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Showing 72 XPUs • 4 selected

Alibaba

Hanguang 800

AMD

MI100

23.1 TFLOPs

AMD

MI210

181 TFLOPs

AMD

MI250X

383 TFLOPs

AMD

MI300A

980.6 TFLOPs

AMD

MI300X

1,307 TFLOPs

AMD

MI325X

1,400 TFLOPs

AMD

MI350X

2,100 TFLOPs

AMD

Radeon PRO W7900

122 TFLOPs

AMD

Radeon RX 7900 XT

104 TFLOPs

AMD

Radeon RX 7900 XTX

122 TFLOPs

AWS

Inferentia2

190 TFLOPs

AWS

Trainium

190 TFLOPs

AWS

Trainium2

680 TFLOPs

Baidu

Kunlun II

Biren Technology

BR100

Cambricon

MLU370

256 TFLOPs

Cerebras

WSE-3

Enflame Technology

CloudBlazer T20

Etched

Sohu

10,000 TFLOPs

FuriosaAI

Warboy

Google

TPU v4

275 TFLOPs

Google

TPU v5e

197 TFLOPs

Google

TPU v5p

459 TFLOPs

Google

TPU v6e (Trillium)

918 TFLOPs

Graphcore

Bow IPU

Graphcore

IPU-M2000

Groq

LPU Inference Engine

Huawei

Ascend 910B

Iluvatar CoreX

BI-V150

300 TFLOPs

Intel

Data Center GPU Max 1100

177 TFLOPs

Intel

Data Center GPU Max 1550

419 TFLOPs

Intel Habana

Gaudi 2

432 TFLOPs

Intel Habana

Gaudi 3

1,835 TFLOPs

Multi-Metric Comparison

Relative performance across 5 key metrics (normalized to 100 = best in comparison)

Compute Performance (BF16)

Memory Capacity

Power Consumption

Power Efficiency

Specifications

Specification	Tenstorrent Wormhole	Microsoft Maia 100	NVIDIA GeForce RTX 4070 Super	Etched Sohu
Architecture	Tensix Core	Maia	Ada Lovelace	Transformer ASIC
Form Factor	—	—	PCIe	PCIe
VRAM	24 GB	64 GB	12 GB	144 GB
Memory Bandwidth	—	—	504 GB/s	4,800 GB/s
TFLOPs (FP32)	—	—	35.5	10,000
TFLOPs (FP16)	—	—	71	—
TFLOPs	364	700	71	10,000
TFLOPs (FP8)	—	—	—	—
TDP	160 W	500 W	220 W	700 W
Launch Date	Oct 2023	Nov 2023	Jan 2024	Jun 2024

Efficiency Metrics

Metric	Wormhole	Maia 100	GeForce RTX 4070 Super	Sohu
TFLOPs per Watt (FP32-eq)	1.14	0.70	0.16	14.29
Memory Bandwidth per GB	—	—	42.0 GB/s	33.3 GB/s

Performance Equivalence

How many units of each GPU are needed to match the performance of the others?

To match 1x Tenstorrent Wormhole

Microsoft Maia 100

Compute (FP32-eq)

0.52x

Maia 100 is 1.92x faster

VRAM

0.38x

Maia 100 has 2.67x more

NVIDIA GeForce RTX 4070 Super

Compute (FP32-eq)

5.13x

Need 5.13x GeForce RTX 4070 Super

VRAM

2.00x

Need 2.00x GeForce RTX 4070 Super

Etched Sohu

Compute (FP32-eq)

0.02x

Sohu is 54.95x faster

VRAM

0.17x

Sohu has 6.00x more

To match 1x Microsoft Maia 100

Tenstorrent Wormhole

Compute (FP32-eq)

1.92x

Need 1.92x Wormhole

VRAM

2.67x

Need 2.67x Wormhole

NVIDIA GeForce RTX 4070 Super

Compute (FP32-eq)

9.86x

Need 9.86x GeForce RTX 4070 Super

VRAM

5.33x

Need 5.33x GeForce RTX 4070 Super

Etched Sohu

Compute (FP32-eq)

0.04x

Sohu is 28.57x faster

VRAM

0.44x

Sohu has 2.25x more

To match 1x NVIDIA GeForce RTX 4070 Super

Tenstorrent Wormhole

Compute (FP32-eq)

0.20x

Wormhole is 5.13x faster

VRAM

0.50x

Wormhole has 2.00x more

Microsoft Maia 100

Compute (FP32-eq)

0.10x

Maia 100 is 9.86x faster

VRAM

0.19x

Maia 100 has 5.33x more

Etched Sohu

Compute (FP32-eq)

0.00x

Sohu is 281.69x faster

FP32 Compute

0.00x

Sohu is 281.69x faster

VRAM

0.08x

Sohu has 12.00x more

Memory Bandwidth

0.10x

Sohu has 9.52x more

To match 1x Etched Sohu

Tenstorrent Wormhole

Compute (FP32-eq)

54.95x

Need 54.95x Wormhole

VRAM

6.00x

Need 6.00x Wormhole

Microsoft Maia 100

Compute (FP32-eq)

28.57x

Need 28.57x Maia 100

VRAM

2.25x

Need 2.25x Maia 100

NVIDIA GeForce RTX 4070 Super

Compute (FP32-eq)

281.69x

Need 281.69x GeForce RTX 4070 Super

FP32 Compute

281.69x

Need 281.69x GeForce RTX 4070 Super

VRAM

12.00x

Need 12.00x GeForce RTX 4070 Super

Memory Bandwidth

9.52x

Need 9.52x GeForce RTX 4070 Super

Pricing

Price Type	Wormhole	Maia 100	GeForce RTX 4070 Super	Sohu
CAPEX (Street Price)	—	—	—	—
OPEX (per hour)	—	—	—	—
Price per TFLOPs (FP32-eq)	—	—	—	—