NVIDIA Quadro M6000 24 GB vs NVIDIA Tesla M40 24 GB Comparison

The Verdict

The data tells a clear story for these two Maxwell-2.0-era 24 GB cards: the Quadro M6000 24 GB is the pick for anyone who needs the best raw compute scores this platform can offer, while the Tesla M40 24 GB is the pick for compute-only deployments where the display outputs and the slight clock advantage of the Quadro are irrelevant. In the two head-to-head benchmarks, the Quadro wins both: 7.1% ahead in OpenCL and 1% ahead in Vulkan. But the M40 is no slouch—it sits in the same 83rd percentile of all GPUs and trails by only 1.3% against a GeForce RTX 3080 Ti in average benchmark score, making it a strong choice for pure number-crunching in a server chassis. If your workload leans on OpenCL, the Quadro’s lead is decisive; if you need Vulkan performance, the gap narrows to a single percentage point, and the M40’s lack of display outputs becomes a non-issue in a headless rig. The Quadro also carries a launch MSRP of 4,999 USD, which you may note once, but the M40 has no listed launch price.

Where Each One Wins

The Quadro M6000 24 GB wins both benchmark categories outright, so it is the default choice for maximizing compute throughput in either API. Its OpenCL score of 40098 versus the M40’s 37439 is a 7.1% advantage—the largest single margin in this comparison, meaning OpenCL-heavy applications like scientific simulations or data-parallel workloads will see a meaningful gain. In Vulkan, the Quadro’s 46425 beats the M40’s 45975 by 1%, a smaller but still consistent edge that could matter in Vulkan-based rendering or compute pipelines.

The Tesla M40 24 GB, despite losing both head-to-head tests, wins in the context of deployment simplicity. It has no display outputs, which means it is designed for compute-only environments where you do not need to drive a monitor. In that scenario, the M40’s slightly lower average benchmark score of 41707 (versus the Quadro’s 43262) is a fair trade-off for a card that consumes the same 250 W TDP and uses the same 8,000-million-transistor GM200 chip. The M40 also has a lower base clock (948 MHz vs 988 MHz) and lower memory clock (1502 MHz vs 1653 MHz), so it runs cooler and quieter in theory, though the fact pack does not provide thermal data. For a rack full of compute cards, the M40’s lack of video outputs is a feature, not a bug—it frees up resources and eliminates any chance of accidental display attachment.

Architecture Differences

Both cards are built on the same fundamental architecture: Maxwell 2.0, fabricated on a 28 nm process at TSMC, with 8,000 million transistors on a 601 mm² die and a transistor density of 13.3M / mm². They share the same GM200 chip, the same 3072 shading units, 192 texture mapping units, and 96 raster operation pipelines. Neither card has ray tracing cores or tensor cores—those are absent from the Maxwell 2.0 design. Both support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. The memory subsystem is identical in size and type: 24 GB of GDDR5 on a 384-bit bus. The core architectural differences come down to clocks and power delivery. The Quadro runs a base clock of 988 MHz and a boost of 1114 MHz, while the M40 runs a base of 948 MHz and a boost of 1112 MHz—a 40 MHz base and 2 MHz boost difference. Memory is where the gap widens: the Quadro’s memory runs at 1653 MHz (6.6 Gbps effective) versus the M40’s 1502 MHz (6 Gbps effective). This produces a bandwidth difference of 317.4 GB/s for the Quadro versus 288.4 GB/s for the M40. The power connector also differs: the Quadro uses a standard 1x 8-pin, while the M40 uses an 8-pin EPS connector, which is a server-oriented power plug. Both cards are dual-slot, 267 mm long (10.5 inches), and have a recommended PSU of 600 W. The Quadro is 111 mm tall (4.4 inches); the M40’s height is not listed.

FAQ

Q: Which card has better OpenCL performance?

A: The Quadro M6000 24 GB wins OpenCL with a score of 40098 versus the Tesla M40 24 GB’s 37439, a 7.1% advantage.

Q: Is the Tesla M40 24 GB suitable for a workstation with a monitor?

A: No. The Tesla M40 24 GB has no display outputs, so it cannot drive a monitor. The Quadro M6000 24 GB, by contrast, offers 1x DVI and 4x DisplayPort 1.2 outputs.

