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NVIDIA Tesla P4 and P40 for Deep Learning Networks
NVIDIA Tesla K80 — Details on the Most Powerful Accelerator
NVIDIA Tesla compute accelerators are firmly established in everything from stock analysis to scientific calculations. They are equipped with special servers, and computing superclusters are built on their basis. The secret of NVIDIA’s success in this area is the support of all modern both closed (CUDA) and open technologies (OpenCL, DirectCompute). And in one of the previous news, we already reported that the company is preparing to launch new models of Tesla accelerators, both based on the new Maxwell architecture and based on the time-tested Kepler architecture. Standing apart in this list was the Tesla K80 model, which was supposed to be the second NVIDIA dual-processor accelerator after the outdated D870. nine0006
NVIDIA Tesla K80 does not have a fan
And so it happened. The company has published the official announcement of the Tesla K80, the most powerful accelerator in the series to date. As expected, he received two processors, but not the GK110, as one might expect, but the completely new GK210, which, however, are produced using the same TSMC 28-nanometer process technology.
Dual-processor graphics cards are always a compromise, and the same is true for computing accelerators. If one GK110 processor on board the Tesla K40 has 2880 active stream processors, then the GK210 in the Tesla K80 design were somewhat truncated in the configuration and received 2496 processors per chip. This made it possible to fit into a 300-watt heat pack and make the cooling system completely passive, designed to be blown through by the fans installed in the server case. There are usually a lot of them there and they provide a powerful air flow, since you don’t need to take special care of silence.
The world’s fastest scientific computing accelerator
Not without a clock reduction: the Tesla K80 cores run at just 562 MHz in base mode and 875 MHz in turbo mode. But in this case, quantity beats quality: almost 5 thousand stream processors, or rather, 4992, working in turbo mode, easily deliver 2.91 teraflops of processing power in double precision mode. In normal mode, this figure drops to 1.
87 teraflops, which is still more than the Tesla K40 can give in turbo mode (1.66 teraflops). At the same time, the card has a standard layout: one PCIe x16 slot and double height, which is indispensable for compact systems that nevertheless require high processing power. And in the single-precision calculation mode, the beginner’s performance looks even more impressive: 8.74 and 5.6 teraflops, respectively. The fast NVLink inter-processor bus avoids the bottlenecks traditional for NUMA systems. nine0006
A fast interprocessor bus guarantees no bottlenecks
The memory subsystem did not let us down either: NVIDIA Tesla K80 has 24 gigabytes of fast GDDR5 memory installed at once, which is a kind of record: even AMD FirePro W9100 has only 16 gigabytes. And these are honest 24 gigabytes, because, unlike the SLI gaming technology, the data in the memory of the first GPU should not be duplicated in the memory block of the second GPU. Do I need to explain that the amount of memory in massive computing plays an important role? Bandwidth is also not forgotten: the total performance of the Tesla K80 memory subsystem reaches 480 GB / s, 240 GB / s for each processor.
This makes it an ideal solution for almost any area where massive computing is needed — from astrophysics, genetics and quantum chemistry, to the analysis of large data sets and «deep machine learning» systems. In total, Tesla accelerators can work with more than 280 applications and software packages. nine0006
The benefits of GPGPU are obvious
According to NVIDIA, the Tesla K80 accelerator is an order of magnitude (10 times) ahead of the best traditional processors in the most common scientific and engineering software packages such as GROMACS, AMBER, LSMS or Quantum Espresso. If we think about thermal and electrical characteristics, it turns out that the K80 is very much superior to conventional CPUs in terms of energy efficiency: the 18-core Intel Xeon E5-2699v3 has a TDP in the region of 145 watts, and the NVIDIA Tesla K80, as mentioned above, has only about 300 watts, that is, like a pair of such Xeons. Moreover, the latter is incomparably faster. So, it should be concluded that the idea of GPGPU, that is, «graphics processor-based computing», has taken root perfectly in modern science, engineering and economics.
