At HorizonIQ, we’ve integrated advanced AI technologies into our infrastructure to achieve exceptional performance, scalability, and efficiency. Combining NVIDIA GPUs with Intel processors provides the ideal solution for data center AI workloads.
While there are many options, let’s discuss why the NVIDIA L40S is the best GPU for AI. We’ll also examine why the combination of Intel Xeon Gold 6336Y and the L40S is optimal for AI training and inference, what makes it a cost-efficient option, and how a security solutions provider is enhancing public safety with GPU and CPU-powered AI.
Why is the NVIDIA L40S GPU a Game-Changer for AI?
The NVIDIA L40S GPU is exceptional for AI applications due to its combination of advanced features and cost-effectiveness. It supports the Transformer Engine, providing high performance for AI inference tasks.
The L40S offers competitive performance at a significantly lower price than the H100, making it an attractive option for large-scale deployments. Here are some of its standout features:
- AI Training and Inferencing: The L40S is significantly improved over the L40, supporting AI clusters and leveraging the NVIDIA Transformer Engine for FP8 precision. This drastically reduces the size of data and memory bandwidth requirements.
- Visualization: Heavy on video encoding/decoding, making it ideal for vGPU workloads and real-time graphics applications.
- Versatility: Suitable for both AI and general-purpose computing tasks, including those requiring high-quality rendering and graphics.
Key Features of the L40S
- NVIDIA Transformer Engine and FP8: Reduces data size and memory bandwidth requirements, optimizing performance for AI workloads.
- Video Encoding/Decoding: Enhanced capabilities for visualization tasks, supporting a wide range of graphics applications.
- Cost-Effectiveness: Offers a balanced performance at a more accessible price point than other high-end GPUs.
| Feature | Details |
| GPU Memory | Optimized with FP8 precision |
| Video Encoding/Decoding | Advanced capabilities for real-time applications |
| Versatility | Ideal for AI, graphics, and general-purpose computing |
| NVIDIA Transformer Engine | Supports FP8 for efficient AI performance |
Why Consider a CPU and GPU Setup for AI Applications?
AI has traditionally been dominated by GPUs for their ability to handle massive parallel computations. However, this approach comes with high costs and some limitations. CPUs, like Intel’s Xeon processors, offer an alternative that can complement GPUs to create a more efficient and versatile computing environment.
In particular, the Intel Xeon Gold 6336Y processor is renowned for its high performance, advanced features, and scalability. It boasts:
- 24 cores and 48 threads for multitasking.
- AVX-512 for handling complex data sets.
- Intel DL Boost for enhanced deep learning performance.
- A generous 36 MB cache, and supports up to 6 TB of DDR4-3200 memory for quick data access.
While the L40S GPU excels at handling complex inference, the Intel Xeon processors are great for CPU-bound aspects of AI workloads, such as data preprocessing and managing the training process. Here’s why Intel’s Xeon processors stand out:
| Reason | Description |
| Built-in AI Capabilities | Intel’s Xeon processors come with built-in AI acceleration, enabling them to handle tasks traditionally GPU-centric. |
| Cost-Effectiveness | Running deep learning tasks on CPUs can be more cost-effective compared to GPUs, especially for training models. |
| Handling Massive Data Volumes | CPUs excel in explorative tasks such as data processing, analysis, and visualization, which can be expensive on GPUs. |
| Avoiding Ecosystem Lock-in | Intel workstations do not rely on NVIDIA’s CUDA ecosystem, offering a more flexible and open approach. |
Customization and Scalability
Our chassis supports up to 2 NVMe drives for high-speed storage and offers dual 10 Gbps Ethernet ports for rapid network connectivity. The Supermicro system features configurable memory ranging from 128 GB to 6 TB and accommodates up to two NVIDIA L40S GPUs for powerful computing and AI tasks.
It includes:
- 4 PCIe 4.0 x8 FHHL slots (which can be combined into 2 PCIe 4.0 x16 slots).
- 2 PCIe 4.0 x16 FHHL slots, and 2 PCIe 3.0 x2 NVMe M.2 slots.
- Utilized with the Intel C621A Chipset, it has a 2U Rackmount form factor and uses the Super X12DDW-A6 motherboard—designed for dual Socket P4 (LGA-4189) Intel Xeon Scalable processors.
Pro tip: Additional drives can be added on a case-by-case basis. These deployments can be launched at any of our London, Amsterdam, Chicago, New Jersey, Seattle, Singapore, Dallas, Phoenix, and Silicon Valley data centers.
Why is the Combination of NVIDIA L40S and Intel Xeon Gold 6336Y Processors the Right Choice for AI?
Performance Synergy
The high core count and memory bandwidth of Intel Xeon Gold 6336Y processors complement the parallel processing capabilities of NVIDIA L40S GPUs, providing optimal performance for training and inference tasks.
Scalability and Flexibility
Both NVIDIA L40S GPUs and Intel Xeon Gold 6336Y processors are designed for scalability, allowing organizations to customize their infrastructure according to their needs. This flexibility is crucial for handling the growing demands of AI workloads.
Cost Efficiency
We provide our customers with a cost-efficient solution by integrating these components into our bare metal infrastructure. The performance gains from using L40S GPUs and Xeon processors reduce the need for additional hardware, lowering overall costs.
The combination of deploying a single Intel Xeon Gold 6336Y processor and NVIDIA L40S would cost approximately $1384 per month—depending on the specific configurations and locations chosen.
In comparison, the same local setup would cost you around $16k to $27k—plus the price of installation, storage, hardware, cooling, and other expenses.
Case Study: HorizonIQ Helps IREX Enhance Public Safety with GPU-Powered AI
HorizonIQ has recently upgraded IREX’s private cloud by integrating advanced GPU nodes, creating a secure and controlled environment for real-time weapon detection. This approach boosts data security and privacy and supports larger neural networks to significantly improve the accuracy and range of IREX’s weapon detection technology.
Key Benefits:
- Enhanced Security: Increased data security and privacy.
- Improved Performance: Larger neural networks for better detection accuracy and faster response times.
The solution also includes CPU video analytics on Intel and AMD processors to offer a cost-effective alternative to relying solely on GPUs. This hybrid approach reduces operational costs and makes it possible for IREX to serve overseas clients with limited budgets or infrastructure.
Hybrid Approach Advantages:
- Cost-Efficiency: Lower operational costs by using both CPUs and GPUs.
- Global Reach: Support for clients without extensive GPU infrastructure.
IREX can now deliver high-performance AI applications that are flexible, scalable, and optimized for cost-efficiency in enhancing public safety solutions.
Why Choose HorizonIQ?
The combination of NVIDIA L40S GPUs and Intel Xeon Gold 6336Y processors balances performance, scalability, and cost-efficiency for data center AI workloads. At HorizonIQ, we’re committed to leveraging these technologies to drive innovation and deliver superior cost savings for our customers.
Ready to accelerate your AI capabilities? Explore our NVIDIA GPUs or contact us today to find out how NVIDIA L40S GPUs and Intel Xeon Gold 6336Y processors can transform your business.
