import { useState } from "react"; import { useNavigate } from "react-router-dom"; import { Button } from "@/components/ui/button"; import { Badge } from "@/components/ui/badge"; import { Card, CardContent } from "@/components/ui/card"; import { Menu } from "lucide-react"; export function Technology() { const [activeTab, setActiveTab] = useState("Hardware"); const navigate = useNavigate(); return (
{/* Navigation */} {/* Header Section */}
{/* Title */}

Our Technology

Low-cost FPGA-based design, optimized for Large Language Models on-device.

{/* Tabs */}
{["Hardware", "Software"].map((tab) => ( ))}
{/* Tab Content */}
{activeTab === "Hardware" && (
{/* Hardware Text Content */}

Hardware Description

EdgeMate is an ultra–low-budget single-board computer designed specifically for large language model (LLM) deployment. Despite its compact form factor and low power consumption, we provide:

  • Accelerated inference performance — delivering up to 15 tokens per second on a 30B model, thanks to its optimized FPGA-based AI engine.
  • High memory capacity — up to 40GB RAM, enabling hosting of LLMs up to ≥30B parameters.
  • Cost efficiency — making advanced AI workloads accessible at low cost.
{/* Hardware Specifications Card */}
Chip AMD Zynq UltraScale+ XCZU3EG
Processor
Quad-core 64-bit Arm Cortex-A53 CPU
Dual-core 64-bit Arm Cortex-R5 CPU
ARM Mali-400 GPU
FPGA Fabric 70K LUT, 360 DSP slices
Memory
8 GB 64-bit DDR4 (2400 Mbps) on CPU side
8 GB / 16 GB / 32 GB DDR4 (2133 Mbps) on FPGA side (SODIMM)
Storage
256 GB PCIe 2.0 x1 NVMe SSD
MicroSD card slot
I/O
USB 2.0 / 3.0 via Type-C
Mini DisplayPort
)} {activeTab === "Software" && (
{/* Software Text Content */}

Software Stack

Our comprehensive software platform enables non-experts to personalize, test, and deploy LLM agents to edge devices with an intuitive web-based interface:

  • React 18 + TypeScript frontend with shadcn/ui design system for modern, accessible components.
  • FastAPI + Python 3.11+ backend with hybrid inference routing between local FPGA and cloud APIs.
  • Integrated RAG system using LangChain, FAISS, and HuggingFace Transformers for document processing.
  • Docker containerization with deployment on Hugging Face Spaces for accessible demonstrations.
{/* Software Features Card */}
Frontend Stack
React 18 + TypeScript
shadcn/ui + Radix UI
Tailwind CSS + Vite
Backend Stack
FastAPI + Python 3.11+
HuggingFace Transformers
OpenAI-compatible APIs
AI/ML Components
LangChain + FAISS
sentence-transformers
Quantization Support
Deployment
Docker Containerization
Hugging Face Spaces
REST API Integration
Development Tools
Python SDK
Model Optimization Tools
Performance Profiler
Security
Secure Boot
Model Encryption
Access Control
)}
{/* Why us? Comparison Section */}

Why us?

{/* Comparison Table */}
{/* Table Header */}
Ours
Raspberry Pi 5
Jetson Orin Nano
{/* Table Rows */} {[ { label: "Price", ours: "$199", pi: "$120", jetson: "$249" }, { label: "RAM", ours: "24GB/40GB", pi: "16GB", jetson: "8GB" }, { label: "CPU", ours: "Cortex-A53", pi: "Cortex-A72", jetson: "Cortex-A78", }, { label: "AI Engine", ours: "Optimized Accelerator on FPGA", pi: "Neon SIMD Instructions", jetson: "Cuda/Tensor Core", }, { label: "Power", ours: "<10W", pi: "5-12 W", jetson: "7-25" }, { label: "LLM decode Performance", ours: "15 tokens/s", pi: "<5 tokens/s", jetson: "15 tokens/s", }, ].map((row, index) => (
{row.label}
{row.ours}
{row.pi}
{row.jetson}
))}
{/* Device Layout Section */}

The device layout plan

Device Layout Plan { console.error("Chips image failed to load"); e.currentTarget.style.display = "none"; }} />

Need help? Contact us to get customized device and services

); }