NVIDIA T1000 8 GB vs NVIDIA Tesla M40 Comparison

Head-to-Head Benchmarks

The recorded data shows a single head-to-head comparison between the NVIDIA Tesla M40 and the NVIDIA T1000 8 GB, and the outcome is decisive. In the Geekbench Vulkan test, the Tesla M40 scores 44,602 points, while the T1000 8 GB scores 34,561 points. This results in a 29.1% advantage for the Tesla M40, a substantial margin that places the two cards in different performance tiers.

The Tesla M40's average benchmark score across all recorded tests is 41,897, which is a strong showing given its nearest rivals. The closest competitor to the M40 is the NVIDIA Tesla M40 24 GB, which averages 41,707, a negligible 0.5% difference. This indicates that the 12 GB M40 performs nearly identically to its 24 GB sibling in these workloads. The AMD Radeon RX 7650 GRE sits slightly ahead at 42,723, a 1.9% margin, while the NVIDIA GeForce RTX 3080 Ti trails at 41,187, a 1.7% deficit. The AMD Radeon Pro 5300 is further behind at 40,870, a 2.5% gap.

For the T1000 8 GB, the average benchmark score is 34,561, placing it in a different competitive bracket. Its nearest rival, the AMD Radeon HD 7970, scores 34,541, a near dead heat with only a 0.1% difference. The NVIDIA A2 is slightly ahead at 34,690, a 0.4% margin, while the NVIDIA TITAN V scores 34,355, a 0.6% deficit. The NVIDIA RTX A1000 trails at 34,207, a 1% gap. These figures show the T1000 8 GB clustered tightly with older or lower-end hardware, whereas the Tesla M40 competes with much faster modern cards.

The percentile rankings reinforce this split. The Tesla M40 sits in the 83rd percentile of all GPUs, while the T1000 8 GB occupies the 79th percentile. A four-percentile gap is meaningful when translated into real-world performance, especially given the 29.1% raw score difference in the Vulkan test.

Architecture Differences

The architectural gap between the two cards is generational and profound. The Tesla M40 uses the GM200 chip built on the Maxwell 2.0 architecture, fabricated on a 28 nm process at TSMC. The T1000 8 GB uses the TU117 chip on the Turing architecture, also produced by TSMC but on a 12 nm process. This process node difference explains much of the efficiency disparity between the two.

The Tesla M40 packs 8,000 million transistors onto a 601 mm² die, yielding a transistor density of 13.3 million per square millimeter. The T1000 8 GB integrates 4,700 million transistors on a much smaller 200 mm² die, achieving a higher density of 23.5 million per square millimeter. The M40 is a large, power-hungry compute board, while the T1000 8 GB is a compact, efficient workstation card.

Memory configurations differ sharply. The Tesla M40 ships with 12 GB of GDDR5 memory on a 384-bit bus, delivering 288.4 GB/s of bandwidth. The T1000 8 GB has 8 GB of GDDR6 memory on a 128-bit bus, providing 160.0 GB/s. The M40's wider bus and larger frame buffer give it a massive bandwidth advantage, nearly double that of the T1000 8 GB. The memory clock rates also differ, with the M40 running at 1502 MHz (6 Gbps effective) and the T1000 8 GB at 1250 MHz (10 Gbps effective). The newer GDDR6 standard in the T1000 8 GB cannot compensate for the narrower bus.

Compute resources are heavily skewed toward the M40. The Tesla M40 has 3,072 shading units, 192 texture mapping units, and 96 raster output units. The T1000 8 GB has 896 shading units, 56 TMUs, and 32 ROPs. This is a 3.4x difference in shader count, a 3.4x difference in TMUs, and a 3x difference in ROPs. The pixel rate for the M40 is 106.8 GPixel/s versus 44.64 GPixel/s for the T1000 8 GB. Texture rate is 213.5 GTexel/s versus 78.12 GTexel/s. FP32 compute is 6.832 TFLOPS for the M40 versus 2.500 TFLOPS for the T1000 8 GB. The M40 is more than 2.7x faster in raw FP32 throughput.

