699-2G193-0200-210 Nvidia 24GB GDDR6 PCI Express Graphics Card.

Nvidia 699-2G193-0200-210 24GB GDDR6 Graphics Card

Engineered for efficient AI and visual computing in space-constrained environments, the NVIDIA 699-2G193-0200-210 L4 GPU delivers robust performance with a fanless, low-profile design. This computing processor excels in data center inference, virtualization, and media streaming workloads.

Product Overview & Key Features

• Manufacturer: NVIDIA
• Part Number: 699-2G193-0200-210
• Core Architecture: NVIDIA L4 Tensor GPU
• Form Factor: Low-Profile, Single-Slot, Fanless
• Primary Use: AI Inference, Video Transcoding, Virtual Desktop Infrastructure (VDI)

Detailed Technical Specifications

Processing Engine & Clock Speeds

• Graphics Processing Unit (GPU): NVIDIA L4 Tensor Core
• Base Clock Frequency: 795 MHz
• Maximum Boost Clock: Up to 2040 MHz for dynamic performance
• Host Interface: PCI Express 4.0 x16 bus

High-Speed Memory Configuration

• Video RAM Capacity: 24GB
• Memory Type: High-bandwidth GDDR6 SDRAM
• Memory Data Rate: Effective speed of 6.251 Gbps
• Memory Bus: 192-bit interface
• Aggregate Memory Bandwidth: 300 GB/s

Power Efficiency & Thermal Design

• Typical Board Power (TBP): A highly efficient 75 Watts
• Cooling Solution: Passive heatsink for silent, maintenance-free operation
• Thermal Output: Low heat generation, ideal for dense server deployment

Physical Dimensions & Compliance

Form Factor & Measurements

• Card Length: 16.854 cm (6.64 inches)
• Card Height: 6.809 cm (2.68 inches) - Low-Profile Bracket
• Slot Requirement: Single PCIe slot occupancy

Operating Environment & Reliability

• Safe Operating Temperature: 0°C to 50°C
• Humidity Range (Operating): 5% to 85% relative humidity (non-condensing)
• Designed For: Data center and enterprise operational conditions

Nvidia 699-2G193-0200-210 24GB Graphics Card Overview

The Nvidia 699-2G193-0200-210 24GB GDDR6 PCI Express 4.0 x16 Fanless Graphics Card belongs to a specialized category of enterprise-class computing processors designed for high-performance data center workloads, accelerated computing environments, and professional-grade AI, analytics, and virtualization infrastructures. This category focuses on reliability, scalability, deterministic performance, and compatibility with modern server architectures rather than consumer-oriented graphics rendering. GPUs in this segment are engineered to operate continuously under sustained loads, supporting mission-critical applications that demand stability, thermal efficiency, and predictable throughput.Within enterprise IT ecosystems, computing processors such as this Nvidia model are deployed as acceleration engines for complex computational tasks. These include deep learning inference, high-performance computing simulations, data analytics pipelines, and virtual desktop infrastructure acceleration. The category emphasizes optimized memory architecture, high-bandwidth interconnects, and firmware-level integration with server platforms, enabling organizations to maximize return on investment through improved processing density and reduced power overhead.

Positioning Within Nvidia Data Center GPU Architecture

This graphics card category is aligned with Nvidia’s data center GPU lineage, which prioritizes compute capability, memory capacity, and software ecosystem compatibility. The 24GB GDDR6 configuration places this product class in a tier optimized for balanced workloads where memory-intensive applications and compute parallelism intersect. Unlike consumer GPUs that emphasize rasterization and display outputs, this category is purpose-built for headless operation, allowing system integrators to deploy dense GPU clusters without unnecessary components.The PCI Express 4.0 x16 interface defines the connectivity standard of this category, delivering high throughput between the GPU and host CPU. This is particularly critical in data center environments where data movement efficiency directly impacts application performance. By leveraging PCIe Gen4 bandwidth, this class of computing processor ensures minimal bottlenecks during large dataset transfers, model inference requests, and real-time analytics workloads.

