Offline AI Modules: Voice-First Offline Architecture, Hardware Reference Stack, Quantization and Benchmarking
Quick Answer
This paper shows that The Offline AI Modules project enables low-power, offline voice-first AI systems for African languages, featuring a modular architecture and a low-cost hardware stack.
Quick Take
Benchmark evaluations on NVIDIA Jetson Orin NX and Raspberry Pi5 show that Q4_K_M quantization provides optimal performance, with gemma-4-E2B-it achieving 28.8t/s decode throughput and 89.2% topic classification accuracy on TierB.
Key Points
- Modular voice-first architecture designed for unreliable internet connectivity in African communities.
- Evaluated on NVIDIA Jetson Orin NX and Raspberry Pi5 across multiple quantization formats.
- Q4_K_M quantization offers the best size-to-quality trade-off for deployment.
- gemma-4-E2B-it achieves 28.8t/s decode throughput on TierB.
- Three instruction-tuned models show effective multilingual performance across various languages.
DeepSignal Analysis
What happened
The Offline AI Modules project focuses on creating low-power, offline AI systems tailored for African languages. It features a modular architecture and a cost-effective hardware stack, with performance benchmarks conducted on NVIDIA Jetson Orin NX and Raspberry Pi5.
Key evidence
- The project includes a modular voice-first offline architecture, a low-cost hardware reference bill of materials, and a quantization and benchmarking pipeline for language models with 2-5 billion parameters.
- Benchmark evaluations indicate that the Q4_K_M quantization format provides the best size-to-quality trade-off, achieving 28.8t/s decode throughput and 89.2% topic classification accuracy on the NVIDIA Jetson Orin NX.
- All three instruction-tuned models evaluated on the Raspberry Pi5 operate within a 16GB memory budget, demonstrating their feasibility for low-power applications.
Why it matters
This initiative addresses the need for reliable AI solutions in regions with limited internet access, particularly for communities that primarily communicate through speech. By focusing on African languages, it aims to enhance accessibility and usability of AI technologies in these areas. The findings on quantization and performance metrics could influence future developments in offline AI systems.
Paper Resources
📖 Reader Mode
~2 min readAbstract:The Offline AI Modules workstream enables practical, low-power, and community-accessible deployment of voice-first AI systems that operate fully offline. Designed for African language communities where speech is the dominant mode of interaction and internet connectivity is unreliable or absent, the workstream delivers three reinforcing components: a modular voice-first offline architecture, a low-cost hardware reference bill of materials, and a reproducible quantization and a reproducible quantization and benchmarking pipeline for instruction-tuned language models in the 2-5B parameter class. This paper presents the first end-to-end benchmark evaluation of the stack across two hardware tiers: an NVIDIA Jetson Orin NX (TierB) and a Raspberry Pi5 (TierA). Three instruction-tuned models are evaluated across four quantization formats, assessed for deployment metrics (decode throughput, chat latency, memory, power) and multilingual quality (topic classification accuracy on MasakhaNEWS across English, Hausa, Igbo, Nigerian Pidgin, and Yoruba; per-language perplexity drift). Speech recognition is evaluated using Ethio-ASR on Amharic and Oromo across both tiers. The principal finding is that Q4_K_M quantization represents the best size-to-quality trade-off for deployment on both tiers: gemma-4-E2B-it achieves 28.8t/s decode throughput and 89.2% topic classification accuracy at Q4_K_M on TierB, while all three models run within the 16GB memory budget on TierA.
| Subjects: | Artificial Intelligence (cs.AI); Networking and Internet Architecture (cs.NI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2610.07026 [cs.AI] |
| (or arXiv:2610.07026v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07026 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zeinab Nezami [view email]
[v1]
Sun, 4 Oct 2026 17:16:45 UTC (643 KB)
— Originally published at arxiv.org
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