The Challenge
The Edge AI Challenge
Current AI solutions weren't designed for the constraints of edge computing.
Models Too Large
Existing LLMs are bloated for edge devices, demanding gigabytes of memory and powerful GPUs that simply aren't available on constrained hardware.
Cloud Dependency
Relying on cloud-based AI introduces unacceptable latency, recurring costs, and serious privacy concerns for sensitive on-device data.
Integration Complexity
Deploying AI on constrained hardware means fighting incompatible runtimes, manual quantization, and fragmented toolchains across chip architectures.
The Solution
EdgeLingo: Intelligence at the Edge
EdgeLingo delivers production-ready language models that run natively on edge hardware. No cloud roundtrips, no privacy trade-offs — just fast, intelligent AI exactly where your users need it.
- 40%+ faster inference vs. TensorFlow Lite
- Works on Snapdragon, M-series, and ARM chips
- Open-source core with enterprise support
- Sub-10ms latency on modern devices
import { EdgeLingo } from '@edgelingo/sdk';
const model = await EdgeLingo.load('el-mini-7b', {
device: 'auto', // Snapdragon, M-series, ARM
quantization: 'q4' // 4-bit for max speed
});
const result = await model.generate({
prompt: 'Translate to Spanish: Hello world',
maxTokens: 64,
temperature: 0.3
});
console.log(result.text);
// → "Hola mundo"
// Latency: 8ms | Memory: 1.2GBWho It's For
Built for Device Manufacturers
From smartphones to autonomous vehicles, EdgeLingo powers intelligent experiences across every device category.
Smartphones
On-device AI assistants, text prediction, voice commands, and real-time translation — all without a network connection.
IoT Devices
Smart home hubs, industrial sensors, and wearables powered by local intelligence — faster responses, lower bandwidth, total privacy.
Automotive
Advanced driver-assistance systems, in-car voice interfaces, and autonomous driving support running entirely on-vehicle.
Testimonials
Loved by Teams Everywhere
Engineers and teams around the world trust EdgeLingo to power their edge AI workloads.
“EdgeLingo cut our on-device inference time by 60%. We migrated three production models in a single sprint and haven't looked back since. The SDK is remarkably well-designed.”
Sarah Chen
VP of Engineering, NovaMobile
“We evaluated every edge-AI framework on the market. EdgeLingo was the only one that actually delivered sub-10ms latency on our Snapdragon chipsets without manual quantization work.”
Marcus Rivera
Lead ML Engineer, AutoDrive Systems
“Our IoT fleet runs 12,000 sensors and EdgeLingo handles local inference on every single one. We went from cloud-dependent to fully offline in under two months.”
Anika Patel
CTO, SensorFlow
“The open-source core gave us confidence to adopt, and the Pro tier's custom training pipeline paid for itself within the first quarter. Support team is world-class.”
James Okoro
Director of AI Products, PixelForge
Pricing
Simple, Transparent Pricing
Start with our open-source core and scale to enterprise when you're ready.