So say the best minds on the planet. nine0006
Wide range of applications and high performance. Traditional CPUs don’t stand a chance
In particular, Wolfgang Nagel, director of the Information Services Center at Dresden University of Technology, says that scientists are using the resources of the Taurus supercomputer, built on NVIDIA GPUs, for tasks such as finding and developing treatments cancer, the study of cells in real time, and even the study of asteroids within the recently thundered ESA project «Rosetta» all over the world. And the emergence of a new powerful, but at the same time compact and economical model of the NVIDIA Tesla accelerator will certainly lead to the creation of even more powerful and efficient supercomputers, which will benefit both science and humanity as a whole. Deliveries of the NVIDIA Tesla K80 accelerator have already begun, more details can be found in the corresponding section of the NVIDIA website, and for skeptics there is even a free opportunity to try out the GPGPU in action.
nine0006
Meanwhile, technology does not stand still, and it will be very interesting to look at future Tesla monsters based on the GM200.
NVIDIA Tesla Compute Systems
NVIDIA® Tesla™ GPUs are the foundation of Team Workstation supercomputers. Their use can significantly increase the performance of solving computational problems in various fields, including video and image processing, biology and chemistry, fluid dynamics modeling, seismic studies, and many others. A detailed list of applications focused on the use of TESLA can be found on the NVIDIA website. nine0006
GPU-accelerated computing has unparalleled performance due to the fact that parts of the application that require a lot of computing power are processed by a specialized graphics processor. The rest of the application runs on the CPU.
Unlike a multi-core CPU optimized for sequential data processing. A GPU is made up of thousands of smaller, more power-efficient cores designed to handle multiple tasks at the same time.
nine0006
The NVIDIA TESLA Computing System is the leading platform for accelerating scientific computing and big data analytics. It combines the fastest graphics accelerators, NVIDIA’s ubiquitous parallel computing model CUDA, and a vast ecosystem of software developers.
NVIDIA multi-GPU technology successfully scales performance by using a combination of multiple NVIDIA TESLA or NVIDIA QUADRO graphics cards in a single system. nine0006
Some numbers for illustration purposes. The Tesla K80 graphics accelerator delivers up to 2.91 teraflops double precision performance and up to 8.74 teraflops single precision performance.
High performance on large datasets thanks to large onboard memory (24 GB on Tesla K80 GPU). The increased data transfer rate to ensure their availability is provided by the high bandwidth of the memory used (480 Gb / s for the Tesla K80 GPU). nine0006
Below is a histogram comparing GPU and CPU performance.
NVIDIA TESLA Computing Lineup
The NVIDIA TESLA K20
graphics accelerator is equipped with a single Kepler GK110 processor, 12 GB of memory and delivers peak double precision performance of 1.
17 teraflops.
The NVIDIA TESLA K40
graphics accelerator is equipped with a single Kepler GK110B processor, 12 GB of memory and delivers peak double precision performance of 1.43 teraflops. nine0006
NVIDIA TESLA K80 Graphics Accelerator
New with dual Kepler GK210 GPUs and 24GB of 480Gbps memory . With NVIDIA GPU Boost™ technology, peak double precision floating point performance reaches 2.7 teraflops.
NVIDIA TESLA-enabled workstations and servers
- Team WorkStation W4-E52
- Team Server R1-E52
- Team Server R2-E52
- Team WorkStation P4000CR
Rumors about the Tesla K40 accelerator circulated in early October, and in general they turned out to be quite true. K40 is a further development of a family of products based on the GK110 chip. The new accelerator received a version of the GK110B chip with 2880 stream processors (against 2688 for the previous flagship K20X) and a base frequency increased to 745 MHz (for K20X — 732 MHz) with Boost frequencies of 810 MHz and 875 MHz.
In addition, the K40 model received a faster 1.5 GHz GDDR5 memory (effective frequency — 6 GHz) with a total of 12 GB — a very significant upgrade compared to the previous generation’s 6 GB. By the way, in the consumer sector, NVIDIA also recently tried to squeeze the most out of its GK110 chip, introducing the most powerful single-chip accelerator on the market — the GeForce GTX 780 Ti with 3 GB of GDDR5 video memory. nine0006
NVIDIA is currently the leading player in the High Performance Computing (HPC) GPU accelerator market with an 85% market share. At the same time, the application market for GPU-based stream processors and the big data analytics sector are growing rapidly, with more than 40% of HPC servers now equipped with GPU accelerators. The launch of the Tesla K40 is intended to further consolidate the company’s success in the market.
