Infrastructure Tools
FLOPS Calculator
Add GPUs, see total FLOPS, power, and VRAM instantly.
How it works
1 Add GPUs with the + button
2 Adjust quantities
3 View detailed FLOPS, power & VRAM results
GPUs & Systems
130| Name | Peak TFLOPS | Memory | TDP | |
|---|---|---|---|---|
HW Ascend 970 Huawei | 4,000 FP8 | 288 GB | — | |
HW Ascend 960 Huawei | 2,000 FP8 | 288 GB | — | |
Vera Rubin NVIDIA · Superchip | 35,000 FP8 | 576 GB | — | |
Rubin NVIDIA · SXM | 17,500 FP8 | 288 GB | — | |
HW Ascend 950DT Huawei | 1,000 FP8 | 144 GB | — | |
HW Ascend 950PR Huawei | 1,000 FP8 | 128 GB | 900 W | |
Instinct MI355X AMD · OAM | 5,033.2 FP8 | 288 GB | 1400 W | |
GB300 NVIDIA · Grace Blackwell Ultra Superchip 252GB | 5,000 FP8 | 252 GB | — | |
Instinct MI350X AMD · OAM | 4,614 FP8 | 288 GB | 1000 W | |
TPU v7 Google · 192GB | 4,614 FP8 | 192 GB | — | |
B200 NVIDIA · SXM 180GB | 4,500 FP8 | 180 GB | 1000 W | |
B300 NVIDIA · SXM 288GB | 4,500 FP8 | 288 GB | 1400 W | |
Instinct MI325X AMD · OAM | 2,614.9 FP8 | 256 GB | 1000 W | |
GB10 Grace Blackwell NVIDIA | 1,000 FP8 | 128 GB | 180 W | |
MT MTT S5000 Moore Threads | 1,000 FP8 | 80 GB | — | |
GeForce RTX 5090 NVIDIA · 32GB | 838.2 FP8 | 32 GB | 575 W | |
HW Ascend 910C Huawei | 800 FP16 | 128 GB | 600 W | |
GeForce RTX 5080 NVIDIA · 16GB | 450.2 FP8 | 16 GB | 360 W | |
H20 NVIDIA · 141GB HBM3e | 148 FP16 | 141 GB | 400 W | |
RTX PRO 6000 Blackwell NVIDIA · Workstation Edition | 125 FP32 | 96 GB | 600 W | |
DGX Spark NVIDIA | 100 FP16 | 128 GB | 240 W | |
M5 Apple · Max | 33.2 FP16 | 128 GB | — | |
M5 Apple · Pro | 16.6 FP16 | 64 GB | — | |
M5 Apple | 8.3 FP16 | 32 GB | — | |
B100 NVIDIA · SXM 192GB | 3,500 FP8 | 192 GB | 700 W | |
H200 NVIDIA · SXM 141GB | 1,979 FP8 | 141 GB | 700 W | |
Gaudi 3 Intel · 128GB | 1,835 FP8 | 128 GB | 900 W | |
H200 NVIDIA · NVL 141GB | 1,670.5 FP8 | 141 GB | 600 W | |
HW Ascend 910B Huawei | 400 FP16 | 64 GB | 400 W | |
BD Kunlun P800 Baidu · Kunlun III | 345 FP16 | — | — | |
L20 NVIDIA · 48GB | 239 FP8 | 48 GB | 275 W | |
GeForce RTX 4080 Super NVIDIA · 16GB | 208.9 FP8 | 16 GB | 320 W | |
H20 NVIDIA · 96GB | 148 FP16 | 96 GB | 400 W | |
MX N260 MetaX | 140 FP16 | 64 GB | 225 W | |
MT MTT S4000 Moore Threads | 100 FP16 | 48 GB | 450 W | |
L2 NVIDIA · 24GB | 96.5 FP16 | 24 GB | — | |
RTX 2000 Ada NVIDIA · 16GB | 48 FP8 | 16 GB | 70 W | |
M4 Max Apple | 36.9 FP16 | 128 GB | — | |
M4 Pro Apple | 18.4 FP16 | 64 GB | — | |
MT S80 Moore Threads | 14.4 FP32 | 16 GB | 255 W | |
M4 Apple | 8.5 FP16 | 32 GB | — | |
