training

314.88 category score

Model	Precision	Sustained
MobileNetV3	FP32	376.00
MobileNetV3	BF16	248.23
Flan-T5 Small	FP32	470.80
Flan-T5 Small	BF16	263.33
DistilGPT2	FP32	391.29
DistilGPT2	BF16	223.21
DistilBERT	FP32	492.41
DistilBERT	BF16	405.03
GraphGPS (Peptides)	FP32	207.50
GraphGPS (Peptides)	BF16	184.43

inference

518.49 category score

Model	Precision	Sustained
MobileNetV3	FP16	1375.69
MobileNetV3	INT8	403.39
Flan-T5 Small	FP16	1009.63
Flan-T5 Small	INT8	503.01
Flan-T5 Small	FP8	566.59
DistilGPT2	FP16	752.18
DistilGPT2	INT8	420.32
DistilGPT2	FP8	457.31
DistilBERT	FP16	898.53
DistilBERT	INT8	438.15
DistilBERT	FP8	514.72
GraphGPS (Peptides)	FP16	268.17
GraphGPS (Peptides)	INT8	138.11
GraphGPS (Peptides)	FP8	137.60

compute

339.34 category score

Model	Precision	Sustained
Dense MatMul	FP32	3030.36
Dense MatMul	FP16	2437.44
Dense MatMul	BF16	2430.16
Dense MatMul	FP64	86.83
Sparse MatMul	FP32	57.57
Sparse MatMul	FP64	57.80

Provisional results — not the final, official release · GT = LynxBenchAI Global Score. Every precision result is sustained throughput under a declared, bounded optimisation budget — no collapsed single number below the category level.

← Back to the leaderboard