NVIDIA GB10 vs NVIDIA Tesla V100 SXM2 16 GB Comparison

The NVIDIA GB10 and NVIDIA Tesla V100 SXM2 16 GB represent two distinct generations of NVIDIA's computing lineup, separated by a wide gap in release timing and architectural philosophy. The GB10, a modern Blackwell 2.0 part, and the V100, a Volta-era workhorse, offer contrasting profiles in raw throughput, memory technology, and API support. Benchmark data shows a split decision: the GB10 dominates in OpenCL workloads, while the V100 holds a commanding lead in Vulkan performance. This analysis breaks down their head-to-head results, architectural deviations, and the specific scenarios where each accelerator proves superior.

Head-to-Head Benchmarks

The most striking divergence between these two GPUs appears in the Geekbench OpenCL test. The NVIDIA GB10 posts a score of 120137, while the NVIDIA Tesla V100 SXM2 16 GB trails significantly at 87454. This translates to a 37.4% advantage for the GB10 — a substantial margin that highlights the newer architecture's efficiency in compute-heavy, general-purpose workloads. The GB10's lead here is not marginal; it is a decisive victory that suggests its Blackwell 2.0 design, with its 29.71 TFLOPS of FP32 throughput, handles OpenCL's parallel processing demands far more effectively than the V100's 15.67 TFLOPS FP32 capability.

However, the reverse scenario unfolds in the Geekbench Vulkan test. The Tesla V100 SXM2 16 GB scores 141336, outpacing the GB10's 114648 by 18.9%. This is a notable reversal, considering the V100's older Volta architecture and its lack of dedicated ray tracing cores — the GB10 features 48 RT cores while the V100 has none. The V100's Vulkan dominance suggests that its 4096-bit memory bus and 897.0 GB/s bandwidth, combined with its higher pixel rate of 195.8 GPixel/s, provide substantial advantages in graphics-oriented or Vulkan-optimized rendering tasks. The V100's 128 ROPs versus the GB10's 48 ROPs likely contributes to this win, as rasterization and fragment processing scale with ROP count.

Looking at aggregate performance, the average benchmark scores place the GB10 slightly ahead. The GB10's average score is 117393, while the V100 averages 114395, giving the GB10 a 2.6% overall edge. When compared to their immediate rivals, both GPUs occupy similar competitive tiers. The GB10 is 0.3% ahead of the NVIDIA RTX 4000 SFF Ada Generation (avg score 117088) and 1.3% behind the AMD Radeon PRO W7700 (avg score 118976). The V100, meanwhile, is 0.4% ahead of the NVIDIA RTX A5500 Mobile (avg score 113944) and 3.3% ahead of the AMD Radeon PRO W7900 (avg score 110725). Both cards sit at the 95th percentile among all GPUs, indicating they are top-tier performers in their respective eras, but the GB10's newer silicon gives it a moderate aggregate edge.

Where Each One Wins

The NVIDIA GB10 is the clear winner in OpenCL-based compute workloads. Its 37.4% lead over the V100 in the Geekbench OpenCL test is the single largest delta between the two cards across any metric. This makes the GB10 the preferred choice for applications that rely heavily on OpenCL for general-purpose GPU computing, such as scientific simulations, data processing pipelines, or machine learning inference tasks that are not CUDA-optimized. The GB10's 6144 shading units, compared to the V100's 5120, provide more raw execution resources, and its FP32 throughput of 29.71 TFLOPS is nearly double the V100's 15.67 TFLOPS. For workloads where FP32 precision is the bottleneck, the GB10's advantage is overwhelming.

Conversely, the NVIDIA Tesla V100 SXM2 16 GB wins decisively in Vulkan rendering scenarios. Its 18.9% lead in the Geekbench Vulkan test positions it as the stronger choice for graphics-intensive applications that leverage Vulkan's low-overhead API. This includes modern game engines, real-time visualization tools, or any rendering workload that can exploit the V100's superior pixel throughput (195.8 GPixel/s versus the GB10's 116.1 GPixel/s) and its massive 4096-bit memory bus. The V100's 128 ROPs are a critical asset here, as they handle the final pixel output stage, and having nearly three times the ROP count of the GB10 (48) allows for faster fill rates in rasterization-heavy scenes.