Q: Do both cards support the same graphics APIs?

A: Yes. Both support DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4. Neither has ray tracing or tensor cores.

Q: What is the memory bandwidth difference?

A: The Quadro M6000 24 GB has a bandwidth of 317.4 GB/s, while the Tesla M40 24 GB has 288.4 GB/s. The difference comes from the Quadro’s higher memory clock of 1653 MHz versus the M40’s 1502 MHz.

Q: Which card is better for a headless compute server?

A: The Tesla M40 24 GB is designed for that use case—it lacks display outputs entirely, which is ideal for a compute-only node. Its performance is slightly lower (41707 average benchmark score vs 43262), but the lack of video outputs is a deliberate design choice for servers.

Q: Are there any power consumption differences?

A: No. Both cards have a TDP of 250 W and a recommended PSU of 600 W. They differ in power connector type: the Quadro uses 1x 8-pin, the M40 uses 8-pin EPS.

Head-to-Head Benchmarks

The most telling benchmark is OpenCL, where the Quadro M6000 24 GB scores 40098 against the Tesla M40 24 GB’s 37439. That 7.1% delta is the largest margin in this comparison, and it aligns with the Quadro’s higher base clock (988 MHz vs 948 MHz) and higher memory bandwidth (317.4 GB/s vs 288.4 GB/s). In raw compute terms, the Quadro delivers 6.844 TFLOPS of FP32 versus the M40’s 6.832 TFLOPS—a small but measurable edge. For OpenCL workloads that are memory-bandwidth-sensitive, the Quadro’s 29 GB/s advantage likely explains the bigger benchmark jump.

The Vulkan benchmark is much closer: the Quadro scores 46425, and the M40 scores 45975, a 1% delta. This suggests that Vulkan’s compute paths are less dependent on the memory clock difference, or that the M40’s slightly lower boost clock (1112 MHz vs 1114 MHz) is nearly negligible in this API. Still, the Quadro wins, and the pattern is consistent—two benchmarks, two Quadro victories. The M40’s best showing is its average benchmark score of 41707, which is 3.7% behind the Quadro’s 43262, but within striking distance of a GeForce RTX 4090 Mobile (43667, 0.9% ahead of the Quadro). The M40 also trails a GeForce RTX 3080 Ti by only 1.3% in average score, which places it in the same performance tier as modern consumer cards despite its 2015 release date.

Specification Differences

The two cards differ in several measurable fields, all stemming from their different positioning. The Quadro M6000 24 GB has a base clock of 988 MHz and a boost of 1114 MHz, while the Tesla M40 24 GB runs at 948 MHz base and 1112 MHz boost. The memory clock is 1653 MHz (6.6 Gbps effective) on the Quadro versus 1502 MHz (6 Gbps effective) on the M40. This yields a bandwidth of 317.4 GB/s for the Quadro versus 288.4 GB/s for the M40. The pixel rate is nearly identical—106.9 GPixel/s for the Quadro versus 106.8 GPixel/s for the M40—and the texture rate is similarly close at 213.9 GTexel/s versus 213.5 GTexel/s. FP32 compute is 6.844 TFLOPS on the Quadro versus 6.832 TFLOPS on the M40. The power connector differs: the Quadro uses a standard 1x 8-pin, while the M40 uses an 8-pin EPS connector. Display outputs are the biggest functional split—the Quadro has 1x DVI and 4x DisplayPort 1.2, while the M40 has no outputs. The Quadro has listed dimensions of 267 mm length and 111 mm height, while the M40 only lists 267 mm length. The release dates differ: the Quadro launched on 2016-03-04, and the M40 on 2015-11-09. The Quadro has a launch MSRP of 4,999 USD; the M40 has no listed launch MSRP. The generation names differ—Quadro Maxwell (Mx000) versus Tesla Maxwell (Mxx)—and their predecessors and successors differ accordingly: the Quadro follows Quadro Kepler and precedes Quadro Pascal, while the M40 follows Tesla Kepler and precedes Tesla Pascal. Everything else—process node, foundry, transistor count, die size, shading units, TMUs, ROPs, memory size, bus width, memory type, TDP, slot width, PSU recommendation, bus interface, and API support—is identical between the two.