NVIDIA GPU – ONE PLATFORM. UNLIMITED DATA CENTER ACCELERATION.
NVIDIA GPU – Accelerating scientific discovery, visualizing big data for insights, and providing smart services to consumers are everyday challenges for researchers and engineers. Solving these challenges demands increasingly complex and precise simulations, processing vast amounts of data, or training sophisticated deep learning networks. These workloads require accelerated data centers to meet the exponential demand for computing.
With the NVIDIA Hopper (H100 and H200) architecture at the forefront of accelerated computing, and Blackwell on the horizon, the evolution of High-Performance Computing (HPC) continues to drive groundbreaking advancements in AI, data analytics, and scientific simulations. The Hopper architecture offers exceptional performance, efficiency, and scalability, designed to tackle the most demanding modern workloads with unprecedented speed and power optimization.
The flexibility of Hopper allows for scaling up to deliver maximum performance or partitioning for smaller, independent workloads, accelerating diverse applications across industries. As we look ahead to the next leap in GPU architecture with Blackwell, data centers will experience even greater throughput, improved utilization, and innovative architecture designs, further empowering researchers and engineers to unlock new scientific discoveries and insights from massive datasets.
OUR VALUE
DECADES OF SUCCESSFUL HPC DEPLOYMENTS
Architected For You
As a leading HPC provider, Aspen Systems offers a standardized build and package selection that follows HPC best practices. However, unlike some other HPC vendors, we also provide you the opportunity to customize your cluster hardware and software with options and capabilities tuned to your specific needs and your environment. This is a more complex process than simply providing you a “canned” cluster, which might or might not best fit your needs. Many customers value us for our flexibility and engineering expertise, coming back again and again for upgrades to existing clusters or new clusters which mirror their current optimized solutions. Other customers value our standard cluster configuration to serve their HPC computing needs and purchase that option from us repeatedly. Call an Aspen Systems sales engineer today if you wish to procure a custom-built cluster built to your specifications.
Solutions Ready To Go
Aspen Systems typically ships clusters to our customers as complete turn-key solutions, including full remote testing by you before the cluster is shipped. All a customer will need to do is unpack the racks, roll them into place, connect power and networking, and begin computing. Of course, our involvement doesn’t end when the system is delivered.
True Expertise
With decades of experience in the high-performance computing industry, Aspen Systems is uniquely qualified to provide unparalleled systems, infrastructure, and management support tailored to your unique needs. Built to the highest quality, customized to your needs, and fully integrated, our clusters provide many years of trouble-free computing for customers all over the world. We can handle all aspects of your HPC needs, including facility design or upgrades, supplemental cooling, power management, remote access solutions, software optimization, and many additional managed services.
Passionate Support, People Who Care
Aspen Systems offers industry-leading support options. Our Standard Service Package is free of charge to every customer. We offer additional support packages, such as our future-proofing Flex Service or our fully managed Total Service package, along with many additional Add-on services! With our On-site services, we can come to you to fully integrate your new cluster into your existing infrastructure or perform other upgrades and changes you require. We also offer standard and custom Training packages for your administrators and your end-users or even informal customized, one-on-one assistance.
