AI

Offline AI Modules: Voice-First Offline Architecture, Hardware Reference Stack, Quantization and Benchmarking

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Briefings

This briefing reports on the Offline AI Modules workstream, which enables low-power, voice-first AI systems for African communities with unreliable internet. The study evaluates three instruction-tuned models (Gemma-4-E2B-it, Llama-3-8B-it, and Mistral-7B-it) across two hardware tiers: NVIDIA Jetson Orin NX and Raspberry Pi5. The principal finding is that Q4_K_M quantization offers the optimal balance between size and quality, achieving 28.8t/s decode throughput and 89.2% topic classification accuracy on the Jetson Orin NX, while all models fit within the 16GB memory budget on the Raspberry Pi5.

Key points

• The Offline AI Modules workstream delivers a modular architecture, low-cost hardware reference stack, and reproducible quantization/benchmarking pipeline for voice-first AI systems.
• Three instruction-tuned models (Gemma-4-E2B-it, Llama-3-8B-it, Mistral-7B-it) were evaluated across four quantization formats on two hardware tiers: NVIDIA Jetson Orin NX and Raspberry Pi5.

Context

This research addresses the critical need for practical, low-power AI deployment in African regions where internet connectivity is unreliable or absent, prioritizing speech as the dominant interaction mode.

Limitations

Evaluation is limited to two hardware tiers (NVIDIA Jetson Orin NX and Raspberry Pi5) and three specific instruction-tuned models. Performance metrics include decode throughput, chat latency, memory, and power, while multilingual quality is assessed via topic classification accuracy on specific languages.

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