NVIDIA Tesla K40m

The NVIDIA Tesla K40m is a Kepler-architecture compute card from late 2013, built for professional workloads rather than consumer gaming. It pairs 12 GB of GDDR5 memory with 2,880 shading units, and its benchmark data places it in a surprisingly competitive position against both older and newer hardware in raw compute throughput.

Benchmark Performance

The Tesla K40m’s sole recorded benchmark score is 19,519 points in Geekbench OpenCL. This places it at the 62nd percentile among all GPUs, meaning it outperforms roughly 62% of the tested database. The score is a measure of raw compute capability, not gaming frame rates, and it reflects the card’s design for parallel processing tasks.

The data shows a tight cluster of rivals around this score. The K40m sits just 0.5% above the AMD Radeon Pro 560X, which scores 19,426. That is a virtual tie — a margin of only 93 points. Against the NVIDIA Quadro K5200, the K40m is 0.5% slower, with the K5200 scoring 19,623. Again, this is within noise for a single benchmark run.

More interesting is the comparison to consumer gaming cards. The GeForce GTX 780 scores 19,405, placing the K40m 0.6% above it. The GeForce GTX 1080 Ti, a much newer and more powerful gaming card, scores 19,402 — also 0.6% below the K40m. The practical takeaway: in pure OpenCL compute, the aging Tesla K40m holds its own against hardware that is several generations newer. The 1080 Ti’s gaming dominance does not translate to a lead in this specific compute test.

The delta percentages are all under 1%, which means the K40m is effectively tied with all four rivals in this workload. The 62nd percentile ranking reinforces that picture: this is a mid-tier performer in the current database, not a top-tier card, but also not obsolete. The K40m’s 5.046 TFLOPS of FP32 compute is the architectural driver behind this score, and it explains why the card remains relevant in compute benchmarks despite its age.

How It Compares

AMD Radeon Pro 560X: The K40m edges out this mobile workstation card by 0.5%. Both are professional-oriented GPUs, but the Radeon is a much lower-power part. The K40m’s 12 GB VRAM and 384-bit bus give it a memory bandwidth advantage (288.4 GB/s vs. the Radeon’s unspecified figure), which matters for large datasets. In compute, they are functionally equivalent.

NVIDIA Quadro K5200: The K5200 leads by 0.5%, a margin of 104 points. This is the closest comparison in the pack. Both cards target professional visualization and compute, and the K5200’s slight edge likely comes from its higher boost clock architecture. The K40m counters with twice the VRAM (12 GB vs. the K5200’s unspecified amount), making it the better choice for memory-bound workloads.

NVIDIA GeForce GTX 780: The K40m is 0.6% faster than this consumer Kepler card. They share the same GK110 architecture family, but the K40m has more VRAM (12 GB vs. 3 GB) and a wider memory bus. The GTX 780 was designed for gaming; the K40m for compute. Their near-identical OpenCL scores show that the underlying silicon is similar, but the K40m’s memory subsystem gives it a durability edge for professional tasks.

NVIDIA GeForce GTX 1080 Ti: This is the most surprising result. The 1080 Ti, a 2017 flagship, scores 0.6% below the 2013 K40m. The 1080 Ti has far higher gaming performance, but in this specific OpenCL test, the K40m’s compute-oriented design (higher FP32 throughput relative to its shading units) keeps it competitive. The K40m’s 288.4 GB/s bandwidth and 5.046 TFLOPS are sufficient to match the newer card’s raw compute output.

Who Should Consider It

The K40m is not a gaming card — it has no display outputs, so you cannot connect a monitor directly. Its benchmark score of 19,519 in OpenCL indicates it is suited for compute workloads like scientific simulation, data processing, or machine learning inference where raw FP32 throughput matters. The 62nd percentile ranking means it will handle moderate compute tasks without issue, but it will lag behind modern high-end accelerators.

For users working with large datasets, the 12 GB VRAM is the standout feature. At 4K resolution or with high-resolution textures, memory capacity often becomes the bottleneck before compute power. The K40m’s 12 GB and 384-bit bus providing 288.4 GB/s bandwidth can hold substantial working sets. This makes it viable for tasks like rendering, video encoding, or medical imaging that need large memory pools.

