Bringing AI to the Edge

We focus on deploying AI models directly on consumer devices — making AI faster, private, and freely accessible to everyone.

Research

swift-qwen3-tts

On-Device Text-to-Speech

Native Swift implementation of Qwen3 TTS 0.6B for real-time, on-device speech synthesis.

  • 67% model compression (2.35 GB → 808 MB)
  • Real-time synthesis (RTF 0.68x)
  • 12 languages supported
Gemma-Prune

On-Device Vision Language Model

Multi-stage compression pipeline for deploying Gemma 3 4B VLM on consumer hardware.

  • 25% model compression (2.8 GB → 2.1 GB)
  • 110 tok/s text generation
  • 3.4x image processing speedup
OptMLX

MLX Memory Optimization Research

Exploring memory optimization techniques for the MLX framework on Apple Silicon.

  • Up to 20x faster mmap loading
  • Zero-copy model loading
  • Comprehensive benchmarks

About

AtomGradient is an independent research group dedicated to making AI run efficiently on edge devices. We believe powerful AI should be private, accessible, and free from cloud dependency. All our research is open-source.

Our research powers EchoStream AI — a product line bringing on-device AI capabilities to real-world applications.

Edge AIPrivacyOpen Research

Build with us

We're looking for researchers and engineers who want to push the boundaries of on-device AI.

View open roles