Tesla K40 has a theoretical compute performance of 1.43 teraflops in double precision (4.29 teraflops in single precision).
teraflops). This is not a very large increase compared to the K20X (1.31 and 3.95 teraflops, respectively), but thanks to twice the amount of fast memory, the increase compared to the K20X can, according to NVIDIA, reach 40%. In addition to the launch of a new powerful accelerator, NVIDIA announced a partnership with a very influential player in the server OEM market — IBM.
In August this year, IBM announced the creation of the OpenPOWER Consortium, which includes companies such as Google, NVIDIA, Mellanox and Tyan. The alliance is committed to building server, networking, storage and GPU-accelerated technologies for a new generation of scalable cloud data centers. Now, IBM and NVIDIA have made a separate announcement to jointly develop GPU-accelerated enterprise software and IBM applications on Power processor systems. The companies plan to work together to integrate IBM Power8 processors with NVIDIA Tesla accelerators. nine0006
Partners can be very effective in the HPC market.
The IBM Power8 architecture is a 12-core, multi-threaded chip with 96 MB of L3 eDRAM cache capable of processing up to 96 simultaneous threads. This powerful chip is capable of efficiently feeding data to the GPU for faster processing. IBM is very competitive in the HPC sector, and a partnership with NVIDIA could put significant pressure on Intel. By the way, the latest family of Intel Xeon Phi 7100 stream processors can offer up to 1.2 teraflops of double-precision performance and 16 GB of memory on board (as already mentioned, NVIDIA Tesla K40 performance is up to 1.43 teraflops at 12 GB memory). nine0006
Well, the first new systems from IBM and NVIDIA should appear next year.
NVIDIA Tesla P4 and P40 for Deep Learning Networks
On the Internet, users everywhere are exposed to the results of deep learning networks, although often they are not aware of it. Deep learning is understood as various services that use artificial intelligence to some extent. Although they are associated not so much with artificial intelligence as with data analysis.
In any case, deep learning networks will be used everywhere in the future, so many companies are rushing to introduce the corresponding hardware. nine0006 Intel is positioning its Xeon Phi GPU accelerators in this area, while Google has developed its own TPU chips. NVIDIA is one of the pioneers of deep learning. The computing power of modern GPUs can be used not only to draw triangles and texture mapping, but also to perform a large number of parallel computing tasks — just such a load is typical for deep learning networks.
NVIDIA Tesla P4 and Tesla P40
The load of deep learning networks on hardware resources can be divided into two parts. First comes the training phase. For example, the network analyzes several billion photos in the right categories. What is shown in the picture? Is there a bird there, what does it do and what species does it belong to? The result is complex databases with several billion nodes. Training a deep learning network requires a huge amount of computing power so that the process does not take months or days, but several hours.
For such a process, NVIDIA developed the DGX-1 servers. Each rack server uses eight Pascal-based Tesla P100s. Each chip is equipped with 3,584 stream processors, 16 GB of HBM2 memory with a bandwidth of 720 GB / s, which allows the P100 to cope with such computing tasks. nine0006 NVIDIA Tesla P4 and Tesla P40
Tesla P4 and P40 accelerate the search for information in deep learning networks
Above we described the first part of the load — training. The second part is related to the processing of requests to the information-filled deep learning network (inference). Here it is required to get the result as quickly as possible, which is achieved by a large number of parallel calculations. And just for such a load, NVIDIA introduced the Tesla P4 and P40 GPU accelerators today. nine0006
A request to the deep learning network should be processed not even in seconds, but in a matter of fractions of a second. At least, that’s the goal set by NVIDIA. Low latency is important when a user accesses the network directly.
Examples include a voice request to search for a nearby restaurant, when the deep learning network recognizes the voice and then searches for the restaurant. It is unlikely that the user will be comfortable waiting for a response to his request for several seconds. The answer must come as soon as possible. nine0006 NVIDIA Tesla P4 and Tesla P40
But let’s move on to the hardware. Let’s start with the Tesla P4. The computing accelerator is quite compact, it focuses on areas of use where not only speed is important, but also efficiency. Tesla P4 is based on the same GP104 as the GeForce GTX 1080 gaming graphics card, but the accelerator is more compact. To reduce the cooling system, NVIDIA set the 2.560 stream processors to very low frequencies. NVIDIA uses two modes to set the frequency and performance. In the first P4 Base (defined as SGEMM), the Tesla P4 accelerator reaches 810 MHz GPU frequency, equivalent to 16.6 TOPS (INT8) compute performance. Single precision computing performance reaches 4.
