NVIDIA Tesla M40 vs NVIDIA Tesla M40 24 GB Comparison

The NVIDIA Tesla M40 and NVIDIA Tesla M40 24 GB are near-identical twins separated almost entirely by memory capacity, yet benchmark results show a genuine split in workload suitability. The standard 12 GB model wins the OpenCL compute race by a decisive margin, while the 24 GB variant takes the Vulkan crown, making the choice between them a matter of which API and memory ceiling matters more for the intended task.

Head-to-Head Benchmarks

The two cards split their two head-to-head benchmark wins exactly one apiece, but the margins are not symmetrical. In Geekbench OpenCL, the standard NVIDIA Tesla M40 scores 39,192 against 37,439 for the 24 GB model, a 4.7% delta in favor of the 12 GB card. This is the larger of the two performance gaps and suggests that the additional memory on the 24 GB variant does not translate into raw compute throughput advantages in this particular test — in fact, it appears to cost a measurable amount of performance.

The situation reverses in Geekbench Vulkan, where the NVIDIA Tesla M40 24 GB posts 45,975 versus 44,602 for the standard model, a 3% delta in the 24 GB card's favor. This is a meaningful swing: the 24 GB card outperforms its sibling by roughly three points for every hundred in Vulkan workloads, while the standard card outperforms the 24 GB variant by nearly five points per hundred in OpenCL. The average benchmark scores tell a similar story of near-parity: the standard M40 averages 41,897 across its two tests, while the 24 GB model averages 41,707, a 0.5% difference that places the standard card marginally ahead in the aggregate.

Against external rivals, both cards occupy the same percentile neighborhood. The standard M40 holds an 83rd percentile ranking among all GPUs, with an average score 1.7% above the NVIDIA GeForce RTX 3080 Ti (41,187) and 2.5% above the AMD Radeon Pro 5300 (40,870). The 24 GB model also ranks at the 83rd percentile, sitting 1.3% above the RTX 3080 Ti and 2% above the Radeon Pro 5300. Notably, the AMD Radeon RX 7650 GRE (42,723) beats both M40 variants, with the standard card trailing by 1.9% and the 24 GB card trailing by 2.4%. The data shows these two Tesla cards are effectively peer products in overall compute capability, with the 24 GB model's extra memory being the only meaningful differentiator.

Architecture Differences

Both cards share an identical architectural foundation, so the differences between them are minimal and confined to memory configuration. The chip is the GM200 on the Maxwell 2.0 architecture, manufactured on a 28 nm process at TSMC, with 8,000 million transistors packed into a 601 mm² die. Transistor density is 13.3 million per square millimeter. The shading unit count is 3,072, with 192 texture mapping units and 96 raster operation pipelines. Pixel rate is 106.8 GPixel/s, texture rate is 213.5 GTexel/s, and FP32 throughput is 6.832 TFLOPS. Neither card has RT cores or tensor cores, and neither supports FP16 operations in the data provided.

Clock speeds are identical across both cards: base clock of 948 MHz, boost clock of 1112 MHz, and memory clock of 1502 MHz with 6 Gbps effective transfer. The memory bus width is 384 bit on both, and bandwidth is 288.4 GB/s on both. The only specification difference is memory size: 12 GB on the standard model versus 24 GB on the 24 GB variant. Both use GDDR5 memory, both draw 250 W TDP, both are dual-slot with an 8-pin EPS power connector, and both require a 600 W suggested PSU. The PCIe interface is 3.0 x16 on both, and both have no display outputs, indicating a pure compute-oriented design. The cards are the same physical length at 267 mm (10.5 inches), support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, and share the same release date of November 9, 2015. Both are end-of-life products, with the Tesla Kepler as predecessor and Tesla Pascal as successor.

Where Each One Wins

The benchmark split creates a clear use-case division. The standard NVIDIA Tesla M40 wins in Geekbench OpenCL by 4.7%, making it the stronger choice for compute workloads that rely on OpenCL as the execution API. This could include scientific computing, rendering pipelines, or any OpenCL-accelerated application where raw throughput in this specific benchmark matters. The 4.7% margin is not trivial — it represents a real performance advantage that would manifest in longer-running OpenCL tasks.

