NVIDIA A2 vs NVIDIA Tesla M40 24 GB Comparison

The NVIDIA Tesla M40 24 GB and the NVIDIA A2 represent two distinct eras of NVIDIA's data center strategy, separated by six years of architectural evolution. The recorded data shows a clear performance hierarchy, with the older Maxwell-based card taking a decisive lead in both recorded benchmark tests, despite its dated design. However, the A2 counters with massive efficiency gains and modern feature support, presenting a trade-off between raw compute and operational footprint.

FAQ

Q: Which GPU is faster in the recorded benchmark suite?

A: The NVIDIA Tesla M40 24 GB wins both head-to-head tests. It scores 37,439 in Geekbench OpenCL versus 35,357 for the A2, a 5.9% advantage, and 45,975 in Geekbench Vulkan versus 34,023, a 35.1% lead.

Q: How do the two cards compare in average benchmark scores?

A: The M40 24 GB has an average benchmark score of 41,707, placing it in the 83rd percentile of all GPUs. The A2 averages 34,690, placing it in the 79th percentile. The M40's average is 20.2% higher than the A2's average.

Q: What is the major efficiency difference between the two cards?

A: The A2 has a thermal design power (TDP) of 60 W and requires no power connectors, while the M40 24 GB has a TDP of 250 W and requires an 8-pin EPS connector. The A2's suggested power supply is 250 W compared to 600 W for the M40.

Q: Do both cards support the same modern graphics APIs?

A: No. The A2 supports DirectX 12 Ultimate (12_2), while the M40 24 GB is limited to DirectX 12 (12_1). Both support OpenGL 4.6 and Vulkan 1.4.

Q: Which card has more memory and bandwidth?

A: The M40 24 GB has 24 GB of GDDR5 on a 384-bit bus, yielding 288.4 GB/s of bandwidth. The A2 has 16 GB of GDDR6 on a 128-bit bus, yielding 200.1 GB/s. The M40 has 50% more memory capacity and 44.1% more bandwidth.

Q: What is the production status of each card?

A: Both are listed as end-of-life. The M40 24 GB was released in November 2015, while the A2 was released in November 2021, six years later.

Architecture Differences

The architectural gap between these two GPUs is substantial, reflecting the generational shift from NVIDIA's Maxwell 2.0 to Ampere designs. The M40 24 GB is built on the GM200 chip using TSMC's 28 nm process, packing 8,000 million transistors into a large 601 mm² die, resulting in a transistor density of 13.3 million per square millimeter. In contrast, the A2 uses the GA107 chip fabricated on Samsung's 8 nm process, containing 8,700 million transistors on a much smaller 200 mm² die, achieving a density of 43.5 million per square millimeter, more than three times the M40's density.

The compute configuration diverges sharply. The M40 24 GB fields 3,072 shading units, 192 texture mapping units, and 96 render output units. The A2 is more modest in raw count, with 1,280 shading units, 40 TMUs, and 32 ROPs. However, the A2 introduces dedicated hardware that the M40 completely lacks: 10 ray tracing cores and 40 tensor cores. This is a fundamental architectural difference, as the Maxwell design predates both ray tracing acceleration and tensor core-based AI processing.

Clock speeds also tell the story of process node evolution. The M40 runs at a base clock of 948 MHz with a boost of 1,112 MHz. The A2 operates at a base of 1,440 MHz and boosts to 1,770 MHz, a 51.9% higher base clock and a 59.2% higher boost clock. Memory technology differs as well, with the M40 using 1,502 MHz GDDR5 (6 Gbps effective) and the A2 using 1,563 MHz GDDR6 (12.5 Gbps effective), giving the newer card faster memory signaling despite its narrower bus.

The physical specifications diverge significantly. The M40 is a dual-slot card measuring 267 mm (10.5 inches) in length, while the A2 is a single-slot design with no listed dimensions. Power delivery is a stark contrast: the M40 draws 250 W and requires an 8-pin EPS connector, while the A2 draws only 60 W and requires no power connector at all. The A2 also upgrades the bus interface to PCIe 4.0 x8, while the M40 uses PCIe 3.0 x16.

The Verdict

The benchmark data presents a clear conclusion: the NVIDIA Tesla M40 24 GB is the superior performer for raw compute workloads, winning both recorded head-to-head tests. Its 35.1% lead in Geekbench Vulkan is particularly decisive, indicating a substantial advantage in graphics-oriented tasks that leverage that API. The M40 also holds the edge in memory capacity and bandwidth, with 24 GB versus 16 GB and 288.4 GB/s versus 200.1 GB/s, making it the better choice for large datasets that require substantial memory residency.

However, the NVIDIA A2 is the logical choice for deployment scenarios constrained by power and space. Its 60 W TDP, no power connector requirement, and single-slot form factor allow it to be deployed in dense configurations where the M40's 250 W dual-slot design would be impractical. The A2's support for DirectX 12 Ultimate and its ray tracing and tensor cores provide feature-level advantages that the M40 cannot match, despite the M40's raw compute lead.

