NVIDIA GeForce RTX 4090 D vs NVIDIA Tesla V100 SXM2 16 GB Comparison

The NVIDIA GeForce RTX 4090 D and the NVIDIA Tesla V100 SXM2 16 GB represent two distinct eras of NVIDIA’s computing lineup, separated by over six years of architectural evolution. The data shows a decisive performance gap, with the RTX 4090 D winning both shared benchmark tests by significant margins. However, the V100 retains relevance in its specific data-center niche, particularly through its dedicated Tensor Core count and HBM2 memory configuration. This analysis breaks down the head-to-head results, answers common questions, and outlines the core architectural and specification differences between these two end-of-life products.

Head-to-Head Benchmarks

The benchmark data available for direct comparison is limited to two compute-oriented tests: Geekbench OpenCL and Geekbench Vulkan. In both instances, the GeForce RTX 4090 D emerges as the clear victor, demonstrating the generational leap in raw compute throughput.

In the Geekbench OpenCL test, the RTX 4090 D scores 278,621, while the Tesla V100 SXM2 manages only 87,454. This represents a deltaPct of 218.6% in favor of the RTX 4090 D — meaning the newer card outperforms the older one by more than threefold. This is not a marginal improvement; it is a complete overhaul of compute capability. The gap is so large that it overshadows any other consideration in raw FP32-style workloads. The V100’s score places it in the 95th percentile of all GPUs, but the RTX 4090 D sits in the 98th percentile, underscoring how far the performance ceiling has moved.

The Geekbench Vulkan result tells a similar story, though with a slightly smaller margin. The RTX 4090 D scores 246,941 against the V100’s 141,336, yielding a deltaPct of 74.7%. While still a commanding victory, the relative closeness of this result compared to OpenCL suggests that the V100’s architecture handles Vulkan’s API overhead better than OpenCL, or that the RTX 4090 D’s driver optimizations are less pronounced in this specific test. Still, the RTX 4090 D is 74.7% faster, which is a substantial lead in any context.

Looking at the broader picture, the RTX 4090 D’s average benchmark score of 178,050 dwarfs the V100’s 114,395. The RTX 4090 D’s nearest rivals in its performance bracket are all modern enterprise-class cards: the NVIDIA RTX PRO 5000 Blackwell (average score 182,109, with the RTX 4090 D trailing by just 2.2%), the NVIDIA A100 SXM4 80 GB (183,725, trailing by 3.1%), and the NVIDIA RTX 5000 Ada Generation (184,664, trailing by 3.6%). This places the RTX 4090 D in the upper echelon of all GPUs, just a few percentage points shy of the absolute top performers.

Conversely, the V100’s nearest rivals are a mixed bag of older and lower-tier modern parts. It sits just 0.4% ahead of the NVIDIA RTX A5500 Mobile (average score 113,944), 2.3% behind the NVIDIA RTX 4000 SFF Ada Generation (117,088), and 2.6% behind the NVIDIA GB10 (117,393). Notably, the AMD Radeon PRO W7900 scores 110,725, placing it 3.3% behind the V100. These comparisons highlight that the V100, while still a competent performer, is now competing with mobile and small-form-factor parts rather than flagship data-center accelerators.

The head-to-head record is unambiguous: the RTX 4090 D wins 2 benchmarks, and the V100 wins 0. There is no single test where the older architecture manages to claw back a victory.

FAQ

Q: Which GPU has the higher raw compute performance in shared benchmarks?

A: The NVIDIA GeForce RTX 4090 D wins decisively. In Geekbench OpenCL, it scores 278,621 versus the Tesla V100 SXM2’s 87,454, a deltaPct of 218.6%. In Geekbench Vulkan, the RTX 4090 D scores 246,941 against 141,336, a deltaPct of 74.7%.

Q: How do these cards rank among all GPUs?

A: The RTX 4090 D sits in the 98th percentile of all GPUs, while the V100 SXM2 sits in the 95th percentile. The RTX 4090 D’s average benchmark score of 178,050 is substantially higher than the V100’s 114,395.

Q: Is the Tesla V100 SXM2 still competitive with modern workstation cards?

A: The data shows it is competitive with lower-tier modern parts. The V100’s average score of 114,395 places it 0.4% ahead of the RTX A5500 Mobile (113,944) and 3.3% ahead of the AMD Radeon PRO W7900 (110,725). However, it trails the RTX 4000 SFF Ada Generation by 2.3% and the GB10 by 2.6%.

Q: What is the memory configuration difference between the two?

A: The RTX 4090 D uses 24 GB of GDDR6X on a 384-bit bus, delivering 1.01 TB/s of bandwidth. The V100 SXM2 uses 16 GB of HBM2 on a much wider 4096-bit bus, delivering 897.0 GB/s of bandwidth.

Q: Which card has more Tensor Cores?

A: The Tesla V100 SXM2 has 640 Tensor Cores, while the RTX 4090 D has 456 Tensor Cores. Despite the lower count, the RTX 4090 D’s newer architecture and higher clocks lead to superior overall performance in the shared benchmarks.

Q: What is the power consumption difference?

