HPE NVIDIA L4 24GB PCIe GPU Accelerator

Brand: HPE | Category: GPUs

SKU: P53867-B21 | Part #: P53867-B21 | MPN: P53867-B21

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The HPE NVIDIA L4 24GB PCIe GPU Accelerator delivers 59.4 TFLOPS at FP32, 118.8 TFLOPS at BF16, and 237.6 TFLOPS at FP8/INT8—the three critical precision points for modern AI inference and machine learning workloads. Built on the NVIDIA Ada Lovelace architecture, this compute-focused accelerator pairs 7,680 CUDA cores with 4th-generation Tensor Cores and 3rd-generation RT Cores to handle demanding inference pipelines at enterprise scale. The 24 GB GDDR6 memory subsystem with 192-bit interface and 300 GB/s memory bandwidth ensures rapid data movement for batch processing scenarios. At just 72 W thermal design power, the HPE NVIDIA L4 (part number P53867-B21) operates efficiently in dense server deployments without excessive cooling overhead.

HPE engineered this full-height, full-length, single-slot accelerator for seamless integration into standard PCIe Gen 4 x16 server slots, eliminating costly infrastructure redesign. Support for Multi-Instance GPU (MIG) partitioning up to 7 instances, NVIDIA vGPU licensing, ECC memory protection, and GPU Direct RDMA capabilities make it ideal for virtualized inference clusters and high-availability production environments. The accelerator operates reliably across a 0°C to 35°C temperature range with no display outputs—a pure compute device engineered for headless deployment. For IT infrastructure teams, network engineers, and AI operations teams evaluating inference acceleration, HPE delivers a power-efficient, standards-based solution that scales across mixed workload clusters. Contact Omnixon Global to request specifications and availability for the HPE NVIDIA L4 24GB PCIe GPU Accelerator (P53867-B21).

Typical Enterprise Deployment Scenarios

• Multi-tenant inference serving with MIG partitioning for containerized AI services across Kubernetes clusters
• Real-time recommendation engines and natural language processing inference at scale with virtualization support
• GPU-accelerated data analytics pipelines leveraging GPU Direct RDMA for low-latency inter-node communication
• High-density server deployments where 72 W per-accelerator power consumption enables 10+ GPUs per rack without thermal strain
• Mission-critical inference workloads requiring ECC memory protection and enterprise-grade reliability in production environments