ENTERPRISE AI ANALYSIS
Large Language Models on Mobile Devices: A Measurement Study of Single- and Multi-Instance Execution
This report distills key insights from cutting-edge research on LLM performance on mobile devices, providing actionable intelligence for your enterprise AI strategy.
Executive Impact & Key Findings
This study comprehensively evaluates Large Language Model (LLM) inference on mobile devices, comparing single- and multi-instance execution using popular engines like llama.cpp and MNN on Llama 3.2 (1B, 3B) models with varying quantization (4-bit, 6-bit, 8-bit). It reveals significant performance differences between inference engines and operating systems, particularly under multi-instance scenarios where MNN shows greater degradation than llama.cpp. The findings highlight the need for OS-aware and engine-specific optimizations for efficient mobile LLM deployment, especially for parallel applications and AI agents.
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Performance uplift on Xiaomi 14 (Android 15) vs OnePlus (OpenCL, 4-bit 1B Llama 3.2)
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MNN throughput drop in multi-instance vs. llama.cpp (17%)
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Estimated Annual Savings for Optimized Mobile LLM Deployment
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Understanding LLM Efficiency in Isolation
This section details the performance of LLMs when a single instance is running, focusing on decoding speed, memory usage, and CPU utilization across different quantization levels and inference engines. It highlights significant variations based on engine, OS, and hardware optimization.
Concurrent LLM Workloads on Mobile
Here, we analyze how LLMs perform when multiple instances run concurrently, examining resource contention (CPU, GPU, memory) and degradation in latency and throughput. This is crucial for agentic workflows and parallel mobile applications.
Strategies for Enhanced Mobile LLM Deployment
This tab provides insights into the factors influencing mobile LLM performance, including hardware-aware scheduling, data management, and kernel implementations. It also discusses the implications for future development and optimization strategies.
25.7 Tokens/s
MNN CPU, Llama 3.2 1B, 4-bit on OnePlus 12. This significantly outperforms llama.cpp CPU (3.29 tokens/s).
Enterprise Process Flow
Host
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ADB
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Devices
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Logs: memory usage, decoding speed, CPU usage, etc.
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Executable (llama.cpp, MNN)
| Engine/Backend | OnePlus 12 | Samsung Galaxy S24+ | Xiaomi 14 (Android 15) | Xiaomi 14 (Android 14) |
|---|---|---|---|---|
| llama.cpp (CPU) | 3.29 | 3.15 | 3.08 | 3.05 |
| MNN (CPU) | 25.70 | 33.43 | 33.81 | 34.03 |
| llama.cpp (OpenCL) | 10.46 | 6.23 | 16.01 | 15.22 |
| MNN (OpenCL) | 29.52 | 29.07 | 22.25 | 28.51 |
| Notes: MNN consistently outperforms llama.cpp on CPU. GPU performance varies significantly by device and quantization level. | ||||
Impact of OS-Level Scheduling on GPU Performance
The study found that despite similar SoCs, Xiaomi 14 often outperforms OnePlus 12 by over 30% and sometimes matches Galaxy S24+ in GPU performance. This discrepancy is attributed to OS-level scheduling (e.g., Android's Energy-Aware Scheduling), which affects memory, thermal control, and CPU/GPU frequencies. This highlights the need for OS-aware inference engine design for mobile LLMs to fully leverage hardware capabilities.
Quantify Your AI Advantage
Estimate the potential annual savings by optimizing LLM deployment on mobile devices within your organization. Adjust the parameters to see your customized ROI.
Estimated Annual Savings
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Annual Hours Reclaimed
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Your Path to Mobile LLM Excellence
Implementing optimized LLMs on mobile devices requires a structured approach. Our roadmap guides you through key phases to ensure successful integration and performance.
Phase 01: Initial Assessment & Benchmarking
Evaluate existing mobile infrastructure, identify critical applications, and establish baseline LLM performance metrics across target devices and operating systems. Define key performance indicators (KPIs) for success.
Phase 02: Engine & Quantization Strategy
Select optimal inference engines (e.g., llama.cpp, MNN) and quantization levels (4-bit, 6-bit, 8-bit) based on model requirements, device constraints, and desired performance/accuracy trade-offs.
Phase 03: OS-Aware Optimization & Tuning
Implement OS-level scheduling adjustments, thermal management strategies, and hardware-specific kernel optimizations to maximize CPU, GPU, and NPU utilization for single- and multi-instance LLM execution.
Phase 04: Agentic Workflow Integration & Testing
Integrate optimized LLMs into parallel applications and AI agentic workflows. Conduct rigorous multi-instance testing to ensure stability, latency, and throughput meet enterprise-grade standards under real-world conditions.
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