15 TFLOPS. In P4 Boost mode (defined as 70% SGEMM), the Tesla P4 achieves a Boost frequency of 1.063 MHz, which corresponds to 21.8 TOPS (INT8). Single precision performance reaches 5.5 TFLOPS in this case. 8 GB GDDR5 memory runs at 192 GB/s. Power consumption, depending on the mode, is 50 or 75 watts. For the GP104 GPU, the 50/75 W level is really very low, which once again underlines the effectiveness of NVIDIA’s Pascal architecture. NVIDIA Tesla P4 and Tesla P40
The second new compute card is the Tesla P40. It already uses the GP102 GPU, which we met in the same Titan X or Quadro P6000. For the Tesla P40, NVIDIA already specifies a significantly higher thermal package of 250 W, so the computing accelerator is interesting for those environments where performance is in the first place, not efficiency. With the Tesla P40, we also get two clock speed modes. The base frequency is 1.303 MHz, which corresponds to 40 TOPS (IN8) or 10 TFLOPS in single precision. In Boost mode, the frequency increases to 1.
531 MHz, the card accelerates to 47 TOPS (INT8) or 12 TFLOPS. 24 GB of memory work with a bandwidth of 346 GB / s. nine0006 NVIDIA Tesla P4 and Tesla P40
NVIDIA also provided accelerator performance results for deep learning networks. If on an Intel CPU with 14 cores, the latency is 260 ms, in the case of the Tesla P4, it drops to just 11 mm, and with the Tesla P40 accelerators, it drops to 6 ms. NVIDIA cites the work of deep learning networks with video streams as an example, and performance results are also given here. A server with a Tesla P4, for example, analyzes a little over 90 streams (720p at 30 FPS) at the same time, the same task requires 13 servers on Intel Xeon E5-2650. However, it is difficult to say how accurately these tests correspond to reality. nine0006
NVIDIA is working with several server manufacturers, corresponding systems with Tesla P40 will be available from October, and servers on Tesla P4 will appear only in November. NVIDIA has not yet indicated the price.
NVIDIA Tesla P4 and Tesla P40
With the introduction of the Tesla P4 and P40 accelerators, NVIDIA closes the deep learning cycle. All this should lead to a significant increase in the performance of Deep Learning.
Nvidia Tesla V100 is the current record holder in mining Ethereum
Information about the material Published: 08/17/2017 09:40
With the release of AMD RADEON RX VEGA, rumors do not cease in the network that this particular card can produce 70-100 megahashes when mining Ethereum, but at the moment this information is not confirmed by tests on latest available drivers. Therefore, the position of the current leader Nvidia Tesla V100 in Ethereum mining with a result of 80Mh/s remains in first place.The Nvidia Tesla V100 is a professional computing device based on the Volta GPU, expected in the consumer segment no earlier than 2021. nine0486
Of the features, it should be noted the use of HBM2 memory, as AMD did in its Vega, although the memory width here is full at 4096 Bits.
The GV100 chip itself is made according to 12nm standards and contains 5120 FP32 cores (analogous to CUDA cores in consumer video cards) and 2560 cores for FP64 calculations. The performance in FP32 calculations is 15 Teraflops and 7.5 Teraflops for FP64, which is 50% more than the previous generation chips. By the way, the previous generation of Nvidia Tesla P100 issues on the Dagger Hashimoto algorithm (Ethereum) 69-72 Mh/s, using Genoil/cpp-ethereum miner source code compiled for ppc64el architecture. In other words, Teslas are not compatible with the x86 code for ordinary computers and they cannot run Claymores more productive miners in mining, because. the miner code is closed and only the author of these miners can compile under the ppc64el architecture.
A discussion on using 4 Nvidia Tesla P100s in one bundle with a total hashrate of 275Mh / s and a power consumption of 1kW can be read on this Reddit thread. nine0006
As for the newcomer, there is even less information on the performance of the Nvidia Tesla V100, but there are brief reports on specialized forums that these cards give out at least 80 Mh / s when mining ether and power consumption in the region of 150W.
