NVIDIA Tesla K20c

Benchmark Performance

The NVIDIA Tesla K20c occupies a fascinating mid-pack position in the database, landing at the 50th percentile among all GPUs tracked. This is a compute-oriented accelerator built on the Kepler architecture, using the GK110 chip fabricated on TSMC's 28 nm process. The silicon packs 7,080 million transistors into a 561 mm² die, yielding a transistor density of 12.6M per mm² — a figure that was substantial for its era but now appears modest against modern designs.

The raw compute specifications tell the core story. The K20c delivers 3.524 TFLOPS of FP32 performance, driven by 2,496 shading units, 208 texture mapping units, and 40 ROPs. This configuration yields a pixel rate of 36.71 GPixel/s and a texture rate of 146.8 GTexel/s. In the context of 2012, these numbers were flagship-class for compute workloads, though the card's purpose was never primarily gaming. The benchmark percentile of 50 places it squarely in the middle of the entire GPU landscape, meaning roughly half of all GPUs in the database outperform it and half fall behind.

The database lists no nearest rivals for this card, which is telling. The K20c's absence of direct competitors in the benchmark records suggests it occupied a unique niche — a professional compute accelerator without gaming-oriented peers in the comparison set. Benchmark results indicate that its FP32 throughput of 3.524 TFLOPS places it in a performance tier that modern mid-range consumer cards surpass, but for its intended scientific and professional workloads, the compute density was competitive at launch. The lack of any deltaPct values against rivals means every performance assessment must be grounded in the absolute figures and the 50th percentile standing.

Who Should Consider It

Given the 50th percentile ranking and the compute-focused architecture, the Tesla K20c is not a sensible choice for contemporary gaming at any resolution. The card has no display outputs, which immediately disqualifies it for any interactive graphics use case — it cannot drive a monitor directly. Benchmark results indicate that its 3.524 TFLOPS FP32 throughput would be insufficient for modern high-refresh-rate gaming at 1080p, let alone 1440p or 4K, where current GPUs routinely exceed 10 TFLOPS.

The realistic audience for this accelerator is found in legacy compute environments. Organizations maintaining older CUDA-based scientific simulation pipelines, or those needing a Kepler-generation compute node for specific software certifications, would find the K20c relevant. Its 5 GB memory capacity and 208.0 GB/s bandwidth support mid-sized datasets that fit within that frame buffer. For workloads like molecular dynamics, finite element analysis, or certain deep learning inference tasks that were developed against the Kepler compute capability, this card remains functional. It is not appropriate for modern AI training, where memory capacity and FP16 or tensor-accelerated paths are essential — the K20c has no FP16 support listed and no tensor cores.

Memory Subsystem

The memory configuration is one of the K20c's defining characteristics. It ships with 5 GB of GDDR5 across a 320-bit bus, producing a memory bandwidth of 208.0 GB/s. The memory clock runs at 1300 MHz, translating to 5.2 Gbps effective data rate. This bandwidth figure is modest by contemporary standards — modern mid-range cards exceed 300 GB/s — but for the Kepler era, 208.0 GB/s was adequate for the scientific workloads the card targeted.

For high-resolution compute tasks, the 5 GB capacity is more limiting than the bandwidth. Datasets that exceed 5 GB require either partitioning or spillover to system memory, which drastically reduces throughput. The 320-bit bus width does provide reasonable efficiency for memory-bound kernels, but the effective bandwidth of 208.0 GB/s constrains performance on large matrix operations or convolutions. The ROP count of 40 further limits any rasterization throughput, though this is largely irrelevant for a card without display outputs. The memory subsystem is best suited to workloads where data fits within the 5 GB frame buffer and where the 208.0 GB/s bandwidth is sufficient to feed the 3.524 TFLOPS compute throughput.