A: The RTX 4090 D has a TDP of 425 W and requires a suggested 800 W PSU, while the V100 SXM2 has a TDP of 250 W and a suggested 600 W PSU. The V100 is an SXM module with no power connectors, whereas the RTX 4090 D is a triple-slot card using a 1x 16-pin connector.

The Verdict

The choice between these two GPUs is starkly defined by the benchmark data. For any application that relies on raw compute throughput in OpenCL or Vulkan workloads, the NVIDIA GeForce RTX 4090 D is the unequivocal pick. It delivers a 218.6% advantage in OpenCL and a 74.7% advantage in Vulkan over the V100 SXM2. Its average benchmark score of 178,050 is 55.7% higher than the V100’s 114,395. The RTX 4090 D also offers more memory (24 GB vs 16 GB) and significantly higher memory bandwidth (1.01 TB/s vs 897.0 GB/s), making it better suited for large datasets.

The NVIDIA Tesla V100 SXM2 should only be considered in very specific legacy contexts. Its 640 Tensor Cores outnumber the RTX 4090 D’s 456, which may be relevant for software that is heavily optimized for Volta’s specific Tensor Core instruction set. Furthermore, its SXM module form factor and 250 W TDP mean it is designed for dense server deployments where power efficiency and physical footprint are critical. The V100 still ranks in the 95th percentile of all GPUs, indicating it is far from obsolete, but it is now bracketed by mobile and small-form-factor parts. For users with existing V100 infrastructure who cannot migrate software stacks, the V100 remains a functional choice. For anyone building a new system, the RTX 4090 D’s performance data makes it the only rational selection.

Specification Differences

The specification sheets reveal a generational chasm between these two NVIDIA products. The RTX 4090 D is built on a 5 nm process (TSMC), while the V100 SXM2 uses a 12 nm process (TSMC). This explains the massive difference in transistor density: the RTX 4090 D packs 76,300 million transistors into a 609 mm² die, yielding a density of 125.3M / mm². The V100 has 21,100 million transistors on a larger 815 mm² die, resulting in just 25.9M / mm².

Clock speeds are also vastly different. The RTX 4090 D has a base clock of 2280 MHz and a boost clock of 2520 MHz, whereas the V100 SXM2 operates at a base of 1312 MHz and boosts to 1530 MHz. This clock advantage, combined with the architectural improvements, drives the RTX 4090 D’s FP32 performance to 73.54 TFLOPS versus the V100’s 15.67 TFLOPS. The FP16 numbers are also telling: the RTX 4090 D achieves 73.54 TFLOPS (1:1 ratio), while the V100 reaches 31.33 TFLOPS (2:1 ratio).

Memory subsystems differ fundamentally. The RTX 4090 D uses 24 GB of GDDR6X on a 384-bit bus with a 1.01 TB/s bandwidth. The V100 uses 16 GB of HBM2 on a 4096-bit bus with 897.0 GB/s bandwidth. While the V100’s bus is wider, the RTX 4090 D’s higher effective memory clock (21 Gbps vs 1752 Mbps) gives it the bandwidth edge.

Physical and power characteristics are also distinct. The RTX 4090 D is a triple-slot card measuring 304 mm in length, 137 mm in height, and 61 mm in width, with a 425 W TDP and a 1x 16-pin power connector. The V100 SXM2 is an SXM module with no specified dimensions, a 250 W TDP, and no power connectors, relying on the host system for power delivery.

Architecture Differences

The architectural divide between Ada Lovelace and Volta is the root cause of the performance gap. The RTX 4090 D is built on the Ada Lovelace architecture with the AD102 chip, while the V100 SXM2 uses the Volta architecture with the GV100 chip. This is a difference of two full generations, with Ada Lovelace introducing significant improvements in ray tracing, shader execution, and AI processing.

The RTX 4090 D features 14,592 shading units, 456 TMUs, and 176 ROPs, alongside 114 RT cores and 456 Tensor Cores. The V100 has 5,120 shading units, 320 TMUs, and 128 ROPs, with 640 Tensor Cores but no RT cores. This means the V100 has no hardware-accelerated ray tracing capabilities, while the RTX 4090 D is fully equipped for DirectX 12 Ultimate (12_2) workloads. The RTX 4090 D also supports Vulkan 1.4 and OpenGL 4.6, which the V100 also supports, but the V100 is limited to DirectX 12 (12_1), lacking the Ultimate feature set.

The Tensor Core implementations are fundamentally different. Volta’s 640 Tensor Cores were designed for early AI workloads, while Ada Lovelace’s 456 Tensor Cores are more advanced and efficient per unit. The RTX 4090 D’s FP16 performance (73.54 TFLOPS) is more than double the V100’s (31.33 TFLOPS), indicating a substantial leap in AI compute capability despite the lower core count.

The RTX 4090 D is a consumer-oriented card with display outputs (1x HDMI 2.1 and 3x DisplayPort 1.4a), while the V100 SXM2 is a compute-only accelerator with no display outputs. The bus interface also differs: the RTX 4090 D uses PCIe 4.0 x16, while the V100 uses the older PCIe 3.0 x16 standard. These architectural differences—node size, core counts, RT support, and memory type—combine to produce the performance disparity seen in the benchmarks. The RTX 4090 D is a modern, feature-rich GPU, while the V100 is a legacy compute workhorse that has been surpassed in every measurable compute metric.