Edge AI Optimization

Mis à jour le 2026-04-14Confiance : high

edge-aiinference-optimizationmobile-deploymentmemory-optimizationlatency-optimization

Specialized techniques for deploying AI models on resource-constrained edge devices, focusing on memory efficiency, latency optimization, and task-specific performance rather than general capabilities.

Core Constraints

Memory-Bound Operations

Models must operate within strict memory limits (<3B parameters)
Memory bandwidth more limiting than computational power
Parameter efficiency critical for deployment success

Latency Requirements

Sub-100ms response times required for user-facing applications
Fast prefill more important than decode speed optimization
Real-time inference constraints shape architecture decisions

Device-Specific Optimization

Mobile processors (Galaxy S24 Ultra, Ryzen HX 370)
CPU-optimized inference paths
Hardware-specific quantization strategies (4-bit with llama.cpp)

Architecture Strategies

Parameter Distribution

Optimize embedding layer size (19% vs traditional 63% allocation)
Balance between knowledge storage and computational efficiency
Effective model size through strategic parameter allocation

Operator Efficiency

Gated Short Convolution blocks show 2.5x better cost ratios
Replace attention mechanisms with more efficient alternatives
Hardware-specific operator optimization (CPU vs GPU paths)

Model Size Targets

<1GB models for on-device reasoning (LFM2.5-1.2B-Thinking)
Sub-3B parameter counts for memory-bound constraints
Task-specific models over general-purpose alternatives

Inference Optimization

CPU Optimization

llama.cpp integration with 4-bit quantization
Memory-efficient attention alternatives (ShortConv)
Optimized operator cost ratios for CPU decode

GPU Batch Processing

SGLang integration for concurrent inference
Scaling performance with multiple simultaneous requests
Input/output token optimization (1024/256 token targets)

Mobile Deployment

On-device profiling and optimization
Platform-specific performance tuning
Battery and thermal management considerations

Training Considerations

Task-Specific Focus

Narrow domain optimization over general capabilities
Easy adaptation to new domain-specific data
Post-training efficiency for specialized tasks

Memory-Aware Training

Architecture choices informed by deployment constraints
Parameter allocation strategies during training
Inference-first design philosophy

Performance Metrics

Latency Benchmarks

Sub-100ms response time requirements
Prefill speed optimization priorities
Real-time inference capability

Memory Efficiency

Model size under deployment constraints
Runtime memory usage optimization
Quantization impact on accuracy vs efficiency

Throughput Scaling