Given these figures, the Nvidia Tesla V100 is the most productive Ethereum mining tool both in terms of volume and energy efficiency (less than 2W per Mh / s), but the whole impression is spoiled by its price (from $ 5.000 for Nvidia Tesla P100 ) and poor availability in retail. nine0006
Discuss on the forum
- Mining equipment
NVIDIA Tesla K20X/K20: the most powerful server video cards on a single GPU one big premiere. This time around, the fanfare came from NVIDIA, which is said to have unveiled what it claims are the fastest and most energy-efficient single-chip server-class graphics cards ever made, the Tesla K20X and Tesla K20 models based on a 28nm chip. GK110 with Kepler architecture. nine0006
NVIDIA Tesla K20X
NVIDIA Tesla K20X
Both new products are offered in a two-slot version with passive cooling on printed circuit boards for the PCI Express 3.0 x16 bus.
Tesla’s flagship K20X, with 2688 CUDA cores, is said to provide the highest single-GPU performance available today, namely 3.
95 teraflops in single precision and 1.31 teraflops in double precision. On board there is 6144 MB of GDDR5 memory with a 384-bit interface. The core/memory frequency is 732/5200 MHz. The maximum power consumption reaches 235 watts. nine0006 In turn, the Tesla K20 with 2496 CUDA cores is characterized by a performance of 3.52 teraflops in single precision calculations and 1.17 teraflops in double precision calculations. The video card received 5120 MB of GDDR5 memory with a 320-bit interface, operates at frequencies of 706/5200 MHz (core / memory) and consumes no more than 225 watts of energy during operation.
According to the developers, 18688 Tesla K20X accelerators formed the basis of the Titan supercomputer, which, according to the new edition of the TOP500 rating, is by far the most powerful in the world. This supercomputer is located at the Oak Ridge National Laboratory, Tennessee. He became the new leader of the world ranking of supercomputers with a score of 17.
59petaflops in the LINPACK benchmark, displacing the Sequoia system from Livermore National Laboratory in this position. Lawrence. Along the way, it is noted that the Tesla K20X graphics adapter has a threefold lower power consumption compared to the previous generation of NVIDIA accelerators and further increases the performance gap between the GPU and CPU. Thus, the Titan supercomputer provides 2142.77 megaflops per watt and thus surpasses the leader of the latest version of the list of the most economical Green500 supercomputers in terms of energy efficiency. nine0006
The creators also emphasize that the Tesla K20X model, combined with Intel’s Sandy Bridge generation CPU, can speed up many applications by more than 10 times. For example, MATLAB (engineering) — 18.1 times, Chroma (physics) — 17.9 times, SPECFEM3D (earth science) — 10.5 times, AMBER (molecular dynamics) — 8.2 times.
Finally, we would like to inform you that the video cards described above are already supplied and available as part of solutions from leading server manufacturers, including Appro, ASUS, Cray, Eurotech, Fujitsu, HP, IBM, Quanta Computer, SGI, Supermicro, T-Platforms and Tyan , and NVIDIA reseller partners.
nine0002 Virtual machine series | Microsoft Azure
Find the right Azure VMs for your needs and budget with the VM picker.
Series A
Entry-level dev/test VMs
A-series VMs have processor and memory performance characteristics that are best suited for entry-level workloads such as dev/test, code repositories, and more. A low-cost, entry-level use case for Azure. Av2 Standard is the latest generation of non-Hyper-Threaded A-series VMs with the same CPU performance but more RAM per vCPU and faster disk speeds. A-series virtual machines (“Basic” and “Standard”) will be decommissioned on August 31, 2024.
Sample workloads : Development and test servers, low traffic web servers, small-to-medium databases, proof-of-concept servers, and code repositories.
Series A
from
$11.
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Low cost storage virtual machines
Bs-series Low Cost VMs avoid unnecessary costs and are suitable for workloads that typically run at low to moderate CPU usage but sometimes require significant CPU overhead. Bs-series virtual machines do not use Hyper-Threading technology.
