NVIDIA Tesla K40st
NVIDIA graphics card specifications and benchmark scores
At a Glance
NVIDIANVIDIA Tesla K40st Specifications
Tesla K40st GPU Core
Shader units and compute resources
The NVIDIA Tesla K40st 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 K40st Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Tesla K40st'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 K40st by NVIDIA dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
NVIDIA's Tesla K40st Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Tesla K40st'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 K40st by NVIDIA Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Tesla K40st, 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 K40st Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the NVIDIA Tesla K40st 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 K40st 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 K40st will perform in GPU benchmarks compared to previous generations.
NVIDIA's Tesla K40st Power & Thermal
TDP and power requirements
Power specifications for the NVIDIA Tesla K40st 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 K40st to maintain boost clocks without throttling.
Tesla K40st by NVIDIA Physical & Connectivity
Dimensions and outputs
Physical dimensions of the NVIDIA Tesla K40st 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 K40st. 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 K40st Product Information
Release and pricing details
The NVIDIA Tesla K40st 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 K40st by NVIDIA represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Tesla K40st Benchmark Scores
No benchmark data available for this GPU.
About NVIDIA Tesla K40st
# NVIDIA Tesla K40st: A Kepler-Era Compute Specialist with Enduring Capabilities
The NVIDIA Tesla K40st is a dual-slot compute accelerator built on the 28 nm Kepler architecture, specifically the GK110B chip manufactured by TSMC. With a launch MSRP of 7,699 USD, this end-of-life product occupies the 50th percentile among all GPUs in the benchmark database, placing it squarely in the mid-range of historical performance. The card integrates 7,080 million transistors on a 561 mm² die, resulting in a transistor density of 12.6M per mm². Its benchmark results place it as a capable compute-oriented solution, though its nearest rival data is absent from the current analysis, making direct competitive comparisons challenging. The K40st was released on November 21, 2013, succeeding the Tesla Fermi line and preceding the Tesla Maxwell generation.
How It Compares
The Tesla K40st currently has no nearest rivals listed in the benchmark database, which limits direct comparative analysis against contemporary or successor products. This absence of near-neighbor data means its percentile ranking of 50 is established against the full historical GPU landscape rather than a tightly clustered competitive set. Without explicit rival scores or deltaPct values, the K40st's positioning must be inferred from its architectural characteristics and raw specification sheet. The card's 3.312 TFLOPS of FP32 compute and 288.4 GB/s memory bandwidth place it in a performance tier that was competitive for professional compute workloads at its release, but the lack of rival data prevents precise percentage-based comparisons. The transition from Tesla Fermi to Tesla Maxwell as predecessor and successor frames its generational context, indicating the K40st represents a mature Kepler iteration with refinements over the prior architecture.
Ray Tracing and Feature Set
The Tesla K40st does not include dedicated ray tracing cores or tensor cores, as these hardware accelerators were not part of the Kepler architecture design. Instead, the card relies on its 2,880 shading units, 240 texture mapping units, and 48 raster output units to handle graphics and compute workloads. The architecture supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.175, providing modern API compatibility despite its 2013 release date. The DirectX 12 (11_1) feature level indicates partial support for the DirectX 12 feature set, with the 11_1 designation representing the maximum feature level achievable on this hardware. OpenGL 4.6 support ensures broad compatibility with professional visualization applications, while Vulkan 1.2.175 enables access to modern low-overhead rendering APIs. The absence of RT and tensor cores means the K40st is not suited for hardware-accelerated ray tracing or AI-accelerated tensor operations, relegating it to traditional rasterization and general-purpose compute workloads where its FP32 throughput of 3.312 TFLOPS can be fully utilized.
Power and Cooling
The Tesla K40st carries a thermal design power of 245 W, requiring a suggested power supply of 550 W for reliable system operation. The card occupies a dual-slot form factor and measures 267 mm or 10.5 inches in length, which is a standard size for professional compute accelerators of its era. The power connector configuration is not specified in the available data, but the 550 W PSU recommendation provides clear guidance for system builders. The dual-slot cooling solution is designed to handle the 245 W thermal load in server or workstation environments, where sustained compute workloads generate significant heat. The absence of display outputs on this card reinforces its compute-focused design, as it is intended to run headless in server configurations rather than driving monitors directly. The PCIe 3.0 x16 bus interface provides adequate bandwidth for data transfer between the card and host system, though the 288.4 GB/s memory bandwidth is the more critical throughput metric for compute workloads that exceed the 12 GB VRAM capacity.
