Nvidia K80 Memory

2x kepler gk210 memory bandwidth.

Nvidia k80 memory. Meanwhile tesla k80 will also be pushing the power envelope again to get 2 gpus on a single card. The machine has 4 nvidia k80s setuped and the outputs of nvidia smi are the information of the 4 cards. 15638 unified memory profiling result. Device tesla k80 0.

Nvidia s pc graphics chips may draw all the attention but supercomputing chips are driving the company s gpu technology ahead. Nvidia gpu boost is a feature available on nvidia geforce and tesla gpus that boosts application performance by increasing gpu core and memory clock rates when sufficient power and thermal headroom are available see the earlier parallel forall post about gpu boost by mark harris. This puts the total memory pool between the two gpus at 24gb with 480gb sec of bandwidth among them. In the case of tesla gpus gpu boost is customized for compute intensive workloads running on clusters.

Nvidia has paired 24 gb gddr5 memory with the tesla k80 which are connected using a 384 bit memory interface per gpu each gpu manages 12 288 mb. Unfortunately each gpu needs to map just over 16gb of memory which means that only one of the two k80 gpu devices can be passed through to a vm currently. The nvidia tesla k80 accelerator dramatically lowers data center costs by delivering exceptional performance with fewer more powerful servers. Esx 6 can support large memory regions but it currently has a fixed limit of 32gb per vm for such mappings.

The gpu is operating at a frequency of 562 mhz which can be boosted up to 824 mhz memory is running at 1253 mhz 5 gbps effective. 480 gb sec 240 gb sec per gpu cuda cores. Memory bandwidth ecc off 480 gb sec 240 gb sec per gpu memory size gddr5 24 gb 12gb per gpu one difference to previous high end solutions that were based on gk110 is that k80 uses a new gpu called gk210. Each k80 have two types of gpu memory.

A k80 card actually contains two separate gk210 gpu devices. Fb and bar1 both have 12 g. Nvidia tesla k80 900 22080 0000 000 passive computing accelerators memory size. It s engineered to boost throughput in real world applications by 5 10x while also saving customers up to 50 for an accelerated data center compared to a cpu only system.

24gb gddr5 12gb per gpu gpu. Their gpu ids are 0 1 2 3.