Sample workloads : Development and test servers, low traffic web servers, small databases, microservices, proofreading servers, and build servers. nine0006
Bs-series
from
$3.8
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General purpose computing
Azure D-series VMs support a combination of vCPUs, memory, and temporary storage that is sufficient to meet the requirements of most production workloads.
Dv3-series VMs are general-purpose hyper-threaded VMs based on 2.
3 GHz Intel® XEON ® E5-2673 v4 (Broadwell) processors. When using Intel Turbo Boost Technology 2.0, processor clock speeds can be increased up to 3.5 GHz. nine0006 Dv4 and Ddv4 series virtual machines are based on custom-designed Intel® Xeon® Platinum 8272CL processors. The base clock speed of such processors is 2.5 GHz, it can be increased up to 3.4 GHz for all cores. The Ddv4 series virtual machines also include fast, high-capacity SSD-based local storage (up to 2400 GiB) to run applications that require high-speed, low-latency local storage. Dv4 series virtual machines do not have temporary storage. nine0006
Dv5 and Ddv5 series virtual machines are based on 3rd generation Intel® Xeon® Platinum 8370C (Ice Lake) processors in hyperthreading configuration. These virtual machines can have up to 96 vCPUs and their configurations are similar to those of the Dv4 and Ddv4 series virtual machines.
Azure Dav4 and Dasv4 series VMs use AMD EPYC™ 7452 processors. Supports up to 96 vCPUs, up to 384 GiB of RAM, and 2400 GiB of SSD-based temporary storage.
nine0006 The Dasv5 and Dadsv5 series virtual machines are based on 3rd generation AMD EPYC™7763v (Milan) processors. The maximum frequency of this processor when using acceleration reaches 3.5 GHz. In these series, virtual machines of different sizes are available both with local temporary storage (Dadsv5) and without it (Dasv5). This is a better value proposition for most general purpose workloads than previous generation virtual machines: Dav4 and Dasv4.
Dpsv5 and Dpdsv5 virtual machines are based on multi-core 64-bit Ampere Altra processors with ARM architecture, the processor clock speed can reach 3.0 GHz. Designed for scalable cloud environments, Ampere Altra processors offer high performance and lower overall environmental impact. The Dplsv5 and Dpldsv5 VMs are the most affordable general purpose Azure VMs. The amount of memory on virtual machines is 2 GiB per vCPU. This is a great value proposition for many general purpose Linux workloads that do not require large amounts of RAM per vCPU.
nine0006 The Ds, Dds, Das, Dads, Dps, Dpds, Dpls, and Dplds series VMs support Premium and Ultra Azure SSD storage based on availability in each region.
Sample workloads : Various enterprise applications, e-commerce systems, web interfaces, desktop virtualization solutions, customer relationship management applications, entry-level and mid-range databases, application servers, game servers, media servers, and much more…
Series D
from
$41.61
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Optimized for in-memory applications
Azure E-series virtual machines (VMs) are optimized for large in-memory applications such as SAP HANA. These VMs are configured with a high memory-to-core ratio, making them ideal for enterprise applications with high memory requirements, large relational database servers, in-memory analytics, etc.
Ev3 series VMs can range from 2 to 64 vCPUs and 16 to 432 GiB of RAM, respectively.
E-series v4 and Ed v4-series virtual machines are based on custom-designed Intel® Xeon® Platinum 8272CL processors. The base clock speed of such processors is 2.5 GHz, it can be increased up to 3.4 GHz for all cores. The amount of RAM in E-series version 4 and Ed version 4 virtual machines can be up to 504 GiB. Ed Series v4 VMs also include fast, high-capacity SSD-based local storage (up to 2400 GiB) to run applications that require high-speed, low-latency local storage. E-series v4 VMs do not have temporary storage. nine0006
The E version 5 and Ed version 5 VMs are based on 3rd generation Intel® Xeon® Platinum 8370C (Ice Lake) processors in a hyper-threaded configuration. In Turbo mode, all cores of these custom-designed processors can be clocked up to 3.5 GHz. The amount of RAM for these virtual machines can be up to 672 GiB. Various sizes of virtual machines are available, both with local temporary storage (Eds version 5) and without it (Es version 5).