Clock speeds tell a different story. The T1000 8 GB has a base clock of 1065 MHz and a boost clock of 1395 MHz, higher than the M40's 948 MHz base and 1112 MHz boost. The T1000 8 GB also has FP16 capability at 5.000 TFLOPS with a 2:1 ratio, whereas the M40 has no recorded FP16 performance. However, these advantages do not overcome the massive core count deficit.

Power and physical design diverge completely. The Tesla M40 has a 250 W TDP, requires an 8-pin EPS power connector, and a suggested 600 W power supply. It is a dual-slot card measuring 267 mm (10.5 inches) with no display outputs. The T1000 8 GB has a 50 W TDP, needs no power connectors, and requires only a 250 W power supply. It is a single-slot card measuring 156 mm (6.1 inches) with a height of 69 mm (2.7 inches) and features 4x mini-DisplayPort 1.4a outputs. Both use PCIe 3.0 x16 and support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4.

Where Each One Wins

The Tesla M40 wins decisively in raw compute performance. Its 29.1% Vulkan score advantage, 6.832 TFLOPS of FP32, and 288.4 GB/s of memory bandwidth make it the clear choice for compute-heavy workloads such as deep learning inference, scientific simulation, or any task that saturates shader throughput. The 12 GB frame buffer also allows larger datasets to reside in GPU memory, avoiding PCIe transfers.

The T1000 8 GB wins on efficiency, physical footprint, and display capability. Its 50 W TDP is one-fifth of the M40's 250 W, and it requires no external power connectors. It is a single-slot, 156 mm card that fits in compact workstations or small form factor systems. It has 4x mini-DisplayPort 1.4a outputs, making it suitable for multi-monitor professional desktop use, something the M40 cannot do at all since it has no display outputs.

The T1000 8 GB also holds a clock speed advantage, with a 1395 MHz boost versus 1112 MHz for the M40, and it supports FP16 at 5.000 TFLOPS. For workloads that leverage FP16, such as certain AI inference frameworks, the T1000 8 GB may narrow the gap despite its lower FP32 throughput. The M40 has no FP16 support, so any FP16 task is either run in emulation or not supported at all.

In the benchmark data, the M40's nearest rivals are all high-end cards (RTX 3080 Ti, RX 7650 GRE), while the T1000 8 GB's rivals are older or lower-end parts (HD 7970, A2, TITAN V). This positioning confirms that the M40 belongs in a performance class above the T1000 8 GB, despite the latter being a newer architecture.

Specification Differences

The two cards differ on nearly every specification field. The process node is 28 nm for the M40 versus 12 nm for the T1000 8 GB. Transistor count is 8,000 million versus 4,700 million. Die size is 601 mm² versus 200 mm². Transistor density is 13.3M per mm² versus 23.5M per mm².

Base clock is 948 MHz versus 1065 MHz. Boost clock is 1112 MHz versus 1395 MHz. Memory clock is 1502 MHz (6 Gbps effective) versus 1250 MHz (10 Gbps effective). Memory size is 12 GB versus 8 GB. Memory type is GDDR5 versus GDDR6. Bus width is 384 bit versus 128 bit. Bandwidth is 288.4 GB/s versus 160.0 GB/s.

Shading units are 3072 versus 896. TMUs are 192 versus 56. ROPs are 96 versus 32. Pixel rate is 106.8 GPixel/s versus 44.64 GPixel/s. Texture rate is 213.5 GTexel/s versus 78.12 GTexel/s. FP32 is 6.832 TFLOPS versus 2.500 TFLOPS. FP16 is null versus 5.000 TFLOPS (2:1).

