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Kaihe AIBOX-G1
NVIDIA Ultimate · AI Training Machine
$5349
AIBOX-G1 features latest NVIDIA GB10 architecture, 1000 TOPS power. Built for fine-tuning, large-scale RAG, and 100B+ model inference.
GB10 Architecture · Ultimate Answer for Training & Inference
Specifications
| Chip | NVIDIA GB10 Grace Blackwell |
| AI Power | 1 PetaFLOP (FP4) |
| CPU | 20-core ARM (10x X925 + 10x A725) |
| Memory | 128GB LPDDR5x Unified Memory |
| Storage | 4TB Enterprise SSD |
| Network | 10GbE (NVIDIA ConnectX-7) |
| Wireless | Wi-Fi 7 + BT 5.3 |
| Ports | 4x USB4, 1x HDMI 2.1 |
| Cooling | Industrial Liquid Cooling |
| Power | Typical 170W TDP |
| OS | Ubuntu 22.04 LTS / DGX OS |
| Dimensions | 150x150x50.5mm |
Suitable For
Vertical LLM Privatization
National Labs
Large Enterprise AI Base
Ultimate Geek Collection
Military Simulation
Supercomputing Edge Node
Key Features
✅ Peak Performance: 1000 TOPS, supports 200B+ parameter models for local deployment & inference
✅ Local Full Training: Supports LoRA/QLoRA or full-parameter fine-tuning for private industry models
✅ Infinite Storage: 4TB enterprise NVMe SSD, supports expansion for PB-level private AI knowledge base
✅ Liquid-class Cooling: Designed for extreme power output, ensures stability during heavy model training
✅ Top Security: Hardware-level encryption & isolation for national projects or large enterprise core data
📝 4-Step Quick Start Guide
01
Plug & Power
Connect power & LAN, auto initialization on startup
02
Access Dashboard
Open browser in same LAN and visit kaihe.local
03
Bind API
Fill in cloud LLM API key, restart app to activate
04
Social Binding
One-click bind WeChat/Lark/DingTalk to interact
Frequently Asked Questions
What is Kaihe AIBOX-G1?
GB10 Architecture · Ultimate Answer for Training & Inference
How much does Kaihe AIBOX-G1 cost?
Kaihe AIBOX-G1 is priced at $5349. Enjoy unlimited local AI usage with zero token fees.
Who is Kaihe AIBOX-G1 suitable for?
Vertical LLM Privatization, National Labs, Large Enterprise AI Base, Ultimate Geek Collection, Military Simulation, Supercomputing Edge Node