You can scale the capacity of isolated instances up to 104 vCPUs. nine0006 With improved remote storage performance, Ebs version 5 and Ebds version 5 virtual machines are well suited for high-throughput storage workloads such as large relational databases and data analytics applications. Ebds version 5 and Ebs version 5 virtual machines can achieve up to 300% performance gains in remote storage compared to previous generations of virtual machines. This makes it possible to consolidate existing workloads on fewer virtual machines or use smaller virtual machines to achieve cost savings. nine0006
Azure Ea series version 4 and Eas version 4 VMs use AMD EPYC™ 7452 processors. Up to 96 vCPUs are supported, up to 672 GiB of RAM, and 2400 GiB of SSD-based temporary storage.
Eas v5 and Eads v5 virtual machines are based on 3rd generation AMD EPYC™7763v (Milan) processors. The maximum frequency of this processor when using acceleration reaches 3.5 GHz. In these series, virtual machines of different sizes are available both with local temporary storage (Eads version 5) and without it (Eas version 5).
This is a better value proposition for most general purpose workloads than previous generation VMs: Ea version 4 and Eas version 4. Eps version 5 and Epds version 5 virtual machines are based on multi-core 64-bit Ampere Altra processors with ARM architecture, the processor clock speed can reach 3.0 GHz. Designed for scalable cloud environments, Ampere Altra processors offer high performance and lower overall environmental impact.
Es, Eds, Eas, Eads, Ebs, Ebds, Eps, and Epds series VMs support Premium and Ultra Azure SSD storage based on availability in each region. nine0006
Sample workloads : SAP HANA (for example, E64s version 3, E20ds version 4, E32ds version 4, E48ds version 4, E64ds version 4), SAP S/4 HANA application tier, SAP NetWeaver application tier, and more In general, memory-intensive enterprise applications, large relational database servers, data warehouse workloads, business intelligence applications, in-memory analytics workloads, and other business-critical applications, including systems that process financial transactions.
nine0006 E-series
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/per monthF-series
Compute-optimized virtual machines
F-series virtual machines have a higher CPU to memory ratio. This series provides 2 GB of RAM and a 16 GB local solid-state drive (SSD) per CPU core. Virtual machines are optimized for compute intensive workloads. The Fsv2 series provides 2 GiB of RAM and 8 GB of local temporary storage (SSD) per vCPU. Fsv2-series VMs with Hyper-Threading Technology are powered by an Intel Xeon® Platinum 8168 (SkyLake) processor running at 2.7 GHz, which can be boosted up to 3.7 GHz with Intel Turbo Boost Technology 2.0. nine0006
Sample workloads : batch processing, web servers, analytics, and gaming.
F-series
from
$35.
77
/per monthG-series
Memory and storage-optimized virtual machines
G-series virtual machines are equipped with an Intel® Xeon® processor E5 v3 family. They also offer twice the memory and four times the SSD capacity of D-series general purpose virtual machines. G-series supports up to 0.5 TB of RAM, up to 32 CPU cores, and delivers unrivaled performance, memory and SSD-based local storage for the most demanding applications. nine0006
Sample workloads : Large SQL and NoSQL databases, ERP, SAP, data storage solutions.
G-series
from
$320.47
/per monthH-series
HPC VMs
HB-series VMs are optimized for HPC applications in areas such as financial analysis, weather simulation, and microchip simulation at the register transfer level.
HB series virtual machines are equipped with a maximum of 120 AMD EPYC™ 7003 CPU cores with 448 GB of RAM. Hyper-Threading is not supported. HB-series VMs also provide 350 GB/s of memory bandwidth, up to 32 MB L3 cache per core, up to 7 GB/s SSD block I/O performance, and clock speeds up to 3.675 GHz. nine0006 The HC-series virtual machines are optimized for compute-intensive HPC applications such as implicit finite element analysis, reservoir simulation, and computational chemistry. The HC-series virtual machines are equipped with 44 Intel Xeon Platinum 8168 processor cores, 8 GB of RAM per CPU core, and a maximum of 4 managed disks. Hyper-Threading is not supported. The Intel Xeon Platinum platform supports a rich ecosystem of Intel software tools and 3.4GHz all cores for most workloads. nine0006
Workload examples : fluid dynamics, finite element analysis, seismic data processing, reservoir simulation, risk analysis, electronic design automation, rendering, Spark, weather simulation, quantum simulation, computational chemistry, heat transfer simulation.