TPU v6e Google · 32GB | — | 32 GB | — | |
Instinct MI300X AMD · 192GB | 2,614.9 FP8 | 192 GB | 750 W | |
GH200 NVIDIA | 1,979 FP8 | 468 GB | 1000 W | |
Instinct MI300A AMD · 128GB | 1,961.2 FP8 | 128 GB | 760 W | |
H100 NVIDIA · NVL 94GB | 1,670.5 FP8 | 94 GB | 400 W | |
Data Center GPU Max 1550 Intel · 128GB | 839 FP16 | 128 GB | 600 W | |
L40S NVIDIA · 48GB | 733 FP8 | 48 GB | 350 W | |
RTX 6000 Ada NVIDIA · 48GB | 728.5 FP8 | 48 GB | 300 W | |
RTX 5000 Ada NVIDIA · 32GB | 522.2 FP8 | 32 GB | 250 W | |
L4 NVIDIA · 24GB | 485 FP8 | 24 GB | 72 W | |
TPU v5p Google · 95GB | 459 FP8 | 95 GB | 450 W | |
Data Center GPU Max 1100 Intel · 48GB | 419.5 FP16 | 48 GB | 300 W | |
L40 NVIDIA · 48GB | 362 FP8 | 48 GB | 300 W | |
MX C500 MetaX | 280 FP16 | 64 GB | 350 W | |
GeForce RTX 4070 Ti NVIDIA · 12GB | 160.4 FP8 | 12 GB | 285 W | |
M2 Ultra Apple | 54.4 FP16 | 192 GB | — | |
M3 Max Apple | 32.8 FP16 | 128 GB | — | |
M2 Max Apple | 27.2 FP16 | 96 GB | — | |
MT S3000 Moore Threads · 32GB | 15.2 FP32 | 32 GB | 250 W | |
M3 Pro Apple | 14.8 FP16 | 36 GB | — | |
M2 Pro Apple | 13.6 FP16 | 32 GB | — | |
M3 Apple | 7.1 FP16 | 24 GB | — | |
CB MLU590 Cambricon | — | — | — | |
TPU v5e Google · 16GB | — | 16 GB | — | |
H100 NVIDIA · SXM5 80GB | 1,979 FP8 | 80 GB | 700 W | |
H100 NVIDIA · PCIe 80GB | 1,513 FP8 | 80 GB | 350 W | |
H800 NVIDIA · PCIe 80GB | 1,513 FP8 | 80 GB | 350 W | |
Gaudi 2 Intel · 96GB | 865 FP8 | 96 GB | 600 W | |
GeForce RTX 4090 NVIDIA · 24GB | 330.3 FP8 | 24 GB | 450 W | |
A800 NVIDIA · PCIe 80GB | 312 FP16 | 80 GB | 300 W | |
BR BR100 Biren | 256 FP32 | 64 GB | 550 W | |
GeForce RTX 4080 NVIDIA · 16GB | 195 FP8 | 16 GB | 320 W | |
Instinct MI210 AMD · PCIe | 181 FP16 | 64 GB | 300 W | |
Radeon RX 7900 XTX AMD · 24GB | 122.8 FP16 | 24 GB | 355 W | |
Radeon RX 7900 XT AMD · 20GB | 103.2 FP16 | 20 GB | 315 W | |
CB MLU370-X8 Cambricon | 96 FP16 | 48 GB | 250 W | |
A10G NVIDIA · 24GB | 70 FP16 | 24 GB | 300 W | |
M1 Ultra Apple | 42.4 FP16 | 128 GB | — | |
GeForce RTX 3090 Ti NVIDIA · 24GB | 40 FP16 | 24 GB | 450 W | |
Arc A770 Intel · 16GB | 39.4 FP16 | 16 GB | 225 W | |
Data Center GPU Flex Series Intel · 170 16GB | 16 FP32 | 16 GB | 150 W | |
Data Center GPU Flex Series Intel · 140 12GB | 8 FP32 | 12 GB | 75 W | |
RTX A2000 NVIDIA · 12GB | 8 FP16 | 12 GB | 70 W | |
M2 Apple | 7.1 FP16 | 24 GB | — | |
MI250X AMD · 128GB | 383 FP16 | 128 GB | 560 W | |
Instinct MI250 AMD · 128GB | 362.1 FP16 | 128 GB | 500 W | |
A100 NVIDIA · PCIe 80GB | 312 FP16 | 80 GB | 300 W | |
