NVIDIA Tesla K80

The NVIDIA Tesla K80 is a dual-slot, compute-oriented accelerator built on the Kepler 2.0 architecture, featuring the GK210 chip fabricated on a 28 nm process at TSMC. With 7,100 million transistors on a 561 mm² die, this end-of-life product was released in late 2014 and targets professional compute workloads rather than graphics output, as it has no display outputs. The benchmark data shows an average score of 18,866 across Geekbench OpenCL and Vulkan tests, placing it at the 61st percentile of all GPUs, which indicates it remains a mid-pack performer despite its age.

Benchmark Performance

The Tesla K80 delivers a Geekbench OpenCL score of 18,620 and a Geekbench Vulkan score of 19,111, yielding an average of 18,866. These scores place the card at the 61st percentile overall, meaning it outperforms roughly six out of every ten GPUs in the database. The variance between the two API tests is modest, with Vulkan scoring about 2.6% higher than OpenCL, suggesting the Kepler architecture handles both compute interfaces with similar efficiency.

In raw compute terms, the K80 provides 4.113 TFLOPS of FP32 performance, a figure that is respectable for a card from its generation but clearly outstripped by modern parts. The pixel rate stands at 42.85 GPixel/s, while the texture rate reaches 171.4 GTexel/s, both driven by a base clock of 562 MHz that boosts to 824 MHz. These clock speeds are conservative, yet the 2,496 shading units, 208 texture mapping units, and 48 raster operations pipelines ensure the card can handle substantial parallel workloads.

When compared to its nearest rivals, the K80 sits in a tight cluster. The Quadro RTX 4000 scores 18,852, which is a mere 0.1% lower than the K80's average. The Tesla K20m, a predecessor from the same Kepler family, scores 19,011, putting the K80 0.8% behind. The GeForce RTX 4050 Mobile scores 19,049 (1% higher), and the AMD Radeon 780M scores 19,057 (1% higher). These deltas are minuscule, indicating that the K80 performs within a narrow band of contemporary and older hardware across these synthetic benchmarks.

How It Compares

Against the NVIDIA Quadro RTX 4000, the K80 is essentially tied, with the Quadro leading by only 0.1%. This is notable because the RTX 4000 is a much newer professional card, yet the K80's raw compute throughput keeps it competitive in these specific OpenCL and Vulkan tests. The K80's advantage lies in its sheer number of shading units, while the RTX 4000 likely benefits from architectural efficiency and newer features.

The NVIDIA Tesla K20m presents a direct generational comparison within the Tesla lineup. The K20m scores 0.8% higher than the K80, which is a surprisingly small gap given the K80's later release and higher specifications. This suggests that the K20m's higher boost clocks or memory configuration offset the K80's additional hardware resources in these workloads, making the performance difference nearly negligible.

The GeForce RTX 4050 Mobile, a laptop-oriented GPU, leads the K80 by 1%. This is a significant achievement for the mobile part, as it delivers comparable compute performance in a far smaller power envelope. For the K80, this means it is no longer competitive with even mid-range mobile solutions from a performance-per-watt perspective, though raw scores remain similar.

The AMD Radeon 780M, an integrated graphics solution, also edges out the K80 by 1%. This integrated part achieving near-parity with a dedicated dual-slot accelerator underscores how far GPU compute has advanced. The K80's performance is now within the reach of integrated graphics, which fundamentally changes its value proposition for new buyers.

Ray Tracing and Feature Set

The Tesla K80 has no dedicated ray tracing cores and no tensor cores, as these features were not part of the Kepler 2.0 architecture. Instead, the card relies entirely on its 2,496 shading units for all compute tasks, including any ray tracing workloads that would be handled via software rather than hardware acceleration. This places the K80 at a distinct disadvantage for modern ray-traced applications, where dedicated hardware is essential for real-time performance.

In terms of API support, the K80 supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.175. The DirectX 12 support at the 11_1 feature level is a limitation, as it lacks some of the advanced features of full DirectX 12 Ultimate. Vulkan 1.2.175 provides broad compatibility with modern compute and graphics APIs, but the absence of hardware ray tracing means any such workload will be processed inefficiently. OpenGL 4.6 is fully supported, which is adequate for scientific visualization and legacy compute tasks.

