NVIDIA RTX A1000 vs NVIDIA Tesla M40 24 GB Comparison

Head-to-Head Benchmarks

The recorded data shows a clear sweep for the NVIDIA RTX A1000 across the two shared benchmark tests, though the margin varies significantly by workload. In Geekbench OpenCL, the RTX A1000 scores 52,078 against the Tesla M40 24 GB's 37,439, a decisive 28.1% advantage. That is a substantial gap, placing the Ampere-based card firmly ahead in compute-heavy OpenCL tasks. The Vulkan result is closer: the RTX A1000 posts 49,574 versus 45,975 for the Tesla M40, a 7.3% lead. While still a win, the smaller delta suggests the Maxwell architecture holds up better in graphics-oriented API workloads than in raw compute throughput.

Looking at the broader context, the Tesla M40 24 GB's average benchmark score is 41,707, which places it in the 83rd percentile of all GPUs. Its nearest rival is the NVIDIA Tesla M40 (non-24 GB variant) at 41,897, just 0.5% higher, meaning the 24 GB model is essentially performance-equivalent to its predecessor in these aggregated metrics. The RTX A1000, by contrast, has an average score of 34,207, placing it in the 79th percentile. This is interesting: despite winning both head-to-head tests, the RTX A1000's overall average is dragged down by the inclusion of its 3DMark Steel Nomad DX12 result (969), a test the Tesla M40 does not have recorded. The A1000's nearest rival is the NVIDIA RTX A2000 12 GB at 34,154, a mere 0.2% difference, showing that the A1000 sits at the top of a tightly clustered group of mid-range workstation cards.

The data reveals a paradox: the RTX A1000 wins every direct comparison, yet its aggregate percentile is lower because the benchmark suite covers different workloads. The Tesla M40's 83rd percentile reflects its strength in the two tests it does run, while the A1000's 79th percentile includes a DX12 test where it scores relatively low (969). This is a critical nuance: the A1000 is not universally faster, it is faster in the specific tests where both cards are measured.

Architecture Differences

The two GPUs come from different generations and foundries, which explains much of their behavioral divergence. The Tesla M40 24 GB uses the GM200 chip built on TSMC's 28 nm process, packing 8,000 million transistors into a 601 mm² die. The transistor density is 13.3 million per mm². The RTX A1000 employs the GA107 chip on Samsung's 8 nm node, with 8,700 million transistors in a much smaller 200 mm² die, achieving a density of 43.5 million per mm². That is a 3.3x density advantage, allowing the A1000 to fit more transistors in a third of the silicon area.

The memory subsystems are starkly different. The Tesla M40 offers 24 GB of GDDR5 on a 384-bit bus, yielding 288.4 GB/s of bandwidth. The RTX A1000 provides only 8 GB of GDDR6 on a 128-bit bus, with 192.0 GB/s. The M40 has triple the capacity and 50% more bandwidth, though the A1000 uses a newer memory type with higher effective speed (12 Gbps versus 6 Gbps). For frame buffer-heavy tasks, the M40 is clearly superior, but for latency-sensitive or cache-friendly workloads, the A1000's architecture compensates.

Compute resources also differ. The Tesla M40 has 3,072 shading units, 192 texture mapping units, and 96 ROPs. The RTX A1000 has 2,304 shaders, 72 TMUs, and 32 ROPs. Despite fewer cores, the A1000's FP32 throughput is 6.737 TFLOPS, nearly identical to the M40's 6.832 TFLOPS. The A1000 also adds 18 RT cores and 72 tensor cores, features the Maxwell-based M40 lacks entirely. The A1000 supports FP16 at 6.737 TFLOPS (1:1 ratio), while the M40 has no recorded FP16 capability.

Clock behavior diverges significantly. The M40 runs at a 948 MHz base and 1112 MHz boost, while the A1000 has a lower 727 MHz base but a much higher 1462 MHz boost. The A1000's boost clock is 31% higher than the M40's, which helps close the core-count deficit. Pixel rate favors the M40 at 106.8 GPixel/s versus 46.78 GPixel/s for the A1000, and texture rate also favors the M40 (213.5 GTexel/s versus 105.3 GTexel/s). These numbers show the M40 is built for raw rasterization throughput, while the A1000 relies on architectural efficiency and specialized cores.

Power and physical design are opposites. The M40 consumes 250 W, requires a dual-slot cooler, an 8-pin EPS connector, and a 600 W suggested PSU. The A1000 sips 50 W, is single-slot, has no power connectors, and needs only a 250 W PSU. The M40 is 267 mm long, while the A1000 is 163 mm with a height of 69 mm. The M40 has no display outputs, making it a pure compute accelerator, whereas the A1000 offers 4x mini-DisplayPort 1.4a. The bus interface also differs: PCIe 3.0 x16 for the M40 versus PCIe 4.0 x8 for the A1000, though the latter's newer standard may offset the narrower link.

Where Each One Wins

The Tesla M40 24 GB wins decisively on memory capacity and bandwidth. Its 24 GB frame buffer is three times larger than the A1000's 8 GB, and its 288.4 GB/s bandwidth is 50% higher. For workloads that involve large datasets, high-resolution textures, or batch processing that exceeds 8 GB, the M40 is the only viable option between the two. The M40 also has higher pixel and texture rates, making it superior for traditional rasterization-heavy tasks, despite lacking modern API features. The M40's 96 ROPs versus 32 ROPs means it can fill more pixels per clock, which shows in its 106.8 GPixel/s versus 46.78 GPixel/s.

