NVIDIA GeForce MX570 A vs NVIDIA Tesla M40 24 GB Comparison

The NVIDIA Tesla M40 24 GB and the NVIDIA GeForce MX570 A are fundamentally different products that win in different benchmark environments. The Tesla M40 24 GB dominates in Vulkan compute, scoring 45,975 versus 37,601 for the MX570 A, a decisive 22.3% advantage. However, the MX570 A takes the OpenCL crown with a score of 39,780 against the Tesla’s 37,439, a 5.9% lead. The data shows a split decision: the Tesla M40 24 GB is the compute-heavy workstation card, while the MX570 A is a modern, power-efficient mobile chip that excels in API-specific workloads. Each card wins exactly one head-to-head benchmark, making the choice entirely dependent on the target application.

Where Each One Wins

The Tesla M40 24 GB is the clear winner for Vulkan-based workloads. Its Vulkan score of 45,975 is not just higher than the MX570 A’s 37,601; it is also 22.3% faster in that specific test, which is the largest performance gap between the two cards in any benchmark. This suggests the Maxwell 2.0 architecture, despite its age, holds a significant advantage in Vulkan compute tasks, likely due to its massive 384-bit memory bus and 24 GB of GDDR5 memory. For users running Vulkan applications, the Tesla M40 24 GB is the only choice between these two.

The MX570 A wins in OpenCL, posting 39,780 points versus the Tesla’s 37,439. This 2,341-point difference translates to a 5.9% lead. The MX570 A’s advantage here is notable because it achieves this with far less hardware: 2 GB of GDDR6 memory on a 64-bit bus, compared to the Tesla’s 24 GB on a 384-bit bus. The Ampere architecture’s newer design, including 16 ray-tracing cores and 64 tensor cores, likely contributes to more efficient OpenCL execution. For OpenCL-specific compute tasks, the MX570 A is the superior option.

Beyond the head-to-head numbers, the overall average benchmark scores reinforce the split. The Tesla M40 24 GB has an average benchmark score of 41,707, placing it at the 83rd percentile of all GPUs. The MX570 A averages 38,691, sitting at the 81st percentile. While the Tesla leads in overall average, the gap is narrow (about 7.8%), and the MX570 A’s OpenCL victory shows it can outperform in specific scenarios. The MX570 A also wins on efficiency, with a 25 W TDP compared to the Tesla’s 250 W, making it the winner for any power-constrained or mobile deployment.

FAQ

Q: Which card has the higher average benchmark score?

A: The NVIDIA Tesla M40 24 GB has a higher average benchmark score of 41,707, compared to 38,691 for the NVIDIA GeForce MX570 A. This places the Tesla at the 83rd percentile of all GPUs versus the 81st percentile for the MX570 A.

Q: What is the biggest performance difference in head-to-head testing?

A: The largest gap is in the Geekbench Vulkan test, where the Tesla M40 24 GB scores 45,975, which is 22.3% higher than the MX570 A’s 37,601. This is a substantial margin, far exceeding the 5.9% difference seen in the OpenCL test.

Q: Does the MX570 A beat the Tesla M40 24 GB in any benchmark?

A: Yes, the MX570 A wins the Geekbench OpenCL test with a score of 39,780, beating the Tesla M40 24 GB’s 37,439. This represents a 5.9% advantage for the MX570 A.

Q: How do these cards compare to their nearest rivals?

A: The Tesla M40 24 GB is closely matched with the NVIDIA Tesla M40, which has an average score of 41,897, a -0.5% delta. The MX570 A is nearly identical to the AMD Radeon Pro 580X, which scores 38,706, a 0% delta, and is also 1% ahead of the NVIDIA GeForce MX570.

Q: Which card has better Vulkan performance?

A: The Tesla M40 24 GB has decisively better Vulkan performance. Its Geekbench Vulkan score of 45,975 is 22.3% higher than the MX570 A’s 37,601, making it the clear winner for Vulkan-based applications.

Q: What is the memory configuration difference that impacts performance?

A: The Tesla M40 24 GB features 24 GB of GDDR5 memory on a 384-bit bus, delivering 288.4 GB/s bandwidth. The MX570 A has only 2 GB of GDDR6 on a 64-bit bus, providing 96.00 GB/s. This threefold difference in bandwidth explains the Tesla’s advantage in memory-intensive workloads.

Head-to-Head Benchmarks

The two cards split their head-to-head matchups, with each winning one test. The most striking result is in Geekbench Vulkan, where the Tesla M40 24 GB scores 45,975 against the MX570 A’s 37,601. This 8,374-point difference gives the Tesla a 22.3% lead, the largest margin in any comparison between the two. This result is consistent with the Tesla’s overall hardware advantage: it has 3,072 shading units, 192 TMUs, and 96 ROPs, versus 2,048 shading units, 64 TMUs, and 32 ROPs on the MX570 A. The Tesla’s 24 GB memory buffer and 288.4 GB/s bandwidth also dwarf the MX570 A’s 2 GB and 96.00 GB/s, which matters greatly in Vulkan compute tasks.

In Geekbench OpenCL, the tables turn. The MX570 A scores 39,780, beating the Tesla M40 24 GB’s 37,439 by 2,341 points, a 5.9% margin. This is a significant upset given the Tesla’s larger silicon and memory resources. The MX570 A’s Ampere architecture, built on an 8 nm process at Samsung, supports features the Tesla lacks, such as 16 ray-tracing cores and 64 tensor cores. The Tesla M40 24 GB, based on 28 nm Maxwell 2.0, has no such accelerators. The MX570 A also runs at a higher boost clock of 1155 MHz versus 1112 MHz on the Tesla, and its GDDR6 memory operates at 12 Gbps effective, double the Tesla’s 6 Gbps. These architectural efficiencies likely drive the MX570 A’s OpenCL victory.

