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Concept Fable

Mixed precision training

Conclusion first:Mixed Precision Training uses different numerical precisions simultaneously during model training, such as FP32, FP16 and bf16, to reduce memory usage and increase throughput while preserving the stability of critical computations. It is usually combined with loss scaling, master weights or high-precision computation for key layers to avoid gradient underflow, numerical instability and degraded training quality.

Mixed Precision Training · Training

Read the story first. There is no rush to give a definition here; let the question grow on its own first.

Fable

Why silversmiths sometimes use a large scale and sometimes a small one

In its early years, the silversmith shop made only one kind of silver cup. The master used the finest small scale to weigh every speck of silver; it was slow, but there were few cups and there was enough time.

Later the royal city placed a large order: several thousand cups had to be delivered before the winter banquet. If every step used the small scale, the furnace fire would wait for people, and the schedule would certainly be missed.

The shopkeeper first switched to a large scale. Weighing large pieces of silver material was fast, but when it came to cup-rim patterns and thin-sheet proportions, the error began to deform the finished product.

Some insisted on using small scales everywhere. Quality was stable, but output was too low to deliver.

The old silversmith separated the processes: large-scale melting, transport, and rough blending used the big scale; key alloy ratios, patterned thin sheets, and final calibration used the small scale. Every time he switched from the big scale to the small scale, he checked whether error had accumulated.

At first the apprentices found it troublesome, but later they noticed the furnace no longer waited in vain, and the rims of the cups were no longer crooked. Later the royal city added another order for gilt-rimmed ceremonial cups. The old silversmith did not send every process back to the small scale; instead he added checks only where errors tended to accumulate: rim thickness, depth of the pattern, and final weight.

The silversmith shop also kept a master ledger, recording key recipes with the finest numbers to avoid carrying deviation too far through multiple rounds of rough weighing. The apprentices gradually understood that rough scales are not laziness, and small scales are not omnipotent. True craft is letting cheap, fast weighing handle most repetitive work, then reserving expensive, precise weighing for the nodes that determine success or failure.

The shopkeeper finally understood that speed and precision are not necessarily a choice between two; the key is knowing which weighings can be coarser and which must be precise. Later the steward wrote this division of labor into the furnace rules: new orders first mark ordinary processes and critical processes; ordinary processes pursue throughput, while critical processes keep fine scales and double-checking. During review, they also randomly reweigh a few cups to confirm that the small errors from the large scale have not snowballed later. If a batch of silver material has many impurities, temporarily increase the proportion of fine weighing; if it is just repeated rough blanks, let the furnace fire run continuously, saving time for the cup rims, thin walls, and patterns most prone to deformation.

Reveal

This story is about: mixed-precision training

Mixed Precision Training uses different numerical precisions simultaneously during model training, such as FP32, FP16 and bf16, to reduce memory usage and increase throughput while preserving the stability of critical computations. It is usually combined with loss scaling, master weights or high-precision computation for key layers to avoid gradient underflow, numerical instability and degraded training quality.

What matters is that it is not just a term, but a structural problem that repeatedly appears in real AI / Agent systems. Understanding it lets you judge when to add a model, when to change a process, and when to add governance.

Metaphor mapping

  • Large scale: low-precision computation, such as FP16/bf16
  • Small scale: high-precision computation, such as FP32
  • The royal city's big order: compute and throughput pressure of large-model training
  • Deformed cup rim: numerical error causes training instability
  • Main ledger: high-precision main weights or critical state
  • Error checking: loss scaling and numerical stability control

Andy Ren Xu's judgment

The practical value of this concept is that it helps you break "AI features that look smart" into deliverable, verifiable, and governable units of work.

fp16bf16fp32GPU memorytraining efficiency
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