NVIDIA Quadro GV100 vs NVIDIA Tesla M40 Comparison

Head-to-Head Benchmarks

The recorded data shows a decisive performance gap between the NVIDIA Tesla M40 and the NVIDIA Quadro GV100, with the GV100 winning both head-to-head benchmark comparisons. In Geekbench OpenCL, the Quadro GV100 scores 150,004, while the Tesla M40 scores 39,192. This represents a 73.9% advantage for the GV100, a massive margin that places the two cards in entirely different performance tiers. The OpenCL workload exercises general-purpose compute across the GPU, and the GV100's result is roughly 3.8 times higher than the M40's output, indicating a fundamental difference in raw computational throughput.

The Geekbench Vulkan comparison tells a similar story, though with a slightly narrower gap. The Quadro GV100 posts 139,526 points, while the Tesla M40 manages 44,602. The delta here is 68% in favor of the GV100. Vulkan is a lower-level graphics API that often exposes more of the hardware's true capabilities, and the GV100's superior score suggests that its architectural advantages carry over into both compute and graphics-oriented workloads. The M40's Vulkan result is still respectable in absolute terms, but it falls far short of the GV100's output.

Looking at the broader context, the Tesla M40's average benchmark score across all recorded tests is 41,897, placing it at the 83rd percentile among all GPUs in the database. Its nearest rivals include the NVIDIA Tesla M40 24 GB, which scores 41,707 (a 0.5% delta), and the NVIDIA GeForce RTX 3080 Ti, which scores 41,187 (a 1.7% delta). The M40 also sits close to the AMD Radeon RX 7650 GRE, which scores 42,723 (a negative 1.9% delta relative to the M40), and the AMD Radeon Pro 5300, which scores 40,870 (a positive 2.5% delta). These figures show that the Tesla M40, despite being an older architecture, still holds its own against a range of much newer cards in synthetic benchmarks, particularly in OpenCL and Vulkan tests.

The Quadro GV100, by contrast, has an average benchmark score of 35,520, which places it at the 80th percentile among all GPUs. Its average is actually lower than the M40's average, which is a notable quirk of the data: the GV100's average is dragged down by its Passmark scores, which are very low (140 in DirectX 10, 168 in DirectX 11, 84 in DirectX 12, 207 in DirectX 9, 836 in G2D, 19,650 in G3D, and 9,069 in GPU compute). These Passmark numbers are not comparable to the Geekbench scores, and they reflect a different testing methodology. The GV100's nearest rivals include the NVIDIA GeForce RTX 5070 Ti Mobile (35,435, a 0.2% delta), the AMD Radeon Pro Duo (35,860, a negative 0.9% delta), the NVIDIA T1000 (36,289, a negative 2.1% delta), and the NVIDIA A2 (34,690, a positive 2.4% delta). This places the GV100 in a competitive zone with mid-range workstation and mobile GPUs, despite its high-end specifications.

The head-to-head data is unambiguous: the Quadro GV100 wins both recorded comparisons by wide margins. The biggest win comes in OpenCL, where the GV100 is 73.9% ahead of the M40. In Vulkan, the GV100 is 68% ahead. These deltas are so large that they cannot be explained by clock speed differences alone; they point to a generational leap in architecture, memory bandwidth, and compute resources. The M40's architecture is Maxwell 2.0, while the GV100 is built on Volta, and that architectural gap is clearly visible in the benchmark results.

The Verdict

From the data, the choice between these two cards is straightforward for most workloads. The NVIDIA Quadro GV100 is the clear winner in every head-to-head benchmark recorded. It outperforms the Tesla M40 by 73.9% in Geekbench OpenCL and by 68% in Geekbench Vulkan. If the task involves OpenCL compute, Vulkan rendering, or any workload that scales with raw shader throughput and memory bandwidth, the GV100 is the superior option by a substantial margin.