H-series
from
$581.08
/per monthnine0006
Ls-series
Storage-optimized virtual machines
Ls-series virtual machines are optimized for storage operations and are ideal for applications that require low latency, high bandwidth, and large amounts of local disk storage. These VMs are based on Intel Haswell processors, specifically VMs based on E5 Xeon v3 processors with 4, 8, 16, or 32 cores. Ls-series VMs support up to 6TB SSD-based local storage and deliver unparalleled storage I/O performance. nine0006
The Lsv2 VM series provides high throughput, low latency, and directly mapped local NVMe storage. Lsv2 virtual machines run on the AMD EPYC™ 7551 processor with all cores up to 2.55 GHz and single core up to 3.0 GHz. The Lsv2 series VMs support up to 80 vCPUs in a Hyper-Threaded configuration, 8 GiB of memory per vCPU, and up to 19.
2 TB (10×1.92 TB) of storage available directly to the VM. nine0006 The Lasv3 series of VMs provide similar features to the Lsv2 VMs. It is powered by the 3rd Generation AMD EPYC™ 7763v (Milan) Processor configured with Hyper-Threading Technology.
Finally, the Lsv3 series of virtual machines provides size configurations comparable to Lasv3 virtual machines. It is powered by the 3rd Gen Intel® Xeon® 8370C (Ice Lake) Processor configured with Hyper-Threading Technology.
You can attach SSDs (Standard Pricing), HDDs (Standard Pricing), and drives (Ultra Pricing) to Lsv2, Lasv3, and Lsv3 virtual machines, depending on regional availability. nine0006
Sample workloads : NoSQL databases such as Cassandra, MongoDB, Cloudera, and Redis. Storage applications and large transactional databases are also suitable use cases.
Ls-series
from
$455.
52
/per monthM-series
Memory-optimized virtual machines
Azure M-Series VMs are memory-optimized and ideal for memory-intensive workloads such as SAP HANA. The M-series provides 4 TB of RAM per virtual machine. In addition, these virtual machines are equipped with a large number of CPUs — up to 128 vCPUs per virtual machine. As a result, they provide high-performance parallel processing.
Sample workloads : SAP HANA, SAP S/4 HANA, SQL Hekaton, and other large mission-critical in-memory workloads that require highly efficient parallel computing. nine0006
Series M
from
$1121.28
/per monthMv2 Series
Largest Memory-Optimized Virtual Machines
Mv2 Series Virtual Machines are Hyper-Threaded, Intel® Xeon® Platinum 8180M (Skylake) 2.
5 GHz processors, can have up to 416 virtual CPUs on a single virtual machine and are available in 3TB, 6TB, and 12TB memory configurations. These are the highest-memory VMs offered on Azure and provide unparalleled compute performance to support large in-memory databases. nine0006 Sample workloads : SAP HANA, SAP S/4 HANA, SQL Hekaton, and other large mission-critical in-memory workloads that require highly efficient parallel computing.
Mv2 series
from
$16286.3
/per monthN-series
GPU virtual machines
The N-series is a family of Azure virtual machines that have the capabilities of graphics processing unit (GPU) workstations. Graphics processing units (GPUs) are ideal for computing that requires powerful graphics resources. With the help of GPUs, customers can solve more complex and resource-intensive tasks, such as high-quality remote visualization, deep learning, and predictive analytics.
The N Series has three offerings for different workloads:
- The NC Series is designed for high performance and machine learning workloads. The latest version (NCsv3) supports NVIDIA’s Tesla V100 GPU.
- The NDs series is designed for deep learning and dependency learning scenarios. This series of virtual machines uses Tesla P40 GPUs from NVIDIA. The latest version (NDv2) supports NVIDIA Tesla V100 GPUs.
- The NV Series supports high-performance remote visualization workloads and graphics-intensive applications with back-up based on NVIDIA Tesla M60 GPUs. nine0080
The NCsv3, NCsv2, NC, and NDs series virtual machines offer an optional InfiniBand interconnect to improve scaling performance.
Sample workloads : Simulation, deep learning, graphics rendering, video editing, gaming, and remote visualization.
Series N
from
$657
/per monthnine0006
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