A30 NVIDIA · 24GB | 165 FP16 | 24 GB | 165 W | |
A40 NVIDIA · 48GB | 149.7 FP16 | 48 GB | 300 W | |
IX BI-V100 Iluvatar CoreX · Tiangai 100 | 147 FP16 | 32 GB | 250 W | |
EF Yunsui T20 Enflame | 134.4 FP16 | 32 GB | 300 W | |
EF Yunsui i20 Enflame | 128 FP16 | 16 GB | 150 W | |
A10 NVIDIA · 24GB | 125 FP16 | 24 GB | 150 W | |
GeForce RTX 3080 Ti NVIDIA · 12GB | 34.1 FP16 | 12 GB | 350 W | |
RTX A5000 NVIDIA · 24GB | 27.8 FP16 | 24 GB | 230 W | |
RTX A4500 NVIDIA · 20GB | 23.7 FP16 | 20 GB | 200 W | |
M1 Max Apple | 20.8 FP16 | 64 GB | — | |
RTX A4000 NVIDIA · 16GB | 19.2 FP16 | 16 GB | 140 W | |
M1 Pro Apple | 10.6 FP16 | 32 GB | — | |
A100 NVIDIA · SXM4 40GB | 312 FP16 | 40 GB | 400 W | |
A100 NVIDIA · PCIe 40GB | 312 FP16 | 40 GB | 250 W | |
A100 NVIDIA · SXM4 80GB | 312 FP16 | 80 GB | 400 W | |
Instinct MI100 AMD · 32GB | 184.6 FP16 | 32 GB | 300 W | |
V100S NVIDIA · PCIe 32GB | 130 FP16 | 32 GB | 250 W | |
RTX A6000 NVIDIA · 48GB | 38.7 FP16 | 48 GB | 300 W | |
GeForce RTX 3090 NVIDIA · 24GB | 35.6 FP16 | 24 GB | 350 W | |
GeForce RTX 3080 NVIDIA · 10GB | 29.8 FP16 | 10 GB | 320 W | |
GeForce RTX 3070 NVIDIA · 8GB | 20.3 FP16 | 8 GB | 220 W | |
M1 Apple | 5.2 FP16 | 16 GB | — | |
TPU v4 Google · 32GB | — | 32 GB | — | |
Vera Rubin NVL72 NVIDIA 72 GPUs | — | 20.7 TB | — | |
DGX B300 NVIDIA 8 GPUs | — | 2.1 TB | 14.5 kW | |
DGX GB300 NVL72 NVIDIA 72 GPUs | — | 20.7 TB | — | |
DGX SuperPOD GB200 NVIDIA 2304 GPUs | — | — | 3840 kW | |
GB200 NVL32 NVIDIA 32 GPUs | — | — | 53.3 kW | |
GB200 NVL36 NVIDIA 36 GPUs | — | — | 60 kW | |
GB200 NVL576 NVIDIA 576 GPUs | — | — | 960 kW | |
GB200 NVL72 NVIDIA 72 GPUs | — | — | 120 kW | |
HGX B200 8-GPU NVIDIA 8 GPUs | — | 1.4 TB | 10.2 kW | |
DGX H200 NVIDIA 8 GPUs | — | 1.1 TB | 8.5 kW | |
HGX B100 8-GPU NVIDIA 8 GPUs | — | 1.5 TB | 5.6 kW | |
HGX H200 NVIDIA 8 GPUs | — | 1.1 TB | 8 kW | |
DGX H100 NVIDIA 8 GPUs | — | 640 GB | 8.5 kW | |
HGX H100 4-GPU NVIDIA 4 GPUs | — | 320 GB | 4.2 kW | |
HGX H100 8-GPU NVIDIA 8 GPUs | — | 640 GB | 8 kW | |
DGX A100 320GB NVIDIA 8 GPUs | — | 320 GB | 6 kW | |
DGX A100 640GB NVIDIA 8 GPUs | — | 640 GB | 6.5 kW | |
HGX A100 4-GPU NVIDIA 4 GPUs | — | 320 GB | 3.2 kW | |
HGX A100 8-GPU NVIDIA 8 GPUs | — | 640 GB | 6 kW |
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About the FLOPS Calculator
Estimate the total floating-point performance, power draw, and memory capacity for any combination of datacenter GPUs and pre-configured systems. All figures use vendor-published peak TFLOPS adjusted by your chosen efficiency factor.