From Float64 to INT4: The underlying logic and scenario adaptation of large model precision selection

In the field of deep learning, model accuracy is not only a technical parameter, but also a tool for bargaining between performance and cost. This article will analyze 8 core accuracy solutions from Float64 to INT4 based on technical principles and actual cases , helping you find the "golden section" that best suits your business scenario.

1. High-precision camp: the guardian of scientific computing

Float64 (double-precision floating point number)

• Technical features : 64-bit binary storage is used, providing about 15-16 significant digits, and a dynamic range of 1e-308 to 1e+308.
• Core scenarios :
• High-precision scientific computing (such as quantum mechanics simulation)
• Numerical stability requirements in financial risk control systems
• Small gradient calculations during neural network weight initialization
• Limitations : The video memory usage is twice that of FP32, and the computing speed is reduced by 40%-60%.

Float32 (single-precision floating point number)

• Industry status : The current standard accuracy for deep learning training, sovereign weights are always stored in FP32 to ensure gradient update stability.
• Typical applications :
• Main weight storage during model training
• Accuracy-sensitive medical image segmentation tasks
• Reward Function Calculation in Reinforcement Learning

2. Balancing factions: the art of compromise between efficiency and precision

BFloat16 (brain floating point number)

• Technological breakthrough : 16-bit storage is achieved by truncating the FP32 mantissa bits, maintaining the same exponent bit width (8 bits) as FP32, with a dynamic range loss of only 0.01%.
• Advantageous scenarios :
• Native support for Google TPU ecosystem (accelerated matrix operations)
• Mixed precision training in the pre-training phase of large models
• Word vector calculation in natural language processing

Float16 (half-precision floating point)

• Performance leap : Compared with FP32, the memory usage is reduced by 50%, and the computing speed is increased by 2-3 times on NVIDIA Ampere architecture GPU.
• Risk Warning :
• The probability of exploding/vanishing gradients increases by 30%
• Need to be used with Loss Scaling technology
• Mature applications :
• Stable Diffusion Wensheng Graph Model Reasoning
• Acoustic Models for Real-Time Speech Recognition

3. Low-precision revolution: the law of survival in the mobile Internet era

INT8 (8-bit integer)

• Quantization revolution : Mapping floating-point numbers to the integer space of -128~127, compressing the model volume to 1/4, and increasing the CUDA core computing throughput by 8 times.
• Industrial practice :
• Lightweight reasoning on mobile phones (such as MobileNetV3)
• Real-time object detection on edge devices
• Rough Ranking Model for E-commerce Recommendation System

INT4 (4-bit integer)

• Extreme optimization : The model size is compressed to 1/8 in exchange for a representation range of -8 to 7. A single A100 graphics card can run a model with over 10 billion parameters.
• Technical Challenges :
• The accuracy loss is 10%-15% (data distribution needs to be calibrated)
• Does not support direct quantization of complex activation functions
• Breakthrough Case :
• Meta's LLaMA-7B INT4 Quantitative Version
• Alibaba Cloud Qwen's mobile conversation engine
• Offline voice assistant on smartwatch

IV. Precision Selection Decision Tree (with Comparison Table)

5. Practical suggestions: precision combination strategies at different stages

1. Training phase : FP32 (master weight) + FP16 (temporary calculation) + BF16 (gradient aggregation)
2. Deployment phase :

• Cloud service: FP16+INT8 mixed precision (dynamic switching)
• Mobile terminal: INT4 quantization + CPU/GPU heterogeneous computing

• Special scenarios :
• Medical diagnosis model: FP32 full calculation
• Game NPC dialogue system: INT8+FP16 hybrid reasoning