Machine Learning & AI Workstation System Application Recommendations

Machine Learning & AI System Application Requirements

• Regression models such as non-neural network classifiers
• Statistical models Such as Python SciKitLearn and the R language
• Deep Learning models with frameworks like PyTorch and TensorFlow

Workstation hardware is recommended for programming model training workflow compared to inference workflow.

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CPU Recommendation

CPU is the main computing engine, while GPU has limitations for onboard memory (VRAM) availability. But GPU acceleration is important for performance.

We recommend single-socket CPU Intel Xeon W workstations to address mapping memory to multiple GPUs. The number of CPU cores chosen will depend on the expected load for non-GPU tasks. Intel CPUs are better than AMD processors.

Recommended Graphics card for machine learning Workstation and AI Workstation

GPU Recommendation

Sorted by the importance of each GPU component.

1. Tensor Cores
2. memory bandwidth GPU

 

Tensor Cores

Tensor Cores are tiny cores that perform matrix multiplication which is part of any deep neural network.

Memory Bandwidth

The following shared memory sizes based on the following architectures:

• Volta (Titan V): 128kb shared memory / 6 MB L2
• Turing (RTX 20s series): 96 kb shared memory / 5.5 MB L2
• Ampere (RTX 30s series): 128 kb shared memory / 6 MB L2
• Ada (RTX 40s series): 128 kb shared memory / 72 MB L2

Geforce RTX Vs Professional RTX ADA:

Consumer graphics cards such as NVIDIA’s GeForce RTX 4070 Ti, RTX 4080, and 4090 are good for performance and price optimization.

Professional NVIDIA GPUs such as RTX 5000 Ada and RTX 6000 Ada with more onboard memory are necessary for larger data problems and work well in multi-GPU environments but are expensive.

RAM Recommendation

1. Based on GPU VRAM:

Depending on the AI jobs being run on the CPU side of an ML/AI system with at least more than double the amount of CPU memory.

GeForce RTX 4070ti GPUs would have 12GB of total VRAM so the workstation should be configured with 64GB

GeForce RTX 4090 GPUs would have 48GB of total VRAM so the workstation should be configured with 128GB

2. Based on data analysis:

Sometimes able to pull a full data set into memory for processing and statistical work so as much as 1TB system memory is required. So, we advise using workstation processors.

Storage Recommendation

If data streaming speeds are large then recommend using NVMe HDD.

SATA-based SSDs offer higher capacity to store data at a lesser cost for staging jobs

Platter drives can be used for archival storage and larger data sets of more than 20TB data.

Workstation System Recommendation

	Single GPU Workstation
	Optimized for generative vision models and ML and AI development work
CPU	Intel Xeon w7-3455 Processor (67.5M Cache, 2.50 GHz)
GPU(s)	NVIDIA GeForce RTX 4080 SUPER 16GB
RAM	64GB DDR5 4800 REG ECC (2x32GB)
Features	Single, powerful video card Up to 192GB of RAM

 

Intel oneAPI:

Intel oneAPI is a massive collection of very high-quality developer tools, and, it’s free to use for AI developers.

TensorFlow:

TensorFlow is an open-source software library for numerical computation using data flow graphs and has become one of the standard frameworks for machine learning.

Global Nettech HP, DELL Workstations on Rental:

Global Nettech specializes in providing best of class graphics workstations designed to accelerate your CAD design or visual effects or AI/ML workflow. Rental from Global Nettech carry the industries top brands like Dell, HP, Lenovo and Apple. Global Nettech is suited to cater the rental needs of your company wherever you are.

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