S0K89C HPE Nvidia L4 24GB PCIe Accelerator

HPE S0K89C Nvidia L4 24GB PCIe Accelerator Overview

The HPE S0K89C Nvidia L4 24GB PCIe Accelerator category encompasses high-performance GPU compute solutions engineered for demanding AI, inference, and edge computing workloads. These accelerators combine HPE’s robust server integration and Nvidia’s advanced GPU architecture to offer a powerful solution for deployment in data centers, AI inference services, and high-throughput computing environments. Within this category you will find variants, accessories, supporting infrastructure, and sub-components that complement or extend the capabilities of the HPE S0K89C Nvidia L4 family.

Main Feature

• Brand : HPE 
• Model : S0K89C
• Product Type : Graphics Card

Key Specifications

• Product Type : Graphics Card
• Memory Size Per Board 24 Gb Gddr6
• Memory Bandwidth For Board 300 Gb/s
• Number Of Accelerators Per Card 1

Technical Feature

Each product in this category must meet or exceed the following baseline specifications. These specifications serve as the foundation of what distinguishes the HPE S0K89C Nvidia L4 24GB PCIe Accelerator from more general GPU or PCIe accelerator lines.

GPU Memory and Bandwidth

The defining feature is the 24 GB of GPU memory, which enables large model inference, multi-tenant workloads, and high throughput applications. The memory interface and bandwidth capabilities support rapid data transfers essential for deep learning inference. Typical bandwidth reaches into the hundreds of gigabytes per second range, enabling the accelerator to feed data into the GPU cores without bottlenecks.

Compute and Tensor Cores

The L4 architecture includes specialized tensor cores and CUDA cores optimized for mixed precision, INT8, INT4, and FP16 operations. These cores accelerate neural network inference, transformer models, computer vision pipelines, and speech recognition tasks. Efficiency in throughput per watt is a hallmark of this architecture.

PCIe Interface and Form Factor

The PCIe interface (typically PCIe Gen4 x16) ensures compatibility with server motherboard slots in HPE systems and other industry-standard chassis. The form factor is usually a full-height, full-length accelerator card, sometimes with dual-slot cooling solutions. Efficient thermal design and power delivery are essential for sustained performance under heavy workloads.

Power and Thermal Design

Expect a power draw in the range of 150W to 300W (depending on mode and workload), with cooling via active fans or server airflows. The design must support thermal dissipation under heavy load, ensuring sustained performance without thermal throttling.

Base Accelerator Modules

This subcategory includes the straight accelerator cards without extras. These are ideal for users who already have server infrastructure and only need to add the GPU compute module. They come with factory firmware and standard warranties, ready to slot into supported servers.

Accelerator Bundles

Bundled packages combine the accelerator with thermal plates, optional backup modules, or cabling solutions. These bundles simplify deployment, by including all ancillary parts in one package. OEM-certified bundles may also include firmware compatibility checks and integration validation with HPE servers.

Power and Power Delivery Components

To supply reliable power, this subcategory covers PCIe auxiliary power cables, redundant power modules, power splitters, and voltage regulators. These components help ensure stable power delivery to the GPU under peak loads.

Firmware, Drivers, and Software Support Packages

GPU firmware updates, driver packages, and validated software stacks (such as Nvidia TensorRT, CUDA, MIG, and other toolkits) are essential for performance, compatibility, and security. This subcategory provides downloadable or kit-based support modules.

Accessory and Adapter Modules

Adaptors, riser cards, bracket mounting kits, retention frames, and GPU extension cables fall into this subcategory. They support nonstandard chassis or custom deployment setups where direct insertion is not feasible.

Performance and Use Cases

The HPE S0K89C Nvidia L4 24GB PCIe Accelerator category targets high throughput inference, edge deployment, virtualization, and AI inferencing tasks. Below are key use cases and performance expectations:

Multi-Tenant Hosting and Virtualization

Users can deploy multiple isolated inference environments or containers on a single card, leveraging GPU partitioning (MIG-like features). This enables cloud providers and service hosts to maximize utilization across workloads.

Edge Deployment and AI at the Edge

In deployments where edge servers require GPU compute, the L4 24GB accelerator offers a balance of performance and power efficiency. Its compact footprint and moderate power envelope make it suitable for telecom edge, retail edge AI, and remote data processing sites.

High Throughput Computing and Analytics

While not designed for full HPC floating-point workloads like FP64, the accelerator can handle analytics pipelines, inference-augmented ETL, real-time data processing, and streaming data workloads that benefit from GPU acceleration.

Feature

Several features set this category apart. These capabilities increase usability, reliability, and integration in enterprise environments. Here are some of the highlights:

Multi-Instance GPU (MIG) and Partitioning

The architecture supports splitting the GPU into multiple instances, enabling better resource isolation. Each instance can be assigned to a separate job or container, maximizing utilization while maintaining performance guarantees.

