This list compares various amounts of computing power in instructions per second organized by order of magnitude in FLOPS. Scientific E notation index: 2 | 3 | 6 | 9 | 12 | 15 | 18 | 21 | 24 | >24 Deciscale computing (10−1) 5×10−1: Computing power of the average human mental calculation for multiplication using pen and paper Scale computing (100) 1 OP/S: Power of an average human performing calculations using pen and paper 1 OP/S: Computing power of Zuse Z1 5 OP/S: World record for addition set Decascale computing (101) 5×101: Upper end of serialized human perception computation (light bulbs do not flicker to the human observer) Hectoscale computing (102) 2.2×102: Upper end of serialized human throughput. This is roughly expressed by the lower limit of accurate event placement on small scales of time (The swing of a conductor's arm, the reaction time to lights on a drag strip, etc.) 2×102: IBM 602 1946 computer. Kiloscale computing (103) 92×103: Intel 4004 First commercially available full function CPU on a chip, released in 1971 500×103 Colossus computer vacuum tube supercomputer 1943 Megascale computing (106) 1×106: Computing power of the Motorola 68000 commercial computer introduced in 1979. This is also the minimum computing power of a Type 0 Kardashev civilization. 1.2×106: IBM 7030 "Stretch" transistorized supercomputer 1961 Gigascale computing (109) 1×109: ILLIAC IV 1972 supercomputer does first computational fluid dynamics problems 1.354×109: Intel Pentium III commercial computing 1999 147.6×109: Intel Core i7-980X Extreme Edition commercial computing 2010 Terascale computing (1012) 1.34×1012: Intel ASCI Red 1997 Supercomputer 1.344×1012 GeForce GTX 480 in 2010 from Nvidia at its peak performance 4.64×1012: Radeon HD 5970 in 2009 from AMD (under ATI branding) at its peak performance 5.152×1012: S2050/S2070 1U GPU Computing System from Nvidia 11.3×1012 :GeForce GTX 1080 Ti in 2017 13.7×1012: Radeon RX Vega 64 in 2017 15.0×1012: Nvidia Titan V in 2017 80×1012: IBM Watson 170×1012: Nvidia DGX-1 The initial Pascal based DGX-1 delivered 170 teraflops of half precision processing. 478.2×1012 IBM BlueGene/L 2007 Supercomputer 960×1012 Nvidia DGX-1 The Volta-based upgrade increased calculation power of Nvidia DGX-1 to 960 teraflops. Petascale computing (1015) 1.026×1015: IBM Roadrunner 2009 Supercomputer 1.32×1015: Nvidia GeForce 4000 Series RTX4090 Consumer graphics card, achieves 1.32 petaflops in AI applications, October 2022 2×1015: Nvidia DGX-2 a 2 Petaflop Machine Learning system (the newer DGX A100 has 5 Petaflop performance) 10×1015 (1016): Minimum computing power of a Type