Tag: Quantization

INT8 vs FP16 vs INT4: Which Precision Is Best for Edge Devices?

INT8, FP16, and INT4 are different ways devices balance performance, power efficiency, and accuracy. FP16 offers higher accuracy but uses more power. INT8 provides the best balance between efficiency and performance, making it widely used in smartphones and laptops. INT4…

Quantization vs Pruning: Optimizing LLMs for Edge Devices

QuantizationPruningArchitectural DifferencesLatencyTOPS (Tera Operations Per Second)Power ConsumptionMemory Footprint & BandwidthSoftware EcosystemDeployment ConsiderationsWhich Design Is More EfficientKey Takeaways This Quantization vs Pruning comparison explains how both optimization strategies affect edge LLM deployment efficiency. For large language models (LLMs) on edge devices, quantization primarily optimizes the numerical…