FP16 and BF16 Two ways to store a model’s numbers in 16 bits, and what they cost in memory.
Short answer. FP16 and BF16 are two ways of storing a number in 16 bits, and a common starting point for running AI models. FP16 (IEEE half precision) uses 1 sign bit, 5 exponent bits and 10 fraction bits. BF16 (bfloat16) uses 1 sign bit, 8 exponent bits and 7 fraction bits, which gives it the same range as 32-bit numbers but less precision. Either way each weight takes 2 bytes, so a 7-billion-parameter model needs about 14 GB. Smaller formats, 8-bit and 4-bit, cut that further at some cost in accuracy.
01
Bits, range and precision
The exponent bits set the range of sizes a number can take, and the fraction bits set how finely it is resolved. FP16 is fine-grained but narrow: NVIDIA lists its largest normal value as 65,504 and says its dynamic range, including very small values, spans 40 powers of 2, against 264 for single precision. BF16 keeps the 8 exponent bits of FP32 and trims the fraction, so numbers overflow or vanish less easily than in FP16.
Format
Sign bits
Exponent bits
Fraction bits
Range
FP32 (IEEE single precision)
1
8
23
The usual reference format for training
FP16 (IEEE half precision)
1
5
10
Largest normal value 65,504 (NVIDIA)
BF16 (bfloat16)
1
8
7
Same dynamic range as FP32 (Google)
02
Why AI uses 16 bits
Google’s engineers chose bfloat16 for their TPUs because neural networks are far more sensitive to the size of the exponent than to the size of the fraction, and because a hardware multiplier shrinks with the fraction width. NVIDIA notes that half precision halves the bytes moved, which speeds up memory-limited steps, and that its GPUs offer up to eight times more half-precision arithmetic throughput than single precision.
03
Memory by format
Weights only, in gigabytes, before the KV cache and runtime overhead. NVIDIA’s own example is a 7-billion-parameter model at 16 bits, about 14 GB.
Model size
FP32
FP16 or BF16
INT8
INT4
7 billion parameters
28 GB
14 GB
7 GB
3.5 GB
27 billion parameters
108 GB
54 GB
27 GB
13.5 GB
70 billion parameters
280 GB
140 GB
70 GB
35 GB
04
What to check
Which format the model was released in, and which format a vendor’s benchmark used.
Whether your hardware and software support the format you need. AMD’s ROCm guide for Ryzen, covering releases through 7.2.1, said only FP16 had been officially validated, though other types may work.
Do not compare a TFLOPS figure in FP16 with one in FP32 or FP4 (see TFLOPS).
If memory is tight, ask about 8-bit and 4-bit versions, and test them on your own documents.
05
Where ANVE stands
For ANVE’s launch units, the memory in the specification decides which format a model can run in: 64 GB of unified memory in ANVE One, a 24 GB graphics card with 64 GB of system memory in ANVE Office, and a 24 GB graphics card with 128 GB of system memory in ANVE Office Pro. No sealed unit exists yet.
06
Questions people ask
Which is better, FP16 or BF16?
Neither in general: BF16 trades precision for the same range as FP32, which Google found suits neural networks, while FP16 has finer precision but a narrow range. Use the format a model was released in unless a test shows otherwise.
Is 16-bit “full precision”?
No. Training has traditionally used 32-bit single precision, and 16-bit is called half precision.
Why do model cards mention FP16 or BF16?
They say how the weights are stored, which sets the file size and the memory needed.
Does 16-bit hurt accuracy?
NVIDIA says some networks need extra care, such as loss scaling, when trained in FP16. For running a published model, 16-bit is the baseline: the LLM.int8() and GPTQ papers both report their losses against the uncompressed model.
07
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
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