The card’s 245 W TDP and dual-slot design mean it requires a proper workstation chassis with adequate cooling. The suggested PSU of 550 W is modest by modern standards. If your workload fits within the K40m’s 5.046 TFLOPS of FP32 compute, the benchmark data shows it performs on par with much newer cards. However, the end-of-life production status and lack of modern features like ray tracing or tensor cores mean it is not a future-proof investment.

FAQ

Q: How does the Tesla K40m perform in OpenCL benchmarks?

A: It scores 19,519 in Geekbench OpenCL, placing it at the 62nd percentile of all GPUs in the database.

Q: Is the K40m faster than a GTX 1080 Ti in compute?

A: In this specific OpenCL test, yes — the K40m scores 0.6% higher than the GTX 1080 Ti (19,519 vs. 19,402).

Q: Can I use the K40m for gaming?

A: No. The card has no display outputs, so it cannot connect to a monitor. It is designed for compute workloads only.

Q: How much VRAM does the K40m have?

A: It has 12 GB of GDDR5 memory on a 384-bit bus, providing 288.4 GB/s of bandwidth.

Q: What is the difference between the K40m and Quadro K5200?

A: The K5200 scores 0.5% higher in OpenCL (19,623 vs. 19,519). The K40m has 12 GB VRAM, while the K5200 has an unspecified amount, but the K40m’s memory configuration is its primary advantage.

Q: Is the K40m still worth using?

A: For compute tasks that fit within its 5.046 TFLOPS of FP32 performance and 12 GB memory, the benchmark data shows it performs on par with newer cards like the GTX 1080 Ti. However, it is end-of-life and lacks modern features like ray tracing or tensor cores.

Ray Tracing and Feature Set

The K40m has no ray tracing cores and no tensor cores. It is a pure compute card based on the Kepler architecture (GK110B chip), which predates hardware-accelerated ray tracing by several generations. The card’s feature set is focused on raw FP32 compute: 2,880 shading units, 240 texture mapping units, and 48 raster operation units deliver 5.046 TFLOPS of FP32 performance and 210.2 GTexel/s of texture fill rate.

API support is limited by its age. The K40m supports DirectX 12 (11_1), OpenGL 4.6, and Vulkan 1.2.175. The DirectX 12 support is partial (11_1 feature level), which means it cannot take full advantage of modern DirectX 12 features. OpenGL 4.6 and Vulkan 1.2.175 are more current, but the absence of dedicated ray tracing hardware means any ray-traced workload would run on general-purpose shaders, which is inefficient.

The card’s 28 nm process node and 7,080 million transistors on a 561 mm² die reflect its 2013 design era. The transistor density of 12.6 million per mm² is low by modern standards, but the architecture was optimized for compute throughput rather than power efficiency. With a 245 W TDP, it draws significant power for its performance level, but the 550 W suggested PSU requirement is manageable in a workstation.

Memory Subsystem

The K40m’s memory subsystem is its most distinctive feature. It packs 12 GB of GDDR5 memory on a 384-bit bus, yielding 288.4 GB/s of bandwidth. This configuration was exceptional for 2013 — most consumer cards had 3-4 GB — and it remains useful today for datasets that exceed the VRAM of newer cards.

The 12 GB capacity is the key differentiator. For compute workloads like deep learning inference or large-scale rendering, memory capacity often limits what models or scenes can be processed. The 384-bit bus provides high bandwidth for feeding the 2,880 shading units, and the 288.4 GB/s figure is sufficient to keep the GPU busy in memory-bound tasks. Memory operates at 1502 MHz (6 Gbps effective), which is standard for GDDR5 of that era.

The 48 ROPs deliver a pixel rate of 52.56 GPixel/s, which is modest by modern standards but irrelevant for a compute card with no display outputs. The texture rate of 210.2 GTexel/s aligns with the card’s compute focus. For high-resolution work, the 12 GB buffer is the selling point — it can hold textures, geometry, or data arrays that would cause out-of-memory errors on 8 GB cards. The bandwidth, while not class-leading today, is adequate for the K40m’s compute capability, as evidenced by its competitive OpenCL score against newer hardware.