The lack of tensor cores is particularly notable for machine learning applications, where tensor operations are a core workload. The K80's FP32 throughput of 4.113 TFLOPS can handle some neural network inference tasks, but training modern models would be impractically slow without tensor acceleration. For the K80's intended professional compute role, this means it is best suited for traditional HPC workloads like fluid dynamics, finite element analysis, or molecular dynamics, where raw FP32 throughput is the primary requirement.

Power and Cooling

The Tesla K80 has a thermal design power of 300 W, which is substantial for a dual-slot card. This power draw requires a suggested power supply of 700 W, and the card draws its power through a single 8-pin connector. The 300 W TDP is typical for a high-performance compute accelerator of its era, and the dual-slot cooler is designed to dissipate that heat under sustained load.

For system integration, the K80's 267 mm length (10.5 inches) means it will fit in most full-size workstation chassis, but the dual-slot footprint occupies space that could otherwise hold additional expansion cards. The single 8-pin connector simplifies cabling compared to multi-connector designs, though the 700 W PSU recommendation ensures adequate headroom for the rest of the system. The card's power efficiency, however, is poor by modern standards, as its 300 W draw yields only 4.113 TFLOPS, while contemporary cards deliver far more performance per watt.

The memory runs at 1253 MHz with an effective data rate of 5 Gbps, which is modest by current standards. This clock speed, combined with the 384-bit bus, produces a bandwidth of 240.6 GB/s. The power consumption is heavily influenced by this memory configuration, as GDDR5 at these speeds draws significant current. For compute workloads that are memory-bandwidth bound, the 240.6 GB/s figure is a limiting factor, though it was competitive at launch.

Who Should Consider It

The Tesla K80's benchmark scores indicate it is suitable for compute workloads that are not heavily dependent on modern features. At 1080p resolution, the K80's 4.113 TFLOPS can handle many scientific simulations and data processing tasks with reasonable throughput, as demonstrated by its 61st percentile ranking. For users running legacy CUDA applications that predate tensor core requirements, the K80 remains functional, though the 1% performance deficit against the Radeon 780M suggests that integrated solutions are now equally capable.

For 1440p workloads, the K80's memory bandwidth of 240.6 GB/s becomes a bottleneck, particularly for applications that process large datasets. The 12 GB VRAM is generous, but the bandwidth limits how quickly data can be fed to the shading units. Users working with high-resolution imagery or large matrix operations will find the K80 adequate but not exceptional, as its performance cluster with the RTX 4050 Mobile and Radeon 780M confirms.

At 4K resolution or above, the K80 is not recommended for real-time graphics, as it has no display outputs and its pixel rate of 42.85 GPixel/s is insufficient for modern rendering demands. However, for offline compute tasks like rendering or simulation that do not require real-time interaction, the K80 can still produce results, albeit slowly. The 0.8% gap behind the Tesla K20m suggests that users upgrading from that card would see minimal benefit, making the K80 a poor upgrade path.

Memory Subsystem

The Tesla K80 is equipped with 12 GB of GDDR5 memory on a 384-bit bus, yielding a bandwidth of 240.6 GB/s. This configuration was substantial at launch, allowing large datasets to reside on the card without constant PCIe transfers. The 384-bit bus width is a key factor in achieving this bandwidth, as it allows 48 bytes of data to be transferred per clock cycle from the memory controller.

The memory clock of 1253 MHz, with an effective rate of 5 Gbps, is the primary limiter on bandwidth. While 240.6 GB/s was competitive in 2014, modern cards with HBM or faster GDDR6X offer multiples of this throughput. For the K80's compute workloads, the bandwidth determines how quickly data can be streamed to the shading units, and at 240.6 GB/s, the card is well-matched to its 4.113 TFLOPS FP32 throughput for many algorithms.

The 12 GB capacity is more than sufficient for most professional workloads, including large simulations or deep learning inference with moderate batch sizes. However, the combination of capacity and bandwidth means that memory-bound tasks will not scale as well as compute-bound tasks. The K80's memory subsystem is balanced for its era, but it is now a clear bottleneck when compared to the RTX 4050 Mobile's more modern memory architecture, despite the similar overall benchmark scores.