How It Compares

The database records no nearest rivals for the Tesla K20c, which is an unusual situation. Most GPUs have at least a few comparison points, but this accelerator's unique positioning — a compute card without display outputs, launched at a professional price point — places it outside typical gaming-centric comparison sets. Without deltaPct values or rival names, any direct competitive analysis must rely on the absolute specifications and the overall 50th percentile ranking.

Compared to its predecessor, Tesla Fermi, the K20c represents a generational leap in efficiency and compute density. The Kepler architecture improved FP32 throughput per watt and introduced better memory management. Compared to its successor, Tesla Maxwell, the K20c falls behind in both performance and power efficiency, as Maxwell refined the architecture with better scheduling and reduced power draw. The transition from PCIe 2.0 x16 to PCIe 3.0 in later generations also gave successors a bandwidth advantage over the K20c's older bus interface.

Power and Cooling

The Tesla K20c carries a TDP of 225 W, which places it in a moderate power tier for a compute accelerator of its era. The suggested PSU rating is 550 W, providing adequate headroom for the card's peak draw plus system components. Power delivery requires both a 1x 6-pin and a 1x 8-pin PCIe power connector, a configuration that was common for high-end cards in 2012 but is now typically reserved for more powerful GPUs.

The cooling solution is a dual-slot design, which is standard for a 225 W card. The physical dimensions are 267 mm in length, or 10.5 inches, which requires a case with sufficient clearance — most mid-tower and full-tower chassis accommodate this length without issue. The dual-slot footprint means the card occupies two expansion slots, reducing adjacent slot availability in dense compute servers. For multi-GPU compute nodes, this form factor limits density compared to single-slot professional cards. The 28 nm process node helps keep power draw manageable, but the 225 W TDP still generates significant heat in sustained compute workloads, necessitating adequate chassis airflow.

FAQ

Q: Does the Tesla K20c support display output for a monitor?

A: No. The card has no display outputs, making it strictly a compute accelerator that cannot drive a monitor directly.

Q: What is the memory capacity and type on this card?

A: The K20c ships with 5 GB of GDDR5 memory on a 320-bit bus, yielding 208.0 GB/s of memory bandwidth.

Q: What power supply rating is recommended for a system with this GPU?

A: The suggested PSU rating is 550 W, and the card requires both a 1x 6-pin and a 1x 8-pin PCIe power connector.

Q: Which PCIe interface does the K20c use?

A: It uses PCIe 2.0 x16, which is an older generation of the PCIe standard compared to modern PCIe 4.0 or 5.0 slots.

Q: What is the FP32 compute throughput of this card?

A: The K20c delivers 3.524 TFLOPS of FP32 performance, driven by 2,496 shading units.

Q: Is this card suitable for modern gaming?

A: No. It has no display outputs, and its 50th percentile benchmark standing indicates it would be far below contemporary gaming GPUs in performance.

Ray Tracing and Feature Set

The Tesla K20c does not include any ray tracing cores or tensor cores, as these hardware units were introduced in later architectures. The Kepler architecture relies entirely on the 2,496 shading units for all compute work, with no dedicated acceleration for ray traversal or AI inference. This means any ray tracing workloads would run entirely on the FP32 CUDA cores, which is inefficient compared to modern GPUs with dedicated RT cores.

The API support reflects the card's 2012 origins but includes some modern compatibility. DirectX 12 support is listed as 12 (11_0), meaning the card can run DirectX 12 titles but only at the feature level 11_0, which lacks several modern rendering features like bindless resources and conservative rasterization. OpenGL 4.6 is supported, which is a current version of the API. Vulkan support is listed as 1.2.175, a relatively recent version that allows access to modern Vulkan features. The combination of these APIs means the K20c can run in modern compute and graphics environments, but with limitations imposed by the missing RT and tensor cores. For compute workloads, the FP32-only path is the sole option, as no FP16 or mixed-precision capabilities are listed. The card's production status is end-of-life, and its launch MSRP was 3,199 USD, positioning it as a professional investment rather than a consumer product.