NVIDIA GeForce RTX 4090 D vs NVIDIA Tesla V100 PCIe 16 GB Comparison

The NVIDIA GeForce RTX 4090 D and the NVIDIA Tesla V100 PCIe 16 GB represent two very different eras of NVIDIA’s GPU design, separated by over six years of architectural evolution. The data shows a clear performance hierarchy, but the specifics of where and how the newer card wins reveal a story about compute paradigms, memory technologies, and intended workloads. The RTX 4090 D, built on the Ada Lovelace architecture, dominates the shared benchmark suite, while the Tesla V100, a Volta-generation workhorse, trails significantly in raw scores but offers a distinct feature set that still matters in specialized environments.

Head-to-Head Benchmarks

The head-to-head results are decisive in favor of the RTX 4090 D, though the magnitude of the victory varies by test. In the Geekbench OpenCL benchmark, the RTX 4090 D scores 278621 against the Tesla V100’s 163063, a 70.9% advantage. This is a substantial lead, but it is not the largest gap between the two cards. The Geekbench Vulkan test shows an even more pronounced disparity: the RTX 4090 D posts 246941 points, while the Tesla V100 manages only 113062, resulting in a 118.4% delta—more than double the score of its older counterpart. This pattern suggests that the RTX 4090 D’s architectural strengths are particularly well-suited to modern, low-level graphics APIs like Vulkan, where its newer hardware can fully leverage features that the Volta architecture lacks.

Looking at the broader benchmark context, the RTX 4090 D’s average benchmark score is 178050, placing it in the 98th percentile of all GPUs. Its nearest rival, the NVIDIA RTX PRO 5000 Blackwell, scores 182109, which is only 2.2% higher, making the 4090 D essentially a peer of that professional card. In contrast, the Tesla V100’s average score of 138063 lands it in the 96th percentile, and its closest competitor, the NVIDIA Tesla V100 SXM2 32 GB, is a mere 0.2% ahead at 137731. This near-tie with its own SXM2 variant underscores that the PCIe version of the V100 is not a performance outlier within its own family.

The deltaPct values in the head-to-head are striking. A 70.9% lead in OpenCL is already large, but the 118.4% margin in Vulkan indicates that the RTX 4090 D’s advantages are not uniform across APIs. One interpretation is that the RTX 4090 D’s dedicated ray tracing cores (114 vs. none on the V100) and newer tensor cores (456 vs. 640 on the V100, but with different capabilities) allow it to accelerate workloads that the V100 must handle through more generic compute paths. The data does not provide per-test breakdowns beyond these two, but the available numbers paint a consistent picture of the newer card being in a different performance class.

The Verdict

From the data alone, the NVIDIA GeForce RTX 4090 D is the unequivocal performance winner in every benchmark where both cards were tested. It wins 2 of the 2 head-to-head tests, with winsA equal to 2 and winsB equal to 0. If the decision rests purely on measured scores, there is no scenario in which the Tesla V100 comes out ahead. The RTX 4090 D also carries a 98th percentile rank versus the V100’s 96th, reinforcing its position as a higher-tier product in the overall GPU landscape.

However, the verdict is not simply “buy the faster card.” The Tesla V100, with its 16 GB of HBM2 memory on a 4096-bit bus, offers a memory architecture that is fundamentally different from the RTX 4090 D’s 24 GB of GDDR6X on a 384-bit bus. While the RTX 4090 D has higher bandwidth (1.01 TB/s vs 897.0 GB/s), the V100’s HBM2 design was historically favored for certain high-performance computing tasks due to its capacity and access patterns. The data shows the V100’s FP16 performance at 28.26 TFLOPS (2:1) is double its FP32 rate of 14.13 TFLOPS, indicating a legacy focus on mixed-precision workloads. The RTX 4090 D, in contrast, offers FP16 at 73.54 TFLOPS (1:1), meaning its half-precision throughput matches its single-precision rate—a sign that it was designed for a different balance of precision needs.