Beyond these two benchmarks, the GB10 offers advantages in memory capacity and modern features. With 128 GB of LPDDR5X memory versus the V100's 16 GB of HBM2, the GB10 can accommodate far larger datasets in memory, reducing the need for frequent data transfers in large-scale AI or data analytics workloads. The GB10 also supports PCIe 5.0 x16, while the V100 is limited to PCIe 3.0 x16, which doubles the theoretical host interface bandwidth for data ingestion. The V100, however, counters with significantly higher memory bandwidth (897.0 GB/s versus 273.2 GB/s), making it superior for bandwidth-bound operations that require rapid access to smaller datasets.

Architecture Differences

The architectural gap between these two GPUs is generational. The GB10 is built on the Blackwell 2.0 architecture using a 5 nm TSMC process, while the V100 uses the Volta architecture on a 12 nm TSMC node. This process shrink allows the GB10 to pack more functionality into a smaller die: the GB10 measures 382 mm², less than half the V100's 815 mm². The V100's larger die houses 21,100 million transistors, whereas the GB10's transistor count is listed as unknown, but its transistor density is not published. The V100's transistor density is 25.9M per mm², which is low by modern standards due to the older process node.

Core configuration differs markedly. The GB10 features 6144 shading units, 384 texture mapping units (TMUs), and 48 render output units (ROPs). It also includes 48 ray tracing cores and 384 tensor cores. The V100, in contrast, has 5120 shading units, 320 TMUs, and 128 ROPs. It has no ray tracing cores at all, but it does pack 640 tensor cores — significantly more than the GB10. This makes the V100 potentially more capable in FP16 tensor operations, though the GB10's FP16 performance is listed as 29.71 TFLOPS (1:1) with FP32, while the V100 achieves 31.33 TFLOPS (2:1) for FP16. The V100's higher tensor core count and FP16 throughput suggests it was designed with early deep learning training in mind, where reduced precision was a key optimization.

Memory architecture is another fundamental divergence. The GB10 uses LPDDR5X memory on a 256-bit bus, while the V100 uses HBM2 on a 4096-bit bus. The V100's memory bus is 16 times wider, which is why it achieves 897.0 GB/s bandwidth despite slower memory clocks (876 MHz base, 1752 Mbps effective). The GB10's memory runs at 1067 MHz (8.5 Gbps effective) but delivers only 273.2 GB/s due to the narrower bus. This trade-off is clear: the V100 excels at streaming large blocks of data, while the GB10 relies on its larger capacity (128 GB vs 16 GB) to keep more data resident on-chip.

API support also separates them. The GB10 lists "N/A" for DirectX, OpenGL, and Vulkan support, indicating it is positioned as a pure compute or IGP part with limited graphics API compatibility. The V100, however, supports DirectX 12 (12_1), OpenGL 4.6, and Vulkan 1.4, making it a more flexible option for environments that require standard graphics APIs. This explains why the V100 excels in Vulkan benchmarks — it is architecturally and driver-wise equipped to handle those workloads, whereas the GB10's Vulkan score may reflect a less optimized path.

Specification Differences

The following specifications differ between the NVIDIA GB10 and NVIDIA Tesla V100 SXM2 16 GB:

