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LATEST TECH ARTICLES
Galaxy A-Series Gemma3 Pipeline Benchmark
Why This Test Matters One SoC generation changed inference speed by 29%. We ran Gemma3 270M INT8 on four Galaxy A-series devices to find out where on-device LLM becomes practically usable. We tested gemma-3-270m-it-int8 via MediaPipe CPU backend on the Galaxy A16, A26, A36, and A56, measuring latency, token throughput, memory, and accuracy across 25 prompts. We also compared parallel (all 4 devices simultaneously) vs serial (each device independently, 2 runs) execution to ver
17 hours ago
![[On-Device AI Chatbot] Part 3: Core Technologies of Mobile AI: Quantization and NPU Optimization](https://static.wixstatic.com/media/2ea07e_08ed983f9efb45fe9129e06967a91163~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_35,blur_30,enc_avif,quality_auto/2ea07e_08ed983f9efb45fe9129e06967a91163~mv2.png)
![[On-Device AI Chatbot] Part 3: Core Technologies of Mobile AI: Quantization and NPU Optimization](https://static.wixstatic.com/media/2ea07e_08ed983f9efb45fe9129e06967a91163~mv2.png/v1/fill/w_992,h_559,fp_0.50_0.50,q_95,enc_avif,quality_auto/2ea07e_08ed983f9efb45fe9129e06967a91163~mv2.png)
[On-Device AI Chatbot] Part 3: Core Technologies of Mobile AI: Quantization and NPU Optimization
Core Technologies of Mobile AI Quantization and NPU Optimization In Part 2, we discussed our selection of Gemma-2B as the ideal Small Language Model (SLM) for our project and shared our experiences benchmarking CPU and GPU performance in a constrained smartphone environment. However, the initial tests revealed significant challenges: noticeable latency delays and out-of-memory errors. To run LLMs in real-time on a mobile device held in the palm of your hand—not on a data ce
Feb 18
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