For a buyer choosing between these two end-of-life products, the RTX 4090 D is the choice for anyone prioritizing measured compute or graphics performance. The Tesla V100 might still be relevant for specific legacy software stacks or environments that require its HBM2 memory and 640 tensor cores, but based on the benchmark results, it is not competitive on raw speed. The RTX 4090 D’s higher TDP (425 W vs 300 W) and larger physical footprint (Triple-slot vs Dual-slot) are trade-offs that the performance data justifies for most use cases.

Where Each One Wins

The RTX 4090 D wins in every measured category, but the nature of its victories suggests where it is best deployed. Its 73.54 TFLOPS of FP32 compute is over 5 times the V100’s 14.13 TFLOPS, making it vastly superior for single-precision floating-point tasks like real-time graphics, game rendering, and most AI inference workloads. The 118.4% lead in Vulkan is a strong indicator for gaming and interactive applications, where the V100’s lack of display outputs (it has none) would be a disqualifier anyway. The RTX 4090 D also has a higher pixel rate (443.5 GPixel/s vs 176.6 GPixel/s) and texture rate (1,149.1 GTexel/s vs 441.6 GTexel/s), which directly translate to better rasterization performance.

The Tesla V100’s only theoretical advantages lie in areas not directly captured by the benchmark scores. Its 640 tensor cores outnumber the RTX 4090 D’s 456 tensor cores, though the newer architecture of the latter likely makes each core more capable. The V100’s FP16 throughput of 28.26 TFLOPS is higher than its own FP32, suggesting it was optimized for certain deep learning training loops that use half precision. However, the RTX 4090 D’s FP16 at 73.54 TFLOPS is still 2.6 times higher in absolute terms. The V100’s 16 GB HBM2 memory, while smaller than the 4090 D’s 24 GB, offers a 4096-bit bus width that historically provided unique bandwidth characteristics; the data shows the 4090 D still wins on bandwidth, but the V100’s memory subsystem may have lower latency in specific access patterns that the benchmarks do not isolate.

Practically, the RTX 4090 D is the winner for any task that requires high compute throughput, modern API support, or display output. The Tesla V100, with its PCIe 3.0 x16 interface versus the 4090 D’s PCIe 4.0 x16, is a legacy server part best suited for environments that are already built around its Volta architecture and where software is not optimized for Ada Lovelace features. The V100’s lower TDP of 300 W makes it easier to cool and power in dense server chassis, but that is a physical advantage, not a performance one.

FAQ

Q: How much faster is the RTX 4090 D than the Tesla V100 in OpenCL?

A: The RTX 4090 D scores 278621 in Geekbench OpenCL, which is 70.9% higher than the Tesla V100’s 163063.

Q: Does the Tesla V100 win in any benchmark?

A: No. In the two head-to-head tests (Geekbench OpenCL and Geekbench Vulkan), the RTX 4090 D wins both, with winsA equal to 2 and winsB equal to 0.

Q: What is the memory configuration difference?

A: The RTX 4090 D uses 24 GB of GDDR6X on a 384-bit bus, while the Tesla V100 uses 16 GB of HBM2 on a 4096-bit bus. The RTX 4090 D has higher bandwidth at 1.01 TB/s versus 897.0 GB/s.

Q: Which card has more tensor cores?

A: The Tesla V100 has 640 tensor cores, which is more than the RTX 4090 D’s 456 tensor cores. However, the RTX 4090 D’s FP16 performance is 73.54 TFLOPS compared to the V100’s 28.26 TFLOPS.

Q: Are there differences in display output capability?

A: Yes. The RTX 4090 D has display outputs including 1x HDMI 2.1 and 3x DisplayPort 1.4a, while the Tesla V100 has no display outputs.

Q: How do their process nodes compare?

A: The RTX 4090 D is built on a 5 nm process, while the Tesla V100 uses a 12 nm process. Both are manufactured by TSMC.