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Aspen Products Featuring the Nvidia GPU
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Data Center Double Precision & Compute GPUs
| Name | H200 (SXM) |
H200 (NVL) |
H100 (SXM) |
H100 (PCIe) |
H100 (NVL) |
A800 |
|---|---|---|---|---|---|---|
| Appearance |  |
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| Architecture | Hopper | Hopper | Hopper | Hopper | Hopper | Ampere |
| FP64 | 34 TF | 34 TF | 34 TF | 26 TF | 68 TF | 9.7 TF |
| FP64 Tensor Core | 67 TF | 67 TF | 67 TF | 51 TF | 134 TF | |
| FP32 | 67 TF | 67 TF | 67 TF | 51 TF | 134 TF | 19.5 TF |
| Tensor Float 32 (TF32) | 156 TF | 312 TF* | 156 TF | 312 TF* | 156 TF | 312 TF* | 156 TF | 312 TF* | 156 TF | 312 TF* | |
| BFLOAT16 Tensor Core | 1,979 TF | 1,979 TF | 1,979 TF | 1,513 TF | 3,958 TF | 624 TF |
| FP16 Tensor Core | ||||||
| INT8 Tensor Core | 3,958 TOPS | 3,958 TOPS | 3,958 TOPS | 3,206 TOPS | 7,916 TOPS | |
| GPU Memory | 141 GB | 141 GB | 80 GB | 80 GB | 188 GB | 40 GB |
| GPU Memory Bandwidth | 4.8 TB/s | 4.8 TB/s | 3.35 TB/s | 2 TB/s | 7.8 TB/s | 1.5 TB/s |
| TDP | Up to 700 W |
Up to 600 W | 700 W | 300-350 W | 2x 350-400 W | 240 W |
| Interconnect | NVLink: 900GB/s PCIe Gen5: 128GB/s |
NVLink: 900GB/s PCIe Gen5: 128GB/s |
NVLink: 900GB/s PCIe Gen5: 128GB/s |
NVLink: 600GB/s PCIe Gen5: 128GB/s |
NVLink: 600GB/s PCIe Gen5: 128GB/s |
NVLink: 400GB/s PCIe Gen4: 64GB/s |
Data Center Ada Lovelace Single Precision GPUs
| Name | L40S | L40 | L4 |
|---|---|---|---|
| Appearance |  |
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| Architecture | Ada Lovelace | Ada Lovelace | Ada Lovelace |
| FP64 | |||
| FP32 | 91.6 TF | 90.5 TF | 30.3 TF |
| Tensor Float 32 (TF32) | 183 TF | 366 TF | 90.5 TF | 181 TF | 120 TF |
| BFLOAT16 Tensor Core | 362 TF | 733 TF | 181.05 TF | 362.1 TF | 242 TF |
| FP16 Tensor Core | |||
| INT8 Tensor Core | 733 TF | 1,466 TOPS | 362 TOPS | 724 TOPS | 485 TF |
| GPU Memory | 48 GB GDDR6 with ECC | 48 GB GDDR6 with ECC | 24 GB GDDR6 with ECC |
| GPU Memory Bandwidth | 864 GB/s | 864 GB/s | 300 GB/s |
| TDP | 350 W | 300 W | 72 W |
| Interconnect | PCIe Gen4 64 GB/s (bidirectional) |
PCIe Gen4 64 GB/s (bidirectional) |
PCIe Gen4 64 GB/s (bidirectional) |
Professional Ada Lovelace Series GPUs
| Name | RTX 6000 Ada | RTX 5000 Ada | RTX 4500 Ada | RTX 4000 Ada |
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| Appearance |  |
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| Architecture | Ada Lovelace | Ada Lovelace | Ada Lovelace | Ada Lovelace |
| FP64 | 1,423 GF | 1,020 GF | 619.2 GF | 299.5 GF |
| FP32 | 91 TF | 65.28 TF | 39.63 TF | 19.17 TF |
| BFLOAT16 Tensor Core | 91 TF | 65.28 TF | 39.63 TF | 19.17 TF |
| FP16 Tensor Core | ||||
| GPU Memory | 48GB GDDR6 | 32 GB GDDR6 | 24 GB GDDR6 | 20 GB GDDR6 |
| GPU Memory Bandwidth | 960 GB/s | 576 GB/s | 432 GB/s | 280 GB/s |
| TDP | 300 W | 250 W | 130 W | 70 W |
| Interconnect | PCIe 4.0 x16 | PCIe 4.0 x16 | PCIe 4.0 x16 | PCIe 4.0 x16 |
Speak with One of Our System Engineers Today
Nvidia H200 Tensor Core GPU
Supercharging AI and HPC Workloads
The NVIDIA H200 Tensor Core GPU delivers a breakthrough in performance and memory capacity, transforming both generative AI and high-performance computing (HPC) tasks. As the first GPU to feature HBM3e technology, the H200’s enhanced and expanded memory propels the speed of generative AI and large language models (LLMs), while also pushing the boundaries of scientific computing for demanding HPC applications.