Looking at the average benchmark scores, the Tesla M40 24 GB leads overall with 41,707 points, which is 7.8% higher than the MX570 A’s 38,691. This aligns with the Tesla’s 83rd percentile ranking versus the 81st for the MX570 A. However, the margin is modest, and the MX570 A’s nearest rival, the AMD Radeon Pro 580X, scores 38,706, essentially identical to the MX570 A (0% delta). The Tesla M40 24 GB is also tightly clustered with its rivals: it is just 1.3% ahead of the GeForce RTX 3080 Ti (41,187) and 2% ahead of the AMD Radeon Pro 5300 (40,870). These rival comparisons show that while the Tesla has a higher average, the MX570 A is competitive in its own right, especially in OpenCL.

Specification Differences

The two cards diverge sharply on nearly every specification. The Tesla M40 24 GB uses the GM200 chip on a 28 nm process at TSMC, with 8,000 million transistors on a 601 mm² die. The MX570 A uses the GA107SB chip on an 8 nm process at Samsung, with 8,700 million transistors on a 200 mm² die. This makes the MX570 A significantly denser: 43.5M transistors per mm² versus 13.3M for the Tesla. Clock speeds differ, with the Tesla running at 948 MHz base and 1112 MHz boost, while the MX570 A runs at 832 MHz base and 1155 MHz boost. Memory is a major differentiator: the Tesla offers 24 GB GDDR5 on a 384-bit bus with 288.4 GB/s bandwidth, while the MX570 A offers 2 GB GDDR6 on a 64-bit bus with 96.00 GB/s bandwidth.

Compute resources also vary widely. The Tesla M40 24 GB has 3,072 shading units, 192 TMUs, and 96 ROPs, producing 106.8 GPixel/s pixel rate and 213.5 GTexel/s texture rate. The MX570 A has 2,048 shading units, 64 TMUs, and 32 ROPs, yielding 36.96 GPixel/s and 73.92 GTexel/s. The MX570 A does include 16 ray-tracing cores and 64 tensor cores, which the Tesla lacks entirely. Floating-point performance favors the Tesla in fp32 at 6.832 TFLOPS versus 4.731 TFLOPS, but the MX570 A also offers fp16 at 4.731 TFLOPS (1:1), a capability the Tesla does not list. Power and physical specs are opposites: the Tesla draws 250 W and requires an 8-pin EPS connector, while the MX570 A draws 25 W and needs no connector. The Tesla is dual-slot with a 267 mm length, while the MX570 A is an IGP with no dimensions listed.

Architecture Differences

The architectural gap is generational. The Tesla M40 24 GB is built on Maxwell 2.0, a 28 nm design from TSMC with 8,000 million transistors. It offers DirectX 12 (12_1) support, but no ray tracing or tensor cores. The MX570 A is built on Ampere, a 2021-era 8 nm design from Samsung with 8,700 million transistors. It supports DirectX 12 Ultimate (12_2), includes 16 ray-tracing cores and 64 tensor cores, and provides fp16 compute at a 1:1 ratio. The process node difference is stark: 28 nm versus 8 nm, which explains the MX570 A’s vastly higher transistor density (43.5M / mm² versus 13.3M / mm²) despite having a smaller die (200 mm² versus 601 mm²). Memory technology also differs: the Tesla uses GDDR5 with 6 Gbps effective speed, while the MX570 A uses GDDR6 at 12 Gbps effective. The MX570 A also supports PCIe 4.0 x8, whereas the Tesla uses PCIe 3.0 x16. Display outputs differ as well: the Tesla has no outputs, while the MX570 A’s outputs are portable device dependent. Both cards support OpenGL 4.6 and Vulkan 1.4, but the Tesla’s DirectX 12_1 is a generation behind the MX570 A’s 12_2.

The Verdict

The data is clear: choose the NVIDIA Tesla M40 24 GB for Vulkan-heavy compute workloads. Its Vulkan score of 45,975 is 22.3% higher than the MX570 A’s, and its 24 GB memory and 288.4 GB/s bandwidth are unmatched by the MX570 A’s 2 GB and 96.00 GB/s. The Tesla also leads in overall average benchmark score (41,707 versus 38,691) and sits higher in the percentile rankings (83rd versus 81st). For any task that leverages Vulkan or requires large memory buffers, the Tesla M40 24 GB is the superior hardware, despite its 250 W TDP and lack of display outputs.

Choose the NVIDIA GeForce MX570 A for OpenCL-based applications or any power-sensitive deployment. Its OpenCL score of 39,780 beats the Tesla’s 37,439 by 5.9%, and it does so at just 25 W, one-tenth the power draw. The MX570 A’s modern Ampere architecture, with ray tracing and tensor cores, plus its smaller footprint as an IGP, makes it ideal for mobile or embedded systems. Its average benchmark score of 38,691 is only 7.8% lower than the Tesla’s, which is a small penalty for the dramatic efficiency gains. The MX570 A also matches its nearest rival, the AMD Radeon Pro 580X, exactly at 0% delta, showing it is a competitive performer in its class. Ultimately, the Tesla M40 24 GB is the compute powerhouse, while the MX570 A is the efficient specialist. Pick based on the API you use and your power budget.