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Also known as: 8-bit
Eight-bit integer precision used to quantize model weights and activations for faster, cheaper inference.
DESCRIPTION
INT8 quantization replaces floating-point tensors with 8-bit integers to reduce memory and accelerate inference. It is widely supported across CPUs and GPUs, offering strong performance gains with minimal accuracy loss after calibration or QAT. INT8 underpins production optimization in vision, NLP, and recommendation systems. Tooling includes TensorRT, ONNX Runtime, PyTorch, and OpenVINO with device-specific kernels.
ALTERNATIVE DEFINITIONS
ONNX Runtime
Quantization reduces model size and latency by converting weights and activations from floating point to lower precision, such as INT8.
Connects INT8 precision directly to latency and size reduction benefits.
PyTorch
Quantization refers to techniques for performing computations and storing tensors at lower bitwidths than floating point, such as int8.
Defines quantization techniques and computation/storage at reduced precision.
NVIDIA
INT8 precision enables high-performance inference by using 8-bit integer arithmetic with calibration to maintain accuracy.
Highlights calibration to preserve accuracy while accelerating inference.
RELATED TERMS
Quantization Calibration Post-Training Quantization Quantization-Aware Training NVIDIA TensorRT ONNX Runtime Throughput Delays INT4
COMPETING TERMS
KNOWLEDGE AREAS
AI Infrastructure MLOps Mathematics
TARGET AUDIENCE
AI Engineer MLOps Engineer Data Engineer Cloud Architect Software Developer
EXTERNAL SOURCES
- Wikipedia - Quantization (machine learning)
- ONNX Runtime - INT8 quantization guide
- PyTorch - Quantization API and concepts
- OpenVINO - INT8 quantization overview
TOP QUESTIONS
Why is INT8 widely used for inference?
It delivers strong speedups and memory savings with small accuracy impact, supported across many hardware targets.
Category: Relevancy
What is calibration in INT8 quantization?
Calibration estimates scaling factors from representative data to map floats to int8 while preserving accuracy.
Category: Definition
How does INT8 compare to FP16?
INT8 typically yields more compression and speed, but FP16 can preserve accuracy better on some tasks.
Category: Comparison
When is Quantization-Aware Training needed?
When post-training quantization degrades accuracy too much, QAT can recover performance.
Category: Decision
What mistakes occur with INT8 deployment?
Using non-representative calibration data and quantizing sensitive layers without testing degrade accuracy.
Category: Mistakes
Does INT8 help CPU inference?
Yes, many CPUs support int8 vector instructions, improving throughput significantly.
Category: Capabilities
Can activations and weights both be INT8?
Yes, though some pipelines keep activations in higher precision for stability.
Category: Mechanism
How to verify INT8 quality?
Compare accuracy on validation sets and monitor production metrics and drift after deployment.
Category: Testing
What workloads benefit most from INT8?
Large vision models, LLM serving, and recommendation systems with strict latency budgets.
Category: Application