On-Device AI Engineering (Gemma 4 E2B / E4B)

Deploy production-grade AI directly on your hardware — fully offline, private, and cost-free to run.

I embed advanced AI into phones, IoT devices, Raspberry Pi clusters, and NVIDIA Jetson systems using Google Gemma 4 edge models — enabling real-time, multimodal intelligence without cloud dependency.

  • No API costs
  • No data leaving the device
  • No latency from cloud calls

Just fast, private, on-device AI.

What This Means for Your Product

  • AI runs entirely offline
  • Supports text, image, audio, and video
  • Works in low-bandwidth or air-gapped environments
  • Suitable for privacy-sensitive industries (GDPR / HIPAA)
  • Enables AI-native hardware products

Why Gemma 4 Edge Models

E2B

  • Efficient runtime (~2B effective parameters)
  • Fits within <1.5 GB RAM
  • Ideal for embedded and constrained devices

E4B

  • Higher reasoning capability
  • Better for complex multimodal tasks

Shared Capabilities

  • 128K context window
  • Up to 30s audio + 60s video processing
  • Apache 2.0 license (no licensing cost)
  • Single unified multimodal model

Technical Depth — What I Actually Do

Architecture (PLE Efficiency)

Gemma 4 uses Per-Layer Embeddings (PLE) — runtime compute behaves like a smaller model while retaining larger weight capacity.
I design systems based on effective compute cost, not just parameter size.

Correct Model Handling

Multimodal inference requires:

  • AutoModelForMultimodalLM (not image-text classes)
  • Correct processor pipelines per modality
  • Audio resampling at 16 kHz

Getting this wrong leads to silent failures — I handle it correctly from the start.

Attention & Memory Optimisation

  • Hybrid sliding window + global attention
  • Unified KV cache strategy

Critical for:

  • Running 128K context on constrained edge devices
  • Managing memory vs latency trade-offs

Thinking Mode (Advanced Prompting)

  • <|think|> token enables reasoning mode
  • Thought traces must be stripped before reuse

Improper handling reduces multi-turn performance — I design pipelines that avoid this.

Fine-Tuning (QLoRA)

  • 4-bit NF4 base + bfloat16 adapters
  • Target modules: q_proj, v_proj
  • Rank tuning (16–64 depending on task)

Supports multimodal training pipelines using TRL.

Deployment Optimisation

  • LiteRT-LM (Android, <1.5GB RAM)
  • WebGPU (browser deployment)
  • Quantisation:
    • q4_K_M → speed
    • q8_0 → accuracy
    • bf16 → benchmarking

Engagement Options

1. Strategy & Architecture (1–2 Weeks)

  • Use-case validation
  • E2B vs E4B selection
  • Hardware scoping (Pi, Jetson, Edge SoCs)
  • Memory + KV cache planning

2. Prototype / PoC (2–4 Weeks)

  • Working multimodal system on your device
  • Correct pipelines (text, image, audio)
  • Performance benchmarking

3. Deployment & Optimisation (2–4 Weeks)

  • QLoRA fine-tuning
  • Quantisation & optimisation
  • LiteRT-LM / WebGPU deployment
  • CI pipeline + production handover

Industries & Use Cases

Healthcare

  • Voice-to-report systems
  • On-device patient summarisation
  • Fully private (no data leaves device)

Manufacturing

  • Visual defect detection (Jetson)
  • Offline quality inspection systems

Retail & Logistics

  • Receipt OCR (offline)
  • Voice-based inventory lookup
  • Works in warehouses without connectivity

Consumer Devices

  • Always-on voice assistants
  • Smart cameras with local AI
  • Embedded AI products

Supported Hardware

  • Raspberry Pi (standalone & clusters)
  • NVIDIA Jetson (Nano, Xavier, Orin)
  • ARM-based embedded systems
  • Edge AI SoCs
  • Browser (WebGPU deployment)

Why Work With Me

I don’t apply generic LLM knowledge to edge systems.

I understand how Gemma 4 actually works at runtime:

  • PLE architecture
  • Hybrid attention
  • KV cache constraints
  • Multimodal model class differences

And I translate that into real hardware performance.

What You Get

  • Production-ready, documented code
  • Reproducible benchmarks
  • Clear architecture decisions
  • Weekly progress demos
  • Fully remote, async delivery

Technical Stack

Core

  • transformers ≥ 5.5.0
  • accelerate · librosa

Fine-tuning

  • TRL · PEFT / LoRA

Deployment

  • Android (LiteRT-LM)
  • Raspberry Pi · Jetson
  • Browser (WebGPU)

Quantisation

  • q4_K_M · q8_0 · bf16
  • NF4 (QLoRA base)
  • GGUF (llama.cpp)

Book a free 20-minute scoping call

Book a free 20-minute scoping call
→ Get exact model + hardware recommendation
→ Clear deployment path
→ No guesswork

← Back

Thank you for your response. ✨

Time(required)