Vultr offers a wide range of Bare Metal GPU options to meet different user needs. These powerful GPUs help users handle demanding tasks such as machine learning, artificial intelligence, 3D rendering, and complex simulations. With Vultr's bare metal resources, developers can create and launch new applications more effectively, benefiting from direct access to dedicated hardware for maximum performance and control.
This article explains the six different Vultr Bare Metal GPU offerings: A100 Tensor Core (PCIe and SXM), L40S, H100 Tensor Core, GH200 Superchip, and AMD Instinct™ MI300X. You will gain an understanding of the core differences between these GPU variants, helping you make the best choice for a specific workload.
This section provides the necessary technical and performance data to compare the 6 different GPUs based on their CUDA Cores/Stream Processors, Tflops performance, parallel processing.
Below are the stats of the different Bare Metal GPUs Vultr offers to compare performances:
| Variant | CUDA Cores/Stream Processors | Tensor/Matrix Cores | TF32 Tflops with Sparsity | GPU Memory | Architecture Type |
|---|---|---|---|---|---|
| NVIDIA A100 Tensor Core GPU - PCIe | 6912 | 432 | 312 | 80 GB | NVIDIA Ampere |
| NVIDIA A100 Tensor Core GPU - SXM | 6912 | 432 | 312 | 80 GB | NVIDIA Ampere |
| NVIDIA L40S GPU | 18716 | 568 | 366 | 48 GB | NVIDIA ADA Lovelace |
| NVIDIA H100 GPU | 18432 | 640 | 989 | 80 GB | NVIDIA Hopper |
| NVIDIA GH200 Superchip | 18432 | 640 | 989 | 80 GB | NVIDIA Hopper |
| AMD Instinct™ MI300X GPU | 19456 | 1216 | 1307.4 | 192 GB Per Accelerator | AMD CDNA 3 |
CUDA Cores: These are specific type of processing unit designed to work with NVIDIA's CUDA programming model, they play a fundamental role in parallel processing and accelerating various computing tasks focused on graphics rendering. They often use a Single Instruction, Multiple Data (SIMD) architecture so that a single instruction is executed simultaneously on multiple data elements, resulting a high throughput in parallel computing.
Stream Processors: These are the core computational units in AMD GPUs, designed to handle parallel processing tasks such as graphics rendering, general-purpose computation, and AI workloads. Stream Processors also operate on a Single Instruction, Multiple Data (SIMD) architecture, where a single instruction is executed across multiple data points simultaneously.
Tensor Cores: These are specialized hardware component in the NVIDIA GPUs made for accelerating matrix-based computations that are commonly used in deep learning and many artificial intelligence workloads. They are optimized for mathematical operations involved in neural network training and inference by taking advantage of their mixed precision computing, where certain part of the calculations with higher precision and the rest with half precision while maintaining the accuracy in results by using error correction and accumulation.
Matrix Cores: These are specialized hardware units integrated into AMD's Instinct™ GPUs, specifically designed to accelerate matrix-based computations central to artificial intelligence (AI) and high-performance computing (HPC). Optimized for mixed-precision arithmetic, Matrix Cores handle operations like matrix multiplications at exceptional speeds by leveraging data types such as FP16, BF16, and INT8.
Tflops: Also known as TeraFLOPS, used to quantify the performance of a system in floating-point operations per second, it involves floating-point operation involving mathematical calculations using numbers with decimal points. It is a useful indicator for comparing the capabilities of different hardware components. High-performance computing applications like simulations heavily rely on Tflops.
NVIDIA A100 Tensor Core - PCIe: Vultr Bare Metal GPU, powered by the NVIDIA A100 Tensor Core in PCIe format, is suitable for tasks such as AI training and deep learning. This version connects through the PCIe interface, making it compatible with many server setups. With its strong processing capabilities and high memory bandwidth, it helps businesses efficiently manage large datasets and complex models while supporting scalable infrastructure. Learn more about Vultr Bare Metal A100.
NVIDIA A100 Tensor Core - SXM: Vultr Bare Metal GPU, powered by the NVIDIA A100 Tensor Core in SXM format, is optimized for high-performance AI and data science workloads. This configuration allows for better power delivery and thermal management, leading to enhanced performance. It is ideal for training large neural networks and running extensive computational tasks, helping organizations improve their efficiency and output. Learn more about Vultr Bare Metal GPU A100.
NVIDIA L40S: Vultr Bare Metal GPU, powered by the NVIDIA L40S, is built for advanced graphics and AI applications. It offers strong performance for tasks like 3D rendering, AI inference, and video processing. With its high memory capacity and efficient processing, the L40S helps businesses deliver detailed graphics and handle complex data tasks effectively. Learn more about Vultr Bare Metal GPU L40S.
NVIDIA H100 Tensor Core: Vultr Bare Metal GPU, powered by the NVIDIA H100, is designed for next-generation AI workloads, including large language model training and inference. With advanced architecture and powerful tensor cores, the H100 enables organizations to achieve higher performance and efficiency for demanding applications. It is ideal for research and complex tasks that require significant computational resources. Learn more about Vultr Bare Metal GPU H100.
NVIDIA GH200 Superchip: Vultr Bare Metal GPU, powered by the NVIDIA GH200 Superchip, combines multiple GPU components for extreme performance in AI and high-performance computing. This design allows for enhanced scalability and efficiency, making it suitable for complex tasks that demand substantial computing power. It is ideal for large-scale AI deployments, scientific simulations, and data analytics. Learn more about Vultr Bare Metal GH200 Superchip.
AMD Instinct™ MI300X: Vultr Bare Metal GPU, powered by the AMD Instinct™ MI300X, is designed for cutting-edge AI and high-performance computing (HPC) applications. With advanced memory bandwidth and support for large-scale models, the MI300X delivers exceptional performance for training and inference in AI and deep learning tasks. This GPU is ideal for organizations seeking scalable and efficient solutions for complex data processing and scientific simulations. Learn more about Vultr Bare Metal AMD Instinct™ MI300X.