The Tesla M40 does have one advantage in the data: its average benchmark score across all tests is higher (41,897 versus 35,520), and it ranks at the 83rd percentile versus the GV100's 80th percentile. However, this average is skewed by the GV100's unusually low Passmark scores, which are not representative of its performance in Geekbench tests. The M40's average is also supported by its proximity to cards like the GeForce RTX 3080 Ti (within 1.7%) and the AMD Radeon Pro 5300 (within 2.5%), which suggests it remains a capable card for certain legacy or compute-specific workloads. But in direct head-to-head comparisons, the M40 never wins.

Who should pick the Tesla M40? Strictly from the data, it is the better choice only if the workload is confined to the specific benchmark categories where its average score holds up, and even then, the head-to-head results show it trailing. The M40's 12 GB of GDDR5 memory and 288.4 GB/s bandwidth are far below the GV100's 32 GB of HBM2 and 868.4 GB/s, and that memory deficit is a major factor in the benchmark deltas. The M40 also has no display outputs, which limits its utility in any workflow that requires driving a monitor. The GV100 offers four DisplayPort 1.4a outputs.

Who should pick the Quadro GV100? Anyone whose workload is represented by OpenCL or Vulkan benchmarks. The GV100's 5120 shading units, 640 tensor cores, and 16.66 TFLOPS of FP32 performance give it a massive compute advantage. Its 32 GB of HBM2 memory with 4096-bit bus width and 868.4 GB/s bandwidth is in a different class from the M40's 12 GB GDDR5. The GV100 also supports FP16 at 33.32 TFLOPS (2:1), a feature the M40 lacks entirely. For scientific computing, machine learning inference, or any task that leverages tensor cores or high-bandwidth memory, the GV100 is the only rational choice based on the recorded data.

FAQ

Q: Which card wins in Geekbench OpenCL?

A: The NVIDIA Quadro GV100, with a score of 150,004 versus the Tesla M40's 39,192, a 73.9% advantage.

Q: How do the cards compare in Geekbench Vulkan?

A: The GV100 scores 139,526, while the M40 scores 44,602. The GV100 leads by 68%.

Q: Does the Tesla M40 have any benchmark where it wins?

A: No. The head-to-head data records zero wins for the M40 and two wins for the GV100.

Q: What is the average benchmark score for each card?

A: The Tesla M40 averages 41,897 across all recorded tests, while the Quadro GV100 averages 35,520. The M40's average is higher, but the GV100 wins every direct head-to-head comparison.

Q: Which card has more memory and bandwidth?

A: The Quadro GV100 has 32 GB of HBM2 memory with 868.4 GB/s bandwidth. The Tesla M40 has 12 GB of GDDR5 with 288.4 GB/s bandwidth.

Q: Do both cards support the same graphics APIs?

A: Yes, both support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. However, the GV100 adds FP16 compute capability and tensor cores, which the M40 does not have.

Specification Differences

The two cards differ in nearly every major specification category. The Tesla M40 is built on the GM200 chip using Maxwell 2.0 architecture, fabricated on a 28 nm process at TSMC. The Quadro GV100 uses the GV100 chip with Volta architecture on a 12 nm process, also at TSMC. The transistor counts differ dramatically: the M40 has 8,000 million transistors on a 601 mm² die, while the GV100 has 21,100 million transistors on an 815 mm² die. This works out to a transistor density of 13.3 million per mm² for the M40 and 25.9 million per mm² for the GV100.

Clock speeds are higher on the GV100. The M40 runs at a base clock of 948 MHz with a boost of 1112 MHz, while the GV100 runs at 1132 MHz base and 1627 MHz boost. Memory also differs significantly: the M40 uses 12 GB of GDDR5 with a 384-bit bus and 288.4 GB/s bandwidth, while the GV100 uses 32 GB of HBM2 with a 4096-bit bus and 868.4 GB/s bandwidth. The memory clock is listed as 1502 MHz (6 Gbps effective) for the M40 and 848 MHz (1696 Mbps effective) for the GV100.