Fanless Thermal Design Philosophy

A defining characteristic of this category is the fanless thermal design. Fanless GPUs are engineered to integrate seamlessly into server chassis that rely on controlled front-to-back airflow. Rather than using onboard fans, thermal dissipation is managed through precision-engineered heat sinks and optimized airflow paths within the server enclosure. This design philosophy reduces mechanical failure points, enhances long-term reliability, and aligns with enterprise maintenance standards.Fanless computing processors are particularly valuable in hyperscale and colocation data centers where uniform cooling strategies are essential. By eliminating localized fan noise and vibration, this category supports higher rack density and improved overall thermal management. The Nvidia 699-2G193-0200-210 model exemplifies this approach, making it suitable for continuous 24/7 operation in controlled environments.

Optimized for Server-Class Cooling Architectures

Server-class cooling architectures are designed around predictable airflow patterns, and GPUs in this category are manufactured to operate efficiently within those parameters. The heat sink design, component placement, and PCB layout all contribute to uniform heat distribution. This allows data center operators to maintain consistent temperature profiles across racks, reducing hotspots and improving energy efficiency.

By aligning with standard server cooling methodologies, this category minimizes the need for custom thermal solutions. This simplifies deployment, reduces operational complexity, and ensures compatibility with a wide range of enterprise server platforms from leading OEMs.

Memory Architecture and Data Throughput Capabilities

The 24GB GDDR6 memory configuration is a core attribute of this category, providing ample capacity for memory-intensive workloads. GDDR6 memory delivers high bandwidth and low latency, enabling efficient handling of large datasets, complex neural networks, and multi-threaded compute operations. This memory architecture is particularly beneficial for inference workloads, where rapid access to model parameters is essential for maintaining low response times.

In enterprise environments, memory capacity directly influences the scale and complexity of workloads that can be processed on a single GPU. This category supports consolidation of multiple tasks onto fewer hardware resources, reducing infrastructure footprint and operational costs. The balance between memory size and bandwidth ensures that applications can scale effectively without being constrained by data access limitations.

Parallel Computing and AI Workloads

Parallel computing is a defining use case for this category of Nvidia computing processors. By leveraging thousands of compute cores and high-speed memory access, these GPUs accelerate workloads that would otherwise require extensive CPU resources. AI inference, machine learning model deployment, and real-time analytics benefit significantly from this parallelism, enabling faster decision-making and improved user experiences.

This category is also optimized for compatibility with Nvidia’s CUDA ecosystem, allowing developers to leverage mature libraries, frameworks, and toolchains. This software alignment enhances productivity and ensures that organizations can deploy GPU-accelerated applications with confidence and consistency.

Enterprise AI and Inference Acceleration

AI inference workloads demand predictable latency and efficient resource utilization. GPUs in this category are designed to meet these requirements by providing deterministic performance under sustained loads. The 24GB memory capacity supports larger models and batch processing, making it suitable for natural language processing, computer vision, and recommendation systems deployed in production environments.By integrating seamlessly with popular AI frameworks, this category enables organizations to transition from model training to deployment without significant architectural changes. This continuity reduces development cycles and accelerates time to value.

PCI Express 4.0 Integration and System Compatibility

The PCI Express 4.0 x16 interface is a critical element of this category, offering doubled bandwidth compared to previous generations. This high-speed interconnect ensures efficient communication between the GPU and host system, which is essential for data-intensive applications. In multi-GPU configurations, PCIe Gen4 helps maintain balanced performance across nodes, supporting scalable architectures.

System compatibility is a major consideration in enterprise deployments, and this category is engineered to integrate with a wide range of server platforms. Compliance with industry standards ensures smooth installation, firmware recognition, and long-term support within heterogeneous data center environments.

Scalability in Multi-GPU Deployments

Scalability is a hallmark of this category, enabling organizations to deploy multiple GPUs within a single server or across clusters. The combination of PCIe Gen4 bandwidth and optimized power delivery supports dense configurations without compromising stability. This scalability is essential for workloads such as distributed inference, large-scale simulations, and data analytics pipelines.

By supporting standardized deployment models, this category allows IT teams to expand computing capacity incrementally, aligning hardware investments with evolving workload demands.

Power Efficiency and Operational Stability

Power efficiency is a key metric in data center operations, and GPUs in this category are designed to deliver high performance per watt. Efficient power management reduces operational costs and supports sustainability initiatives. The fanless design further contributes to energy efficiency by relying on centralized cooling systems rather than individual fans.

Operational stability is enhanced through enterprise-grade components, rigorous validation, and long lifecycle support. This ensures that systems remain reliable over extended deployment periods, minimizing downtime and maintenance requirements.