The RTX A1000 wins on computational efficiency and modern features. Its 28.1% lead in OpenCL and 7.3% lead in Vulkan demonstrate that its architecture extracts more usable performance per shader. The tensor cores and RT cores open up AI inference, ray tracing, and DLSS workloads that the M40 cannot handle at all. The A1000's FP16 support at 1:1 ratio is critical for mixed-precision machine learning, where the M40 has no capability. The 50 W power draw means the A1000 can run in systems with minimal cooling and power delivery, including small form factor machines or servers with dense GPU populations.

The A1000 also benefits from modern API support: DirectX 12 Ultimate (12_2) versus the M40's DirectX 12 (12_1), plus identical OpenGL 4.6 and Vulkan 1.4 support. The A1000's display outputs make it suitable for workstation use with monitors, while the M40 is headless. For longevity, the A1000 is Active in production status, while the M40 is End-of-life, released in November 2015 versus April 2024 for the A1000.

FAQ

Q: Which GPU has higher raw compute performance in FP32?

A: The Tesla M40 24 GB has 6.832 TFLOPS, while the RTX A1000 has 6.737 TFLOPS. The M40 is marginally ahead by 0.095 TFLOPS, less than 1.5% difference.

Q: Can the Tesla M40 handle ray tracing or AI workloads?

A: No. The M40 has zero RT cores and zero tensor cores. The RTX A1000 includes 18 RT cores and 72 tensor cores, enabling hardware-accelerated ray tracing and AI inference.

Q: Why does the RTX A1000 have a lower average benchmark score if it wins all head-to-head tests?

A: The A1000's average score of 34,207 includes a 3DMark Steel Nomad DX12 result of 969, a test the M40 does not have recorded. The M40's average of 41,707 is based only on its two strong OpenCL and Vulkan scores.

Q: Which card consumes less power?

A: The RTX A1000 uses 50 W, which is one-fifth of the Tesla M40's 250 W. The A1000 also has no power connectors and requires only a 250 W PSU, versus the M40's 8-pin EPS and 600 W PSU recommendation.

Q: Is the Tesla M40's 24 GB memory useful for modern workloads?

A: Yes, particularly for large batch processing or high-resolution datasets that exceed 8 GB. The M40 offers 24 GB GDDR5 with 288.4 GB/s bandwidth, while the A1000 has 8 GB GDDR6 at 192.0 GB/s.

Q: Which card has better display output capabilities?

A: The RTX A1000 has 4x mini-DisplayPort 1.4a. The Tesla M40 has no display outputs, making it unsuitable for direct monitor connection.

Specification Differences

| Specification | NVIDIA Tesla M40 24 GB | NVIDIA RTX A1000 |

|---|---|---|

| Architecture | Maxwell 2.0 | Ampere |

| Process Node | 28 nm (TSMC) | 8 nm (Samsung) |

| Transistors | 8,000 million | 8,700 million |

| Die Size | 601 mm² | 200 mm² |

| Transistor Density | 13.3M / mm² | 43.5M / mm² |

| Base Clock | 948 MHz | 727 MHz |

| Boost Clock | 1112 MHz | 1462 MHz |

| Memory Size | 24 GB GDDR5 | 8 GB GDDR6 |

| Memory Bus Width | 384 bit | 128 bit |

| Memory Bandwidth | 288.4 GB/s | 192.0 GB/s |

| Shading Units | 3072 | 2304 |

| TMUs | 192 | 72 |

| ROPs | 96 | 32 |

| RT Cores | None | 18 |

| Tensor Cores | None | 72 |

| Pixel Rate | 106.8 GPixel/s | 46.78 GPixel/s |

| Texture Rate | 213.5 GTexel/s | 105.3 GTexel/s |

| FP32 | 6.832 TFLOPS | 6.737 TFLOPS |

| FP16 | Not available | 6.737 TFLOPS (1:1) |

| TDP | 250 W | 50 W |

| Slot Width | Dual-slot | Single-slot |

| Power Connectors | 8-pin EPS | None |

| Suggested PSU | 600 W | 250 W |

| Bus Interface | PCIe 3.0 x16 | PCIe 4.0 x8 |

| Display Outputs | No outputs | 4x mini-DisplayPort 1.4a |

| DirectX | 12 (12_1) | 12 Ultimate (12_2) |

| Dimensions | 267 mm | 163 mm x 69 mm |

| Production Status | End-of-life | Active |

| Release Date | 2015-11-09 | 2024-04-15 |

The Verdict

The data points to a clear recommendation based on workload profile. The NVIDIA RTX A1000 is the superior choice for modern, power-sensitive, and feature-dependent environments. It wins both recorded benchmark tests, offers tensor and RT cores, supports FP16, has display outputs, runs on 50 W, and is currently Active with a 2024 release date. Its lower average benchmark score (34,207 versus 41,707) is a statistical artifact of including a DX12 test that the M40 does not run, not evidence of overall weakness.

The Tesla M40 24 GB is only preferable in scenarios that demand massive memory capacity or raw rasterization throughput. Its 24 GB frame buffer is unmatched by the A1000, and its 288.4 GB/s bandwidth, 96 ROPs, and 106.8 GPixel/s pixel rate make it a strong choice for large-texture rendering or bulk compute that fits within its older API constraints. However, its 250 W power draw, lack of modern features, and End-of-life status make it a poor choice for new deployments.

For most users, the RTX A1000 is the logical pick: it is faster in every shared test, dramatically more efficient, and supports the features required for current software. The M40 is a niche product for legacy workloads or memory-bound tasks where 8 GB is a hard limit. The benchmark data shows the A1000 leads by 28.1% in OpenCL and 7.3% in Vulkan, and those margins, combined with the architectural advantages, make the verdict straightforward.