The compute resources are substantially different. The M40 has 3072 shading units, 192 texture mapping units, and 96 render output units. The GV100 has 5120 shading units, 320 TMUs, and 128 ROPs. The GV100 also includes 640 tensor cores, a feature the M40 does not have. Pixel and texture rates reflect these differences: the M40 achieves 106.8 GPixel/s and 213.5 GTexel/s, while the GV100 achieves 208.3 GPixel/s and 520.6 GTexel/s. FP32 performance is 6.832 TFLOPS for the M40 and 16.66 TFLOPS for the GV100. The GV100 also lists FP16 performance at 33.32 TFLOPS (2:1), while the M40 has no FP16 figure recorded.

Other differences include display outputs: the M40 has none, while the GV100 has four DisplayPort 1.4a outputs. The M40 uses an 8-pin EPS power connector, while the GV100 uses a single 8-pin connector. Both are dual-slot cards with a 250 W TDP and a suggested PSU of 600 W. The M40 is 267 mm (10.5 inches) long with no recorded height, while the GV100 is also 267 mm long but has a recorded height of 111 mm (4.4 inches). The M40 was released in November 2015, while the GV100 was released in March 2018. The M40 has no recorded launch MSRP, while the GV100 has a launch MSRP of 8,999 USD.

Architecture Differences

The architectural gap between these two cards is the primary driver of the benchmark results. The Tesla M40 uses Maxwell 2.0, NVIDIA's architecture from the 28 nm era. It is built on the GM200 chip, which is a large die but lacks the specialized compute units found in later architectures. Maxwell 2.0 has no tensor cores, no dedicated FP16 throughput, and no ray tracing hardware. The M40 is a pure compute and graphics card, designed for workloads that rely on FP32 shader performance and memory bandwidth. Its 8,000 million transistors and 601 mm² die size were impressive for 2015, but the architecture is now two generations behind the GV100's Volta design.

The Quadro GV100 uses Volta, NVIDIA's architecture that introduced tensor cores for deep learning and AI workloads. The GV100 chip contains 21,100 million transistors on an 815 mm² die, and its 640 tensor cores provide dedicated hardware for matrix operations. This is a significant architectural advantage over the M40, which has no such units. Volta also introduces a 2:1 FP16 ratio, meaning the GV100 can process FP16 data at twice the rate of FP32. The M40 has no FP16 support listed at all. These architectural features explain why the GV100 dominates in OpenCL and Vulkan benchmarks: it has more shading units, more TMUs, more ROPs, and a much larger memory subsystem.

The memory architecture is another key difference. The M40 uses GDDR5 on a 384-bit bus, which was standard for high-end GPUs in 2015. The GV100 uses HBM2 on a 4096-bit bus, which provides vastly higher bandwidth (868.4 GB/s versus 288.4 GB/s) and a larger capacity (32 GB versus 12 GB). HBM2 also has different power characteristics and physical packaging, though both cards share a 250 W TDP. The GV100's memory advantage is directly visible in the benchmark deltas: memory-bound workloads see the largest performance gains.

The manufacturing process also differs. The M40 is fabricated on a 28 nm process, while the GV100 uses a 12 nm process. This explains the transistor density difference: the GV100 packs 25.9 million transistors per mm², nearly double the M40's 13.3 million per mm². The smaller process node allows higher clock speeds (1627 MHz boost versus 1112 MHz) while maintaining the same 250 W TDP. The GV100's higher transistor count and density enable its additional compute units, tensor cores, and larger memory controller.

Finally, the two cards occupy different product categories. The Tesla M40 is a compute-oriented card with no display outputs, designed for servers and data centers. The Quadro GV100 is a workstation card with four DisplayPort 1.4a outputs, intended for professional visualization and compute. This difference is reflected in their feature sets: the M40 is a pure accelerator, while the GV100 can drive displays. The GV100's successor is Quadro Turing, while the M40's successor is Tesla Pascal. Both cards are end-of-life, but the GV100's later release date (2018 versus 2015) means it incorporates more recent architectural developments. The recorded data leaves no ambiguity: the Quadro GV100 is the superior card in every head-to-head benchmark, and its architectural advantages in compute, memory, and feature set explain why.