Highlights
Experience Next-Level Performance
1.9x
Llama2 70B Inference
Faster
1.6x
GPT-3 175B Inference
Faster
110x
High Performance Computing
Faster
Benefits
Enhanced Performance with Expanded, Faster Memory
Powered by the NVIDIA Hopper™ architecture, the NVIDIA H200 is the first GPU to feature 141 gigabytes (GB) of HBM3e memory, delivering a staggering 4.8 terabytes per second (TB/s) in bandwidth. This represents almost double the capacity of the NVIDIA H100 Tensor Core GPU and 1.4 times the memory bandwidth. With its increased memory size and speed, the H200 accelerates the performance of generative AI and large language models (LLMs), while also driving advancements in HPC workloads. Additionally, it offers improved energy efficiency and a reduced total cost of ownership.
Unlock Insights With High-Performance LLM Inference
In the ever-evolving landscape of AI, businesses rely on LLMs to address a diverse range of inference needs. An AI inference accelerator must deliver the highest throughput at the lowest TCO when deployed at scale for a massive user base.
The H200 boosts inference speed by up to 2X compared to H100 GPUs when handling LLMs like Llama2.
Boost High-Performance Computing Efficiency
In high-performance computing (HPC), memory bandwidth plays a vital role by speeding up data transfers and minimizing bottlenecks during complex processes. For memory-heavy HPC tasks such as simulations, scientific research, and AI applications, the H200’s superior memory bandwidth allows for quicker access and manipulation of data. This results in up to 110X faster outcomes compared to traditional CPU-based systems.
Lower Energy Consumption and Total Cost of Ownership
The H200 sets a new standard for energy efficiency and total cost of ownership (TCO). Delivering exceptional performance within the same power envelope as the H100, this advanced technology enables AI data centers and supercomputing systems to achieve faster speeds while becoming more environmentally friendly. The result is a significant economic advantage, driving progress in both the AI and scientific sectors.
NVIDIA A800 GPU
The ultimate workstation development platform for data science and HPC.
Bring the power of a supercomputer to your workstation and accelerate end-to-end data science workflows with the NVIDIA A800 40GB Active GPU. Powered by the NVIDIA Ampere architecture, the A800 40GB Active delivers powerful compute, high-speed memory, and scalability, so data professionals can tackle their most challenging data science, AI, and HPC workloads.
Nvidia L40 GPU
Unprecedented visual computing performance for the data center.
The NVIDIA L40, powered by the Ada Lovelace architecture, delivers revolutionary neural graphics, virtualization, compute, and AI capabilities for GPU-accelerated data center workloads.
Software Tools for GPU Computing
Tensorflow Artificial Intelligence Library
Tensorflow, developed by google, is an open source symbolic math library for high performance computation. It has quickly become an industry standard for artificial intelligence and machine learning applications, and is known for its flexibility, used in many scientific disciplines. It is based on the concept of a Tensor, which, as you may have guessed, is where the Volta Tensor Cores gets its name.
GPU Accelerated Libraries
There are a handful of GPU accelerated libraries that developers can use to speed up applications using GPUs. Many of them are NVIDIA CUDA libraries (such as cuBLAS and CUDA Math Library), but there are others such as IMSL Fortran libraries and HiPLAR (High Performance Linear Algebra in R). These libraries can be linked to replace standard libraries that are commonly used in non-GPU-Accelerated computing.
CUDA Development Toolkit
NVIDIA has created an entire toolkit devoted to computing on their CUDA-enabled GPUs. The CUDA toolkit, which includes the CUDA libraries, are the core of many GPU-Accelerated programs. CUDA is one of the most widely used toolkits in the GPGPU world today.
NVIDIA Deep Learning SDK
In today’s world, Deep Learning is becoming essential in many segments of the industry. For instance, Deep Learning is key in voice and image recognition where the machine must learn while gaining input. Writing algorithms for machines to learn from data is a difficult task, but NVIDIA has written a Deep Learning SDK to provide the tools necessary to help design code to run on GPUs.
OpenACC Parallel Programming Model
OpenACC is a user-driven directive-based performance-portable parallel programming model. It is designed for scientists and engineers interested in porting their codes to a wide-variety of heterogeneous HPC hardware platforms and architectures with significantly less programming effort than required with a low-level model. . The OpenACC Directives can be a powerful tool in porting a user’s application to run on GPU servers. There are two key features to OpenACC: easy of use and portability. Applications that use OpenACC can not only run on NVIDIA GPUs, but it can run on other GPUs, X86 CPUs & POWER CPUs, as well.
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