NVIDIA Tesla K8

Benchmark Performance

The NVIDIA Tesla K8 presents a peculiar benchmark profile. With an average benchmark score of zero and an empty benchmarks array, the data indicates that no standardized performance tests have been recorded for this part in the database. However, the hardware specifications allow for a theoretical assessment of its computational throughput. The FP32 compute rate stands at 2.491 TFLOPS, which is a modest figure for a professional-grade accelerator from the Kepler era. This places the K8 in the 50th percentile among all GPUs, meaning it sits exactly at the median of the distribution — neither a high-end performer nor a low-end part.

The pixel fill rate is 25.95 GPixel/s, derived from the 32 ROPs operating at the boost clock. Texture throughput reaches 103.8 GTexel/s, driven by 128 TMUs. These numbers indicate a card that was designed for compute workloads rather than rasterization-heavy tasks. The absence of any nearest rivals in the database prevents a direct percentage comparison, but the raw figures suggest that the K8 would deliver roughly a quarter of the FP32 performance of contemporary flagship consumer cards from its generation, while consuming significantly less power. The 811 MHz boost clock is conservative, and the 1536 shading units are organized in a configuration that prioritizes parallel integer and floating-point operations over geometry processing.

Ray Tracing and Feature Set

The Tesla K8 contains no dedicated ray tracing cores and no tensor cores. This is consistent with its Kepler architecture, which predates the introduction of hardware-accelerated ray tracing by several generations. The card offers no acceleration for RT workloads, and any ray-traced rendering would fall entirely on the shader units, which are not optimized for such calculations. The API support is limited: DirectX 12 (11_0), OpenGL 4.6, and Vulkan 1.2.175. The DirectX 12 support is feature-level 11_0, which means it does not expose the full DirectX 12 feature set — a notable limitation for modern gaming or real-time rendering applications that leverage DX12 Ultimate features.

For professional workflows, the feature set is adequate for compute tasks that rely on OpenCL or CUDA, though the lack of tensor cores eliminates any possibility of AI-accelerated inference or deep learning training. The Vulkan 1.2.175 support provides a modern low-level API pathway, but the hardware's age limits its practical utility in current applications. The absence of display outputs means this card is strictly a compute or rendering accelerator — it cannot drive a monitor, and all output must be handled by a separate GPU.

Memory Subsystem

The memory configuration is one of the more compelling aspects of the Tesla K8. It ships with 8 GB of GDDR5 memory on a 256-bit bus, yielding a bandwidth of 160.0 GB/s. The memory clock is 1250 MHz, which translates to 5 Gbps effective. This capacity is generous for the card's era and remains useful for certain dataset sizes in scientific computing or rendering tasks that require large working sets. The 256-bit bus width is moderate, and the resulting bandwidth is sufficient for the FP32 compute rate — the ratio of bandwidth to compute is balanced, meaning the card is unlikely to be heavily bandwidth-starved in most workloads.

At high resolutions, the 8 GB capacity is a clear advantage over consumer cards from the same period that often had 2-4 GB. However, the 160.0 GB/s bandwidth is a bottleneck for texture-heavy workloads at 4K or above. The memory subsystem can sustain the pixel and texture rates calculated earlier, but it will not provide headroom for aggressive anti-aliasing or high-resolution shadow maps. For compute tasks that are memory-bound, such as sparse matrix operations or large hash tables, the capacity is welcome but the bandwidth limits overall throughput.

Power and Cooling

The Tesla K8 has a thermal design power of 100 W, which is remarkably low for a card with 1536 shading units and 8 GB of memory. This efficiency is a direct result of the conservative clock speeds — the base clock of 693 MHz and boost of 811 MHz are well below what the Kepler architecture could achieve at higher power envelopes. The card requires only a single 6-pin power connector, and the suggested power supply rating is 300 W. This makes the K8 unusually accommodating for system builders; it can fit into machines that would struggle with higher-end accelerators.

The card is single-slot and measures 241 mm in length (9.5 inches). This compact form factor, combined with the low power draw, means it can be installed in dense server configurations or workstations with limited internal space. The cooling solution is not specified in the data, but the 100 W TDP suggests that a simple blower-style cooler would be sufficient to maintain acceptable temperatures under sustained load. The production status is end-of-life, and the card was released on 2014-09-15, making it a legacy product that may still be found in older systems.

How It Compares

The nearestRivals array is empty, so no direct comparison to specific competing products can be made from the provided data. In the absence of rival scores, the analysis must rely on the absolute specifications and the percentile ranking. The 50th percentile placement indicates that the K8 is exactly average when measured against the entire GPU landscape. This means it outperforms roughly half of all GPUs in the database, but it is also outclassed by the other half.

In the context of its own generation, the Tesla K8 sits below the K10 and K20 accelerators that NVIDIA offered for high-performance computing. The GK104 chip is a mid-range die, and the compute performance reflects that positioning. Compared to its predecessor, Tesla Fermi, the K8 offers architectural improvements in power efficiency and feature support, particularly in the transition to Kepler's improved scheduling and reduced instruction overhead. Its successor, Tesla Maxwell, would later deliver significant gains in performance-per-watt, but that is not relevant to the K8's standing within its own era.

The lack of display outputs positions it against other compute-only accelerators, but without rival data, a quantitative comparison is impossible. Qualitatively, the 8 GB memory capacity is a differentiator, as many compute cards of that period had less. The FP32 throughput of 2.491 TFLOPS is competitive with mid-range server GPUs of the time, but it is far below the 10+ TFLOPS figures that flagship accelerators achieved.

Who Should Consider It

The Tesla K8 is a niche product with a specific set of strengths and weaknesses. Given its 50th percentile ranking and the specifications, it is suited for workloads that prioritize memory capacity over raw compute speed. The 8 GB VRAM is the standout feature — applications that require loading large datasets into GPU memory, such as certain scientific simulations, medical imaging, or offline rendering with large texture atlases, would benefit from the capacity even if the compute throughput is modest.

For gaming, this card is not viable as a primary GPU due to the lack of display outputs. It could theoretically be used as a PhysX or compute companion in a system with a separate display adapter, but the absence of modern feature support limits its utility. The DirectX 12 (11_0) support means it cannot handle titles that require DX12 Ultimate features, and the lack of ray tracing cores disqualifies it for any RT-enabled workload.

For professional compute, the K8 is best suited to FP32 workloads that are latency-tolerant and do not require high bandwidth. The 160.0 GB/s bandwidth will constrain performance on memory-intensive tasks, but the 2.491 TFLOPS of FP32 throughput is adequate for moderately parallel scientific code. The 100 W TDP and single-slot design make it an attractive option for systems with strict power or space budgets, particularly in server racks where multiple accelerators are installed.

The card is end-of-life, so new deployments are unlikely, but for legacy system maintenance or budget-constrained research environments, the K8 offers a balance of capacity and efficiency that is hard to match at its power level. It is not a card for high-end workloads, but for foundational compute tasks that fit within its memory and bandwidth limits, it remains a functional, if dated, option. Users with workloads that require more than 8 GB of memory or significantly higher bandwidth should look elsewhere, as the K8's architectural limitations cannot be overcome through driver updates or software optimizations.