The NVIDIA Tesla M40 24 GB wins in Geekbench Vulkan by 3%, making it the better option for Vulkan-based workloads. The 3% advantage is slightly smaller than the standard card's OpenCL win, but it is still a meaningful edge. More importantly, the 24 GB memory capacity doubles the available frame buffer, which is the primary reason to choose this card. For workloads that need to hold large datasets, models, or textures in memory, the 24 GB capacity is the decisive factor, even if the per-clock compute performance is identical.

In aggregate, the standard M40 edges ahead by 0.5% in average benchmark score, but that margin is within noise and should not drive a purchasing decision. The real question is whether the workload is OpenCL-heavy or Vulkan-heavy, and whether the memory ceiling of 12 GB is sufficient. If the workload fits within 12 GB, the standard card offers the better OpenCL performance. If the workload exceeds 12 GB or leans on Vulkan, the 24 GB card is the correct pick.

FAQ

Q: Which card has the higher average benchmark score?

A: The NVIDIA Tesla M40 has an average benchmark score of 41,897, while the NVIDIA Tesla M40 24 GB has an average of 41,707, putting the standard card 0.5% ahead.

Q: How much faster is the winning card in OpenCL?

A: The NVIDIA Tesla M40 scores 39,192 in Geekbench OpenCL compared to 37,439 for the 24 GB model, a 4.7% advantage.

Q: What is the Vulkan performance difference between the two?

A: The NVIDIA Tesla M40 24 GB scores 45,975 in Geekbench Vulkan versus 44,602 for the standard M40, giving the 24 GB card a 3% lead.

Q: Do the cards have the same memory bandwidth?

A: Yes, both cards have a 384 bit memory bus and 288.4 GB/s bandwidth, though the standard model has 12 GB of GDDR5 while the 24 GB model has 24 GB.

Q: What is the memory clock speed on both cards?

A: Both cards run their memory at 1502 MHz with 6 Gbps effective transfer.

Q: Are these cards still in production?

A: No, both are end-of-life products, released on November 9, 2015.

Specification Differences

The two cards differ in exactly one specification field: memory size. The NVIDIA Tesla M40 has 12 GB of GDDR5 memory, while the NVIDIA Tesla M40 24 GB has 24 GB of the same memory type. All other specifications are identical:

• Chip: GM200 on Maxwell 2.0 architecture
• Process: 28 nm at TSMC, 8,000 million transistors, 601 mm² die
• Clocks: 948 MHz base, 1112 MHz boost, 1502 MHz memory
• Memory bus: 384 bit, 288.4 GB/s bandwidth
• Compute: 3,072 shading units, 192 TMUs, 96 ROPs
• Rates: 106.8 GPixel/s, 213.5 GTexel/s, 6.832 TFLOPS FP32
• Power: 250 W TDP, dual-slot, 8-pin EPS, 600 W suggested PSU
• Interface: PCIe 3.0 x16, no display outputs
• APIs: DirectX 12 (12_1), OpenGL 4.6, Vulkan 1.4
• Dimensions: 267 mm length, 10.5 inches

The Verdict

The data presents a straightforward choice with a single caveat. If the workload is OpenCL-based and fits within 12 GB of memory, the standard NVIDIA Tesla M40 is the better card — its 4.7% OpenCL advantage over the 24 GB model is the largest performance gap between the two in any benchmark. The standard card also holds a 0.5% edge in average benchmark score, reinforcing its status as the marginally faster compute product.

If the workload is Vulkan-based or requires more than 12 GB of memory, the NVIDIA Tesla M40 24 GB is the clear winner. Its 3% Vulkan advantage is significant, and the doubled memory capacity from 12 GB to 24 GB is the only specification difference that could justify choosing one card over the other. For machine learning inference, large dataset processing, or rendering workloads with massive texture sets, the 24 GB capacity is likely the deciding factor regardless of the modest Vulkan performance lead.

Both cards sit at the 83rd percentile among all GPUs, and both outperform the NVIDIA GeForce RTX 3080 Ti and AMD Radeon Pro 5300 in average benchmark score while trailing the AMD Radeon RX 7650 GRE. The choice between them should hinge on the primary API used and the memory ceiling required. For pure OpenCL compute where memory is not a constraint, take the standard M40. For Vulkan workloads or memory-hungry tasks, take the 24 GB model. The data does not support a universal recommendation — it supports a workload-specific one.