Nexus/GPUs: Difference between revisions

Revision as of 19:05, 28 September 2023

There are several different types of NVIDIA GPUs in the Nexus cluster that are available to be scheduled. They are listed below in order of newest to oldest architecture, and then alphabetically.

Name	Architecture	CUDA Cores	GPU Memory	Memory Bandwidth	FP32 Performance (TFLOPS)	TF32 Performance (Dense / Sparse TOPS)
GeForce RTX 3070	Ampere	5888	8GB GDDR6	448GB/s	20.3	20.3/40.6
GeForce RTX 3090	Ampere	10496	24GB GDDR6X	936GB/s	35.6	35.6/71
RTX A4000	Ampere	6144	16GB GDDR6	448GB/s	19.2	not officially published
RTX A5000	Ampere	8192	24GB GDDR6	768GB/s	27.8	not officially published
RTX A6000	Ampere	10752	48GB GDDR6	768GB/s	38.7	77.4/154.8
GeForce RTX 2080 Ti	Turing	4352	11GB GDDR5X	616GB/s	13.4	n/a
GeForce GTX 1080 Ti	Pascal	3584	11GB GDDR5X	484GB/s	11.3	n/a
GeForce GTX Titan Xp	Pascal	3840	12GB GDDR5X	548GB/s	12.1	n/a
Quadro P6000	Pascal	3840	24GB GDDR5X	432GB/s	12.6	n/a
Tesla P100	Pascal	3584	16GB CoWoS HBM2	732GB/s	9.3	n/a
GeForce GTX Titan X	Maxwell	3072	12GB GDDR5	336GB/s	6.7	n/a
Tesla M40	Maxwell	3072	12GB GDDR5	288GB/s	6.8	n/a
Tesla K80	Kepler	4992	24GB GDDR5	480GB/s	8.7	n/a

@@ Line 8: / Line 8: @@
 ! Memory Bandwidth
 ! FP32 Performance (TFLOPS)
-! [https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/ TF32] Performance ([https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt Dense / Spare TOPS])
+! [https://developer.nvidia.com/blog/accelerating-ai-training-with-tf32-tensor-cores/ TF32] Performance ([https://developer.nvidia.com/blog/accelerating-inference-with-sparsity-using-ampere-and-tensorrt Dense / Sparse TOPS])
 |-
 | GeForce RTX 3070

Nexus/GPUs: Difference between revisions

Revision as of 19:05, 28 September 2023

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