#Precision

Articles about Precision — exploring patterns, best practices, and real-world implementations in production systems.

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Pratik Dhanave · ·5 min read

Numeric Precision — Why Lower Precision Makes Inference Faster

The fastest way to speed up LLM inference is often not a better algorithm but fewer bits per number. Precision — how many bits represent each weight and activation — is a direct lever on both of the limits this series has built up: it halves or quarters the data you move (bandwidth, post 2) and unlocks the GPU's fastest arithmetic units (compute, post 3). Understanding floating-point formats is understanding why fp16, bf16, and int8 are everywhere in modern inference.

FP32, FP16, BF16, and INT8/INT4 — how a float splits bits between range and precision, why bf16 won, and why fewer bits is a double win: less data to stream from HBM and faster tensor-core math. Quantization and why LLMs tolerate it.

#GPU#Quantization#Precision#Inference#Performance#LLMs

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.