The NVIDIA L4 24GB is a professional GPU accelerator designed for server and datacenter environments, focused on AI inference, GPU virtualization, video processing, and modern cloud workloads. Based on a recent and highly energy-efficient architecture, it offers an excellent balance between power, memory, and consumption, ideal for large-scale deployments.
General Description
The NVIDIA L4 stands out for its compact form factor, low power consumption, and high versatility. It is particularly used for:
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AI model inference (LLMs, vision, recommendation),
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video transcoding,
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application and desktop virtualization,
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cloud services requiring efficient and reliable GPUs for continuous operation.
This is not a gaming card, but an infrastructure GPU optimized for power efficiency.
Main Technical Specifications
GPU & Architecture
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GPU Architecture: Recent generation NVIDIA datacenter-oriented
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CUDA Cores: 7,424
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Tensor Cores: AI-dedicated cores for inference and model acceleration
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RT Cores: Present for visualization and certain professional graphics workloads
Memory
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Video Memory: 24 GB GDDR6 with ECC
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Memory Bus: 192-bit
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Memory Bandwidth: approximately 300 GB/s
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ECC memory ensuring data reliability in critical environments
Interface & Form Factor
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System Interface: PCI Express 4.0 x16
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Form Factor: single-slot, low profile / full height depending on version
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Cooling: passive, designed for well-ventilated server chassis
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Video Outputs: none (compute accelerator)
Performance & Features
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Optimized for AI inference (INT8, FP16, BF16)
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Hardware video processing acceleration (encoding/decoding, multi-stream)
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Support for GPU virtualization (vGPU)
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Compatible with CUDA, NVIDIA AI Enterprise ecosystems and cloud solutions
Consumption
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TDP (Thermal Design Power): approx. 72 W, extremely low for a datacenter GPU
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Excellent energy efficiency, ideal for dense and continuous deployments (24/7)
For Whom?
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Data centers & cloud providers
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AI / MLOps Developers (large-scale inference)
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Enterprises using graphic or application virtualization
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Streaming, VDI, video transcoding, edge computing
What it brings to a system
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Very high power efficiency
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Low thermal dissipation, facilitating server integration
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High reliability thanks to ECC memory
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High scalability for AI and modern cloud services
