NVIDIA Tesla K20X
NVIDIA graphics card specifications and benchmark scores
NVIDIA Tesla K20X Specifications
Tesla K20X GPU Core
Shader units and compute resources
The NVIDIA Tesla K20X GPU core specifications define its raw processing power for graphics and compute workloads. Shading units (also called CUDA cores, stream processors, or execution units depending on manufacturer) handle the parallel calculations required for rendering. TMUs (Texture Mapping Units) process texture data, while ROPs (Render Output Units) handle final pixel output. Higher shader counts generally translate to better GPU benchmark performance, especially in demanding games and 3D applications.
Tesla K20X Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Tesla K20X's performance in GPU benchmarks and real-world gaming. The base clock represents the minimum guaranteed frequency, while the boost clock indicates peak performance under optimal thermal conditions. Memory clock speed affects texture loading and frame buffer operations. The Tesla K20X by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Tesla K20X Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla K20X's memory capacity determines how well it handles high-resolution textures and multiple displays. Memory bandwidth, measured in GB/s, affects how quickly data moves between the GPU and VRAM. Higher bandwidth improves performance in memory-intensive scenarios like 4K gaming. The memory bus width and type (GDDR6, GDDR6X, HBM) significantly influence overall GPU benchmark scores.
Tesla K20X by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Tesla K20X, reducing the need to fetch data from slower VRAM. L1 and L2 caches store frequently accessed data close to the compute units. AMD's Infinity Cache (L3) dramatically increases effective bandwidth, improving GPU benchmark performance without requiring wider memory buses. Larger cache sizes help maintain high frame rates in memory-bound scenarios and reduce power consumption by minimizing VRAM accesses.
Tesla K20X Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla K20X against other graphics cards. FP32 (single-precision) performance, measured in TFLOPS, indicates compute capability for gaming and general GPU workloads. FP64 (double-precision) matters for scientific computing. Pixel and texture fill rates determine how quickly the GPU can render complex scenes. While real-world GPU benchmark results depend on many factors, these specifications help predict relative performance levels.
Kepler Architecture & Process
Manufacturing and design details
The NVIDIA Tesla K20X is built on NVIDIA's Kepler architecture, which defines how the GPU processes graphics and compute workloads. The manufacturing process node affects power efficiency, thermal characteristics, and maximum clock speeds. Smaller process nodes pack more transistors into the same die area, enabling higher performance per watt. Understanding the architecture helps predict how the Tesla K20X will perform in GPU benchmarks compared to previous generations.
NVIDIA's Tesla K20X Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Tesla K20X determine PSU requirements and thermal management needs. TDP (Thermal Design Power) indicates the heat output under typical loads, guiding cooler selection. Power connector requirements ensure adequate power delivery for stable operation during demanding GPU benchmarks. The suggested PSU wattage accounts for the entire system, not just the graphics card. Efficient power delivery enables the Tesla K20X to maintain boost clocks without throttling.
Tesla K20X by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Tesla K20X are critical for case compatibility. Card length, height, and slot width determine whether it fits in your chassis. The PCIe interface version affects bandwidth for communication with the CPU. Display outputs define monitor connectivity options, with modern cards supporting multiple high-resolution displays simultaneously. Verify these specifications against your case and motherboard before purchasing to ensure a proper fit.
NVIDIA API Support
Graphics and compute APIs
API support determines which games and applications can fully utilize the NVIDIA Tesla K20X. DirectX 12 Ultimate enables advanced features like ray tracing and variable rate shading. Vulkan provides cross-platform graphics capabilities with low-level hardware access. OpenGL remains important for professional applications and older games. CUDA (NVIDIA) and OpenCL enable GPU compute for video editing, 3D rendering, and scientific applications. Higher API versions unlock newer graphical features in GPU benchmarks and games.
Tesla K20X Product Information
Release and pricing details
The NVIDIA Tesla K20X is manufactured by NVIDIA as part of their graphics card lineup. Release date and launch pricing provide context for comparing GPU benchmark results with competing products from the same era. Understanding the product lifecycle helps evaluate whether the Tesla K20X by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Tesla K20X Benchmark Scores
No benchmark data available for this GPU.
About NVIDIA Tesla K20X
The NVIDIA Tesla K20X stands as a formidable compute accelerator from its era, engineered specifically for high-performance computing environments. Leveraging the advanced Kepler architecture, this card delivers exceptional parallel processing power crucial for complex simulations and data analysis. Its substantial 6 GB of GDDR5 VRAM provides the necessary headroom for handling large datasets without constant system memory swaps. With a thermal design power of 235 watts, the card demands robust cooling solutions typically found in server and workstation chassis. The PCIe 3.0 x16 interface ensures high bandwidth communication with the host system, minimizing data transfer bottlenecks. Professionals in scientific research and engineering fields were the primary target audience for this powerful hardware. The Tesla K20X accelerator was not designed for consumer gaming but for accelerating computationally intensive tasks. Its release marked a significant step forward in making powerful GPU computing more accessible.
Focusing on its performance attributes, the Tesla K20X graphics card excels in raw computational throughput rather than traditional frame rates. The architecture features a high number of CUDA cores optimized for double-precision floating-point operations, which are essential for scientific computations. The 6 GB memory capacity, coupled with high bandwidth, allows it to process massive textures and volumetric data sets efficiently. Thermal performance is managed through a passive heatsink design intended for forced airflow from system fans, ensuring stability under sustained full load. This makes the K20X GPU ideal for applications like computational fluid dynamics and financial modeling where reliability is paramount. It represents a peak in the Kepler generation's capabilities for professional workloads. Users could expect significant acceleration in supported applications compared to using CPUs alone.
The optimal use cases for the NVIDIA Tesla K20X compute card are distinctly professional and technical in nature. It finds its strengths in accelerating tasks within fields such as astrophysics, molecular dynamics, and seismic processing, where its computational power shines. The card's design prioritizes data integrity and calculation accuracy over graphical fidelity, which is why it lacks display outputs. Deploying this accelerator required a deep understanding of parallel programming models like CUDA to fully harness its potential. Its substantial launch price reflected its position as a premium tool for research institutions and large enterprises. The K20X was a key component in many supercomputing clusters, contributing to groundbreaking research. It is a specialist tool, not a general-purpose graphics card, and its value is measured in research outcomes, not frames per second.
When considering the legacy of the Radeon NVIDIA Tesla K20X, it is important to recognize its role in pushing the boundaries of GPU-accelerated computing. This processor helped democratize access to supercomputer-level performance for a wider range of academic and industrial researchers. Its Kepler foundation provided a balance of performance and power efficiency for the 28 nm process technology available at the time. While it has been succeeded by newer architectures offering greater performance and efficiency, the K20X remains a benchmark for its generation. The knowledge gained from deploying and programming these cards has informed the development of modern AI and machine learning accelerators. It stands as a testament to NVIDIA's early commitment to the high-performance computing market. The Tesla K20X accelerator carved out a niche that continues to be critically important in the world of scientific discovery.
The AMD Equivalent of Tesla K20X
Looking for a similar graphics card from AMD? The AMD Radeon RX 480 offers comparable performance and features in the AMD lineup.
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