LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load
LLM Inference at the Edge: Mobile, NPU, and GPU Performance Efficiency Trade-offs Under Sustained Load explores Benchmarking LLM inference performance and efficiency across diverse edge devices to identify optimal deployment strategies under sustained load.. Commercial viability score: 4/10 in LLM Inference Optimization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSLA-Aware Distributed LLM Inference Across Device-RAN-CloudViability: 3.0
- Builds On ThisBeyond GEMM-Centric NPUs: Enabling Efficient Diffusion LLM SamplingViability: 2.0
- Prior WorkSustainability Is Not Linear: Quantifying Performance, Energy, and Privacy Trade-offs in On-Device IntelligenceViability: 4.0
- Prior WorkMobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry ScaleViability: 4.0
- Higher ViabilityPrivate LLM Inference on Consumer Blackwell GPUs: A Practical Guide for Cost-Effective Local Deployment in SMEs
Related Resources
- Can you explain the concept of early exits in LLM inference optimization with TIDE?(question)
- What are the trade-offs between latency reduction and throughput enhancement in LLM inference optimization?(question)
- What are the practical and scalable LLM inference optimization solutions emerging in the field?(question)