Who Should Consider It
The Tesla K40st is suited for users requiring substantial FP32 compute throughput in a professional context, as its 3.312 TFLOPS and 12 GB GDDR5 memory make it capable of handling moderately sized compute tasks. The 50th percentile ranking indicates it sits at the median of all GPUs, meaning it outperforms roughly half of the historical GPU population while trailing the other half. For resolution-based recommendations, the 12 GB VRAM and 384-bit memory bus provide sufficient capacity for 1080p and 1440p workloads, though 4K applications may strain the 288.4 GB/s bandwidth depending on the specific computational demands. The card's 34.50 GPixel/s pixel rate and 138.0 GTexel/s texture rate suggest it can handle traditional graphics rendering at lower resolutions, but its primary value proposition lies in compute tasks that leverage the 2,880 shading units. Users with workloads that fit within 12 GB of VRAM and do not require ray tracing or tensor acceleration may find the K40st a viable option, provided their power supply meets the 550 W recommendation and their system has a PCIe 3.0 x16 slot available.
Benchmark Performance
With no benchmark scores or nearest rival data available in the fact pack, performance analysis must rely on the card's architectural specifications and its percentile ranking. The 50th percentile placement indicates the K40st performs at the median level across all GPUs in the database, which is a meaningful data point for historical context. The FP32 throughput of 3.312 TFLOPS represents the card's peak single-precision compute capability, which is the primary metric for many scientific and engineering compute workloads. The texture rate of 138.0 GTexel/s and pixel rate of 34.50 GPixel/s provide secondary performance indicators for graphics-bound tasks. The memory bandwidth of 288.4 GB/s, derived from the 384-bit bus width and 6 Gbps effective memory speed, is a critical constraint for memory-intensive workloads. Without deltaPct values against specific rivals, the K40st's relative performance cannot be expressed in exact percentages, but its architectural characteristics suggest it was a strong performer in its 2013 context, particularly for double-precision compute tasks common in scientific simulation and financial modeling.
Memory Subsystem
The Tesla K40st is equipped with 12 GB of GDDR5 memory operating at an effective 6 Gbps, connected via a 384-bit memory bus. This configuration yields a total memory bandwidth of 288.4 GB/s, which is a substantial figure for the card's 2013 release period and remains adequate for many modern compute workloads that fit within the 12 GB capacity. The 384-bit bus width allows for high memory throughput, which is essential for compute kernels that repeatedly access large datasets. The 12 GB VRAM capacity is particularly beneficial for workloads that require loading large models or datasets into GPU memory, such as machine learning inference or computational fluid dynamics simulations. At higher resolutions or with larger batch sizes, the 288.4 GB/s bandwidth may become a limiting factor, as memory-bound operations will not scale beyond this throughput ceiling. The GDDR5 memory type was standard for high-performance GPUs of this era, and its effective 6 Gbps speed represents a mature implementation of the technology. For users with workloads that fit within the 12 GB capacity and do not require memory bandwidth beyond 288.4 GB/s, the K40st's memory subsystem remains a capable component, though it will lag behind newer memory technologies in absolute throughput.
FAQ
Q: What is the FP32 compute performance of the Tesla K40st?
A: The card delivers 3.312 TFLOPS of FP32 compute throughput, which places it at the 50th percentile among all GPUs in the benchmark database.
Q: How much memory does the Tesla K40st have and what is its bandwidth?
A: It features 12 GB of GDDR5 memory on a 384-bit bus, providing 288.4 GB/s of memory bandwidth at an effective 6 Gbps memory speed.
Q: Does the Tesla K40st support ray tracing or tensor cores?
A: No, the card does not include dedicated ray tracing cores or tensor cores, as these were not part of the Kepler architecture. It relies on its 2,880 shading units for compute and graphics workloads.
Q: What is the power requirement for the Tesla K40st?
A: The card has a TDP of 245 W and requires a suggested power supply of 550 W. It occupies a dual-slot form factor and measures 267 mm in length.
Q: What APIs does the Tesla K40st support?
A: The card supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.175, providing compatibility with modern graphics and compute APIs.
Q: What is the production status and release date of the Tesla K40st?
A: The card is end-of-life and was released on November 21, 2013. It succeeded the Tesla Fermi generation and was followed by Tesla Maxwell.
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