• Architecture: Blackwell 2.0 (GB10) vs Volta (V100)
• Process Node: 5 nm (GB10) vs 12 nm (V100)
• Die Size: 382 mm² (GB10) vs 815 mm² (V100)
• Transistors: Unknown (GB10) vs 21,100 million (V100)
• Transistor Density: Not listed (GB10) vs 25.9M / mm² (V100)
• Base Clock: 1665 MHz (GB10) vs 1312 MHz (V100)
• Boost Clock: 2418 MHz (GB10) vs 1530 MHz (V100)
• Memory Size: 128 GB (GB10) vs 16 GB (V100)
• Memory Type: LPDDR5X (GB10) vs HBM2 (V100)
• Memory Bus Width: 256 bit (GB10) vs 4096 bit (V100)
• Memory Bandwidth: 273.2 GB/s (GB10) vs 897.0 GB/s (V100)
• Memory Clock: 1067 MHz / 8.5 Gbps (GB10) vs 876 MHz / 1752 Mbps (V100)
• Shading Units: 6144 (GB10) vs 5120 (V100)
• TMUs: 384 (GB10) vs 320 (V100)
• ROPs: 48 (GB10) vs 128 (V100)
• RT Cores: 48 (GB10) vs None (V100)
• Tensor Cores: 384 (GB10) vs 640 (V100)
• Pixel Rate: 116.1 GPixel/s (GB10) vs 195.8 GPixel/s (V100)
• Texture Rate: 928.5 GTexel/s (GB10) vs 489.6 GTexel/s (V100)
• FP32 Performance: 29.71 TFLOPS (GB10) vs 15.67 TFLOPS (V100)
• FP16 Performance: 29.71 TFLOPS 1:1 (GB10) vs 31.33 TFLOPS 2:1 (V100)
• TDP: 140 W (GB10) vs 250 W (V100)
• Slot Width: IGP (GB10) vs SXM Module (V100)
• Suggested PSU: 300 W (GB10) vs 600 W (V100)
• Bus Interface: PCIe 5.0 x16 (GB10) vs PCIe 3.0 x16 (V100)
• Display Outputs: 1x HDMI (GB10) vs No outputs (V100)
• API Support: N/A for DirectX/OpenGL/Vulkan (GB10) vs DirectX 12_1, OpenGL 4.6, Vulkan 1.4 (V100)
• Dimensions: 150 mm x 51 mm x 150 mm (GB10) vs not listed (V100)
• Production Status: Active (GB10) vs End-of-life (V100)
• Release Date: 2025-10-14 (GB10) vs 2017-06-20 (V100)
• Predecessor: Server Hopper (GB10) vs Tesla Pascal (V100)
• Successor: Server Rubin (GB10) vs Tesla Turing (V100)
• Launch MSRP: 3,999 USD (GB10) vs not listed (V100)

FAQ

Q: Which GPU has a higher average benchmark score?

A: The NVIDIA GB10 has a higher average benchmark score of 117393, compared to the Tesla V100 SXM2 16 GB's 114395, representing a 2.6% advantage for the GB10.

Q: Why does the Tesla V100 win the Vulkan benchmark despite being older?

A: The V100's Vulkan score of 141336 beats the GB10's 114648 by 18.9%. This is likely due to the V100's 128 ROPs versus the GB10's 48, its 4096-bit memory bus providing 897.0 GB/s bandwidth, and its full API support for Vulkan 1.4, whereas the GB10 lists N/A for Vulkan support.

Q: How do these GPUs compare in memory capacity and bandwidth?

A: The GB10 offers 128 GB of LPDDR5X memory on a 256-bit bus with 273.2 GB/s bandwidth. The V100 offers 16 GB of HBM2 on a 4096-bit bus with 897.0 GB/s bandwidth. The V100 has over three times the bandwidth, but the GB10 has eight times the capacity.

Q: What is the FP32 performance difference between the two?

A: The GB10 delivers 29.71 TFLOPS of FP32 performance, which is nearly double the V100's 15.67 TFLOPS. This aligns with the GB10's 37.4% lead in the OpenCL benchmark.

Q: Which GPU has more tensor cores?

A: The Tesla V100 SXM2 16 GB has 640 tensor cores, while the GB10 has 384. Despite fewer tensor cores, the GB10's FP16 performance is 29.71 TFLOPS (1:1), while the V100 achieves 31.33 TFLOPS (2:1).

Q: Are there differences in display output capabilities?

A: Yes. The GB10 includes 1x HDMI output, while the V100 has no display outputs at all. The V100 is designed as a compute-only SXM module, whereas the GB10 can drive a display.