For workloads that depend on FP32 throughput, the M40's 6.832 TFLOPS versus the A2's 4.531 TFLOPS gives it a 50.8% advantage. The M40 also leads in pixel rate at 106.8 GPixel/s versus 56.64 GPixel/s, and texture rate at 213.5 GTexel/s versus 70.80 GTexel/s. Yet the A2's 1:1 FP16 capability at 4.531 TFLOPS opens doors to mixed-precision workflows that the M40, with no listed FP16 performance, cannot accommodate.

Specification Differences

The two cards differ across nearly every specification category. The process node moves from 28 nm (M40) to 8 nm (A2), with the foundry shifting from TSMC to Samsung. Transistor count is nearly identical at 8,000 million versus 8,700 million, but the die size shrinks from 601 mm² to 200 mm², and density increases from 13.3M per mm² to 43.5M per mm².

Clock speeds favor the A2: base clock is 948 MHz versus 1,440 MHz, boost clock is 1,112 MHz versus 1,770 MHz. Memory frequency is higher on the A2 at 1,563 MHz (12.5 Gbps effective) versus 1,502 MHz (6 Gbps effective), but the M40 compensates with a 384-bit bus versus 128-bit, giving it 288.4 GB/s versus 200.1 GB/s bandwidth.

Compute unit counts favor the M40: 3,072 shading units versus 1,280, 192 TMUs versus 40, and 96 ROPs versus 32. The A2 uniquely offers 10 RT cores and 40 tensor cores. Pixel and texture rates are higher on the M40 (106.8 GPixel/s versus 56.64 GPixel/s, and 213.5 GTexel/s versus 70.80 GTexel/s). FP32 output is 6.832 TFLOPS versus 4.531 TFLOPS, while FP16 is only listed for the A2 at 4.531 TFLOPS (1:1).

Power and physical specs are heavily divergent: TDP is 250 W versus 60 W, slot width is dual-slot versus single-slot, power connector is 8-pin EPS versus none, and suggested PSU is 600 W versus 250 W. The bus interface is PCIe 3.0 x16 versus PCIe 4.0 x8. The M40 is 267 mm long; the A2 has no listed dimensions. DirectX support differs: 12 (12_1) versus 12 Ultimate (12_2). The M40 has no RT or tensor cores; the A2 has 10 and 40, respectively.

Head-to-Head Benchmarks

The head-to-head data contains two tests, both won by the M40 24 GB. The Geekbench OpenCL result shows the M40 scoring 37,439 against the A2's 35,357, a 5.9% difference. This is a modest lead, suggesting that in OpenCL workloads, the two cards are relatively close, with the M40's higher compute throughput and memory bandwidth giving it a measurable but not overwhelming edge.

The Geekbench Vulkan result is far more lopsided. The M40 scores 45,975, while the A2 manages only 34,023, resulting in a 35.1% delta. This substantial gap indicates that the M40's architecture is significantly better suited to Vulkan's execution model, likely benefiting from its 3,072 shading units and 96 ROPs, which provide superior rasterization and pixel processing throughput. The A2's lower ROP count of 32 and smaller shading unit count of 1,280 place it at a clear disadvantage in this API.

The average benchmark scores reinforce the M40's overall superiority. Its average of 41,707 sits 20.2% above the A2's 34,690. In the context of the database's nearest rivals, the M40's average is closest to the NVIDIA GeForce RTX 3080 Ti at 41,187 (1.3% delta) and the AMD Radeon Pro 5300 at 40,870 (2% delta). The A2's average is closest to the NVIDIA T1000 8 GB at 34,561 (0.4% delta) and the AMD Radeon HD 7970 at 34,541 (0.4% delta). This places the M40 in a performance tier occupied by high-end consumer and workstation cards, while the A2 sits alongside entry-level professional GPUs.

Where Each One Wins

The M40 24 GB wins decisively in raw compute and memory-intensive scenarios. Its 24 GB VRAM, compared to 16 GB, provides 50% more capacity for large models or datasets that must reside entirely in GPU memory. Its bandwidth advantage of 288.4 GB/s versus 200.1 GB/s means it can feed its 3,072 shaders more effectively, sustaining higher throughput in bandwidth-bound workloads. The FP32 output of 6.832 TFLOPS versus 4.531 TFLOPS gives it a 50.8% advantage for single-precision compute, and its pixel and texture rates are roughly double and triple those of the A2, respectively. For any workload that is not power-constrained, the M40 is the clear choice.

The A2 wins in efficiency and modern feature support. Its 60 W TDP is 76% lower than the M40's 250 W, and its lack of a power connector makes it far easier to integrate into existing systems without PSU upgrades. The suggested PSU of 250 W versus 600 W highlights the operational cost difference. The A2's 10 RT cores and 40 tensor cores provide hardware acceleration for ray tracing and AI inference, workloads that the M40 cannot accelerate at all. Its DirectX 12 Ultimate support enables features unavailable on the M40's DirectX 12 (12_1) implementation. The A2's 1:1 FP16 ratio at 4.531 TFLOPS also makes it viable for mixed-precision workflows, whereas the M40 has no listed FP16 capability.

The use-case split is clear: the M40 24 GB targets compute-heavy, memory-hungry applications where power consumption is not a limiting factor, such as traditional rendering or scientific simulation. The A2 targets low-power inference, edge deployment, and dense server configurations where its small footprint and minimal power draw are paramount, and where its tensor cores can be leveraged for AI workloads.