh aen rwed haenx:

• PyTorch caeuq JAX compiler hawj wafer core.
• Hybrid cienci: Cerebras fine-tuning + GPU inference.
• Hardware telemetry ciemx ndat caeuq dienq.

Cungjcied

Gij laux enterprise AI aeu wafer-scale hardware. Aething Inc. guh hawj ciye ciemx gij research seizgan ciuq lai laeg.