TDP is 250 W versus 50 W. Slot width is dual-slot versus single-slot. Power connectors are 8-pin EPS versus none. Suggested PSU is 600 W versus 250 W. Display outputs are none versus 4x mini-DisplayPort 1.4a. Length is 267 mm (10.5 inches) versus 156 mm (6.1 inches). The T1000 8 GB has a recorded height of 69 mm (2.7 inches); the M40 has no height recorded.

Release dates are 2015-11-09 for the M40 and 2021-05-05 for the T1000 8 GB. The M40's predecessor is Tesla Kepler and successor is Tesla Pascal. The T1000 8 GB's predecessor is Quadro Volta and successor is Workstation Ampere. The M40 is from the Tesla Maxwell (Mxx) generation, while the T1000 8 GB is from the Quadro Turing (Tx000) generation.

FAQ

Q: Which card is faster in the Geekbench Vulkan benchmark?

A: The NVIDIA Tesla M40 scores 44,602 points compared to the NVIDIA T1000 8 GB's 34,561 points, giving the M40 a 29.1% advantage.

Q: How does the memory bandwidth compare between the two?

A: The Tesla M40 has 288.4 GB/s of bandwidth from a 384-bit GDDR5 bus, while the T1000 8 GB has 160.0 GB/s from a 128-bit GDDR6 bus.

Q: Does the T1000 8 GB support any features the M40 lacks?

A: Yes, the T1000 8 GB supports FP16 compute at 5.000 TFLOPS with a 2:1 ratio, while the M40 has no FP16 capability. The T1000 8 GB also has 4x mini-DisplayPort 1.4a outputs, whereas the M40 has no display outputs.

Q: What are the power requirements for each card?

A: The Tesla M40 has a 250 W TDP and requires an 8-pin EPS connector and a 600 W power supply. The T1000 8 GB has a 50 W TDP, requires no power connectors, and needs only a 250 W power supply.

Q: How does the Tesla M40 compare to its closest rivals?

A: The M40 averages 41,897 points, which is 0.5% ahead of the Tesla M40 24 GB, 1.7% ahead of the GeForce RTX 3080 Ti, 2.5% ahead of the Radeon Pro 5300, and 1.9% behind the Radeon RX 7650 GRE.

Q: How does the T1000 8 GB compare to its closest rivals?

A: The T1000 8 GB averages 34,561 points, which is 0.1% ahead of the Radeon HD 7970, 0.6% ahead of the TITAN V, 1% ahead of the RTX A1000, and 0.4% behind the NVIDIA A2.

The Verdict

The data points to a clear split: the NVIDIA Tesla M40 is for raw performance, and the NVIDIA T1000 8 GB is for efficiency and desktop integration. Any workload that prioritizes compute throughput will favor the M40. Its 29.1% Vulkan lead, 6.832 TFLOPS of FP32, and 288.4 GB/s of memory bandwidth put it in a league above the T1000 8 GB, whose 2.500 TFLOPS and 160.0 GB/s cannot compete in heavy number-crunching.

Choose the Tesla M40 if the task is compute-bound, such as rendering, simulation, or GPU-accelerated analytics, and the system can accommodate a dual-slot, 267 mm card with a 250 W TDP and 600 W power supply. The 12 GB frame buffer is a further advantage for large models or datasets.

Choose the T1000 8 GB if the need is a low-power workstation card with display outputs. Its 50 W TDP, single-slot design, and 4x mini-DisplayPort outputs make it ideal for multi-monitor professional desktops, CAD workstations, or any environment where power and space are constrained. The 1395 MHz boost clock and FP16 support offer some modern features, but the raw compute gap is too large to overcome.

The recorded benchmark data is unambiguous. The M40 wins the only head-to-head test by a wide margin, and its percentile ranking of 83 versus 79 confirms its higher standing. Neither card is currently in production, as both are end-of-life, but the architecture and specification differences define two distinct usage profiles. For performance, the M40 is the answer. For efficiency and connectivity, the T1000 8 GB is the answer.