Low-Latency and Real-Time Inference

Optimized driver stacks and firmware reduce latency in model execution paths. This is critical for real-time applications like speech recognition, video analytics, or autonomous decision systems.

Memory Optimization and GDDR Efficiency

Memory management techniques, compression, and high bandwidth ensure efficient handling of large model weights, embeddings, and activations. The 24 GB buffer is adequate for mid- to large-scale transformer models and multi-model workloads.

Scalable Multi-Card Deployment

The category supports configurations with multiple cards in a server, allowing scalable inference clusters. Interconnects, NVLink (if supported in certain models), and shared memory paths enable coordinated work across multiple accelerators.

Firmware Update and Security

Firmware packages ensure secure boot, version control, and compatibility with HPE ecosystem updates. Secure firmware helps protect against vulnerabilities and ensures stability.

Compatibility and Integration

Choosing an HPE S0K89C Nvidia L4 24GB PCIe Accelerator means ensuring compatibility across server chassis, BIOS, drivers, power, cooling, and software stacks. Below are key integration considerations:

Server Chassis and Slot Clearance

Confirm that the server chassis supports a full-length PCIe card and provides clearance for the cooling solution. Some enclosures may require modified airflow paths or shrouds. The accelerator should align with HPE server backplanes and mechanical guidelines.

BIOS and Firmware Compatibility

Matching server BIOS versions and firmware updates is crucial. The accelerator may require specific BIOS settings for PCIe bifurcation, ACS capability, or I/O memory mapping. HPE certified BIOS versions are often published alongside accelerator support notes.

Driver and CUDA Stack Versions

The proper Nvidia driver, CUDA toolkit, and inference libraries (TensorRT, cuDNN) must be installed and validated. Operating system (Linux, Windows Server) compatibility should also be checked. Version interlocks matter; mismatches can lead to reduced performance or instability.

Power Supply and Cabling

The power supply must have sufficient capacity for the card’s maximum draw plus headroom for other system components. Check that auxiliary PCIe power connectors meet voltage and current requirements. Stable cabling and clean power rails are essential for consistent operation.

Cooling and Airflow Paths

The server should provide front-to-back or bottom-to-top airflow according to the card’s thermal design. Cooling kits may be needed if ambient temperature or airflow is suboptimal. Heat sinks and shrouds should not block adjacent slots.

Deployment Scenarios and Examples

To help visualize how products in this category might be deployed, here are several scenario examples:

Inference Server for NLP API Hosting

A cloud provider could deploy multiple HPE S0K89C Nvidia L4 24GB accelerators in HPE ProLiant rack servers to host NLP APIs. Each card could serve multiple inference threads in a multi-tenant setup, partitioned using MIG. High throughput and low latency inference allow real-time response to chat, translation, or summarization requests.

Video Analytics at the Edge

In a stadium or retail campus, compute nodes equipped with these accelerators can handle real-time video streams, object detection, facial recognition, and event triggering. The small footprint and robust integration simplify deployment in constrained edge racks.

Driver and Firmware Updates

Apply firmware and driver updates as released to maintain performance, security, and compatibility. Engage vendor support to follow validated version paths for your firmware and server combination.

Resource Partitioning with MIG or Equivalent

If partitioning features are supported, segment the GPU into multiple instances to serve multiple workloads simultaneously. Ensure fairness, performance isolation, and workload balancing across partitions.

Comparison with Related Categories

It’s useful to compare this category with alternative GPU or accelerator categories to choose the right fit:

General GPU Cards (e.g. Gaming / Consumer GPUs)

Consumer GPU cards may offer raw compute but lack enterprise-grade features: firmware reliability, dual-port cooling, driver validation, support contracts, and thermal management in server racks. They often cannot sustain full utilization under continuous AI inference loads.

Higher Memory Accelerators (e.g. 40 GB / 80 GB Data Center GPUs)

Higher memory GPUs might benefit large model training or extreme workloads, but they come at higher cost, power, and cooling demands. The 24 GB L4 is positioned for inference efficiency, delivering better cost per inference in many use cases. If your workload absolutely requires 40 GB+ for model residency, this category might not suffice.

Lower Capacity Inference Accelerators (e.g. 8/16 GB Accelerators)

Lower capacity accelerators may suffice for smaller models or edge deployments, but they limit scalability, batch sizes, and multi-model hosting. The 24 GB tier allows more flexibility, improved throughput, and support for more complex AI workloads without memory bottlenecks.

TPU / ASIC Inference Devices

While specialized inference ASICs (e.g. custom TPUs) may outperform GPU accelerators in specific workloads, they often lack the generality, ecosystem support, and flexibility that Nvidia’s software stack provides. The HPE S0K89C Nvidia L4 24GB category remains advantageous for mixed workloads and evolving AI pipelines.