specific method · filed under inference & serving
Empirical bits-per-element budget selection
Selects a quantization budget by evaluating a fixed checkpoint across BPE settings on a curated evaluation suite.
Also called Bits per Element Selection.
- source
- 1
- model
- 1
- labs adopt it
- 0
- strongest
- evaluated
How sources treat it
One count per evidence span, weakest treatment to strongest.
evaluated 1
Documented in
Evidence
1 span quoted from the sources, strongest treatment first.
We selected the model’s bits-per-element (BPE) budget empirically, by quantizing a fixed intermediate checkpoint at a range of BPE settings and scoring each against a curated suite of evaluations
evaluatedevaluation onlyin Nemotron 3 UltraNVIDIA
Filed alongside
Other methods under inference & serving :: inference quantization.
FP8 KV-cache quantizationFP8FP4 KV-cache quantizationNVFP4 quantizationFour-Over-SixMXFP4 weight quantizationPost-training quantizationQuantizationBlock-scaled INT8 quantization with stochastic roundingFP8 E4M3 quantizationGGUFMax-based scalingMSE-based scalingMXFP8 activation quantizationNVFP4 KV-cache quantizationNVFP4 ModelOpt re-quantizationSSM cache quantizationAWQ INT4 weight quantization (W4A16)BF16 inferenceBlock-wise E4M3 FP8 weight quantizationChannel-wise quantizationDynamic activation scalingEmbedding and KV-cache quantizationFine-grained FP8 quantization