NVIDIA Tesla K80
NVIDIA graphics card specifications and benchmark scores
NVIDIA Tesla K80 Specifications
Tesla K80 GPU Core
Shader units and compute resources
The NVIDIA Tesla K80 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 K80 Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Tesla K80'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 K80 by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Tesla K80 Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla K80'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 K80 by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Tesla K80, 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 K80 Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla K80 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 2.0 Architecture & Process
Manufacturing and design details
The NVIDIA Tesla K80 is built on NVIDIA's Kepler 2.0 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 K80 will perform in GPU benchmarks compared to previous generations.
NVIDIA's Tesla K80 Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Tesla K80 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 K80 to maintain boost clocks without throttling.
Tesla K80 by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Tesla K80 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 K80. 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 K80 Product Information
Release and pricing details
The NVIDIA Tesla K80 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 K80 by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Tesla K80 Benchmark Scores
geekbench_openclSource
Geekbench OpenCL tests GPU compute performance using the cross-platform OpenCL API. This shows how NVIDIA Tesla K80 handles parallel computing tasks like video encoding and scientific simulations. OpenCL is widely supported across different GPU vendors and platforms.
geekbench_vulkanSource
Geekbench Vulkan tests GPU compute using the modern low-overhead Vulkan API. This shows how NVIDIA Tesla K80 performs with next-generation graphics and compute workloads. Vulkan offers better CPU efficiency than older APIs like OpenGL. Modern games and applications increasingly use Vulkan for cross-platform GPU acceleration.
About NVIDIA Tesla K80
When it comes to professional workloads, is the NVIDIA Tesla K80 still a contender, or has time left it behind? Launched in late 2014, this dual-GPU accelerator was built for heavy computation, packing 12 GB of GDDR5 memory across its two Kepler GK210 chips. Its architecture, while not modern, was designed for precision in scientific simulations, financial modeling, and complex data analytics. With a TDP of 300W, it demands robust system cooling and power delivery, raising the question of whether its performance per watt stacks up today. The PCIe 3.0 interface remains serviceable, but are the twin GPUs being held back in newer systems? For certain legacy server racks or cost-conscious research labs, this NVIDIA compute card might still find a niche, but its age is a significant factor to weigh against its potential raw throughput.
Evaluating the Tesla K80 for video editing presents a mixed bag; can it handle modern codecs and timelines? The 12 GB frame buffer is its strongest asset, allowing for substantial high-resolution footage to be manipulated in memory. However, the Kepler architecture lacks dedicated hardware encoders for formats like H.264/HEVC, pushing all encoding work onto the slower CUDA cores. Benchmarks like its Geekbench OpenCL score of 18,620 points show it has parallel processing muscle, but is that enough for real-time playback of 4K or 8K material? Applications optimized for GPU acceleration from that era may see benefits, but contemporary editing software often demands features this GPU simply doesn't possess. The NVIDIA accelerator from 2014 struggles where modern GPUs excel, making it a questionable choice for serious content creation pipelines today.
Software compatibility is a critical hurdle; will your essential applications even recognize this hardware? The Tesla K80 relies on mature enterprise drivers from NVIDIA, which continue to offer support for legacy professional software stacks. Its performance in compute APIs is demonstrated by a Vulkan score of 19,111 points, but how many professional applications leverage Vulkan over CUDA or OpenCL? The key consideration is whether your required plugins, renderers, and simulation tools are built for the Kepler generation. This particular NVIDIA Tesla product thrives in a stable, known software environment but falters when asked to run cutting-edge development tools. You must verify your entire software chain, or risk investing in a powerful paperweight.
The enterprise features of this GPU were its main selling point; do they justify considering it on the secondary market? This compute card was engineered for reliability in 24/7 datacenter operation, with ECC memory support for error-critical tasks. Its dual-GPU design on a single board offered high density for server deployments, but does that design introduce management complexity? To understand its intended use, consider its primary strengths:
- High double-precision floating-point performance for scientific computing.
- ECC memory protection for ensuring data integrity in long-running jobs.
- GPU Direct capabilities for faster data transfer in clustered environments.
- Robust virtualization support for sharing resources across multiple users.
The AMD Equivalent of Tesla K80
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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