Architecture Differences

The two GPUs are fundamentally different in their architectural DNA. The RTX 4090 D is based on the AD102 chip using the Ada Lovelace architecture, fabricated on a 5 nm process at TSMC. It packs 76,300 million transistors into a 609 mm² die, yielding a transistor density of 125.3M / mm². The Tesla V100, in contrast, uses the GV100 chip with the Volta architecture, built on a 12 nm process. It contains 21,100 million transistors on a much larger 815 mm² die, resulting in a significantly lower transistor density of 25.9M / mm². This density difference is a direct consequence of the manufacturing node gap, allowing the newer card to fit nearly 3.6 times more transistors in a smaller area.

The compute resources diverge sharply. The RTX 4090 D features 14,592 shading units, 456 TMUs, and 176 ROPs, alongside 114 ray tracing cores and 456 tensor cores. The Tesla V100 has only 5,120 shading units, 320 TMUs, and 128 ROPs, with 640 tensor cores but no dedicated ray tracing hardware. The absence of RT cores on the V100 is a major architectural difference, as it means the card cannot accelerate ray-traced workloads in hardware. The RTX 4090 D’s FP32 and FP16 performance are both 73.54 TFLOPS, indicating a 1:1 ratio, whereas the V100’s FP16 of 28.26 TFLOPS is exactly double its FP32 of 14.13 TFLOPS, reflecting Volta’s 2:1 design for mixed-precision compute.

The memory subsystems are also architecturally distinct. The RTX 4090 D uses GDDR6X memory with a 384-bit bus and a 1313 MHz memory clock (effective 21 Gbps), while the Tesla V100 uses HBM2 with a 4096-bit bus and an 876 MHz clock (1752 Mbps effective). While the V100’s bus is over 10 times wider, the newer, faster GDDR6X on the 4090 D still achieves higher bandwidth. Additionally, the RTX 4090 D supports DirectX 12 Ultimate (12_2) and Vulkan 1.4, matching the V100’s Vulkan support but exceeding its DirectX 12 (12_1) capability. The RTX 4090 D also uses a PCIe 4.0 x16 interface, double the bandwidth of the V100’s PCIe 3.0 x16.

Specification Differences

The specification sheets highlight several key areas where the two cards differ. The most obvious is process node: 5 nm for the RTX 4090 D versus 12 nm for the Tesla V100. This leads to a massive transistor count difference (76,300 million vs 21,100 million) and die size difference (609 mm² vs 815 mm²). Clock speeds are also divergent, with the RTX 4090 D boosting to 2520 MHz from a base of 2280 MHz, while the V100 boosts to only 1380 MHz from 1245 MHz.

Memory specifications differ in every field: size (24 GB vs 16 GB), type (GDDR6X vs HBM2), bus width (384 bit vs 4096 bit), and bandwidth (1.01 TB/s vs 897.0 GB/s). The shading unit count is nearly three times higher on the RTX 4090 D (14,592 vs 5,120), and its TMU and ROP counts are also higher (456 and 176 vs 320 and 128). Ray tracing cores are present only on the RTX 4090 D (114), while tensor core counts favor the V100 (640 vs 456). Pixel rate (443.5 GPixel/s vs 176.6 GPixel/s) and texture rate (1,149.1 GTexel/s vs 441.6 GTexel/s) are both substantially higher on the newer card.

Power and physical attributes also differ. The RTX 4090 D has a TDP of 425 W with a single 16-pin power connector and a suggested PSU of 800 W, while the V100 draws 300 W via two 8-pin connectors and needs a 700 W PSU. The RTX 4090 D is a Triple-slot card measuring 304 mm in length, whereas the V100 is Dual-slot with no listed dimensions. Display outputs are present on the RTX 4090 D (1x HDMI 2.1 and 3x DisplayPort 1.4a) but absent on the V100. The RTX 4090 D supports DirectX 12 Ultimate (12_2), while the V100 is limited to DirectX 12 (12_1); both support OpenGL 4.6 and Vulkan 1.4. Finally, the RTX 4090 D has a launch MSRP of 1,599 USD, while the V100 has no listed launch MSRP.