NVIDIA A100 SXM4 80 GB vs NVIDIA GB10 Comparison

The NVIDIA A100 SXM4 80 GB and NVIDIA GB10 represent two distinct generations of server-focused accelerators, with the former built on the Ampere architecture and the latter on Blackwell 2.0. Benchmark data from the Geekbench suite shows a clear performance hierarchy, but the decision between them depends heavily on workload characteristics, memory requirements, and form-factor constraints. The A100 delivers a 60.3% higher Vulkan score in head-to-head testing, while the GB10 counters with a newer process node, higher boost clocks, and double the memory capacity.

Where Each One Wins

The NVIDIA A100 SXM4 80 GB is the clear winner in raw compute throughput for graphics and compute workloads. Its Geekbench Vulkan score of 183,725 puts it in the 98th percentile of all GPUs, a full 60.3% ahead of the GB10’s 114,648 in the same test. This advantage is driven by the A100’s massive memory bandwidth of 2.04 TB/s (from HBM2e across a 5120-bit bus) and its higher FP16 throughput of 77.97 TFLOPS (4:1 ratio). The A100 also holds a structural edge in pixel fill rate at 225.6 GPixel/s versus the GB10’s 116.1 GPixel/s, and its 160 ROPs compared to 48 on the GB10 makes it better suited for rasterization-heavy tasks. For any workload that stresses memory bandwidth or requires sustained FP16 compute, the data points decisively to the A100.

The NVIDIA GB10 wins on memory capacity and clock speeds. It offers 128 GB of LPDDR5X memory versus the A100’s 80 GB, a 60% capacity increase that matters for very large models or datasets that exceed the A100’s frame buffer. The GB10 also has a higher boost clock of 2418 MHz versus 1410 MHz, which contributes to its superior texture rate of 928.5 GTexel/s (compared to 609.1 GTexel/s on the A100) and its higher FP32 peak of 29.71 TFLOPS (versus 19.49 TFLOPS). In pure FP32 compute, the GB10 is approximately 52% faster, making it the better choice for single-precision scientific simulations or workloads that do not leverage Tensor Cores for FP16. The GB10’s 95th percentile ranking across all GPUs, while lower than the A100’s 98th, still indicates very strong overall performance, particularly given its 140 W TDP versus the A100’s 400 W.

FAQ

Q: Which GPU has the higher Geekbench Vulkan score?

A: The NVIDIA A100 SXM4 80 GB scores 183,725 in Geekbench Vulkan, which is 60.3% higher than the GB10’s 114,648. This makes the A100 the outright winner in that benchmark.

Q: How does memory capacity differ between the two?

A: The GB10 comes with 128 GB of LPDDR5X memory, while the A100 has 80 GB of HBM2e. The GB10’s capacity is 60% larger, but the A100’s bandwidth is far higher at 2.04 TB/s versus 273.2 GB/s.

Q: What is the FP32 performance comparison?

A: The GB10 delivers 29.71 TFLOPS of FP32 compute, which is about 52% higher than the A100’s 19.49 TFLOPS. This gives the GB10 an edge in single-precision tasks that do not use Tensor Cores.

Q: Are there differences in the process node and architecture?

A: Yes. The A100 uses a 7 nm process with the Ampere architecture (GA100 chip), while the GB10 is built on a 5 nm process with the Blackwell 2.0 architecture (GB20B chip). The GB10’s smaller node contributes to its higher clocks and lower power draw.

Q: Which GPU has a smaller physical footprint?

A: The GB10 is an IGP (integrated graphics processor) with dimensions of 150 mm by 51 mm by 150 mm, whereas the A100 is an OAM module with no listed dimensions. The GB10 also includes one HDMI output, while the A100 has no display outputs.

Q: How do the two compare in terms of production status?

A: The A100 is marked as end-of-life, with a release date of 2020-11-15, while the GB10 is active, with a release date of 2025-10-14. The GB10 is the newer product by nearly five years.

Architecture Differences

The A100 is based on the GA100 chip using the Ampere architecture, fabricated on a 7 nm process at TSMC. It packs 54,200 million transistors on an 826 mm² die, yielding a transistor density of 65.6 million per mm². The GB10, in contrast, uses the GB20B chip with the Blackwell 2.0 architecture, built on a 5 nm process (also TSMC). Its die size is 382 mm², and its transistor count is listed as unknown, so direct density comparisons are not possible. The GB10’s smaller die and newer node allow for higher clock speeds — a base of 1665 MHz and boost of 2418 MHz versus the A100’s 1275 MHz base and 1410 MHz boost.

The two differ significantly in their compute unit configurations. The A100 has 6912 shading units, 432 TMUs, 160 ROPs, and 432 Tensor Cores. The GB10 has fewer shading units (6144), fewer TMUs (384), and far fewer ROPs (48), but it adds 48 RT cores and 384 Tensor Cores. The A100 does not list RT cores at all, indicating it lacks dedicated ray tracing hardware. In terms of memory, the A100 uses HBM2e with a 5120-bit bus, while the GB10 uses LPDDR5X with a 256-bit bus. The A100’s memory clock is 1593 MHz (3.2 Gbps effective), and the GB10’s is 1067 MHz (8.5 Gbps effective). The A100’s Tensor Core FP16 performance is 77.97 TFLOPS (4:1 ratio), whereas the GB10’s FP16 is 29.71 TFLOPS (1:1 ratio), meaning the A100 has a substantial lead in reduced-precision Tensor operations.

The bus interface also differs: the A100 uses PCIe 4.0 x16, while the GB10 uses PCIe 5.0 x16. The GB10 is an IGP with a 150 mm length, 51 mm height, and 150 mm width, and it includes one HDMI output. The A100 is an OAM module with no dimensions listed and no display outputs. Power delivery also diverges — the A100 has a 400 W TDP and a suggested PSU of 800 W, while the GB10 has a 140 W TDP and a suggested PSU of 300 W. Neither uses external power connectors.

Specification Differences

The two accelerators diverge across nearly every major specification category. Process node: 7 nm (A100) versus 5 nm (GB10). Transistor count: 54,200 million (A100) versus unknown (GB10). Die size: 826 mm² (A100) versus 382 mm² (GB10). Base clock: 1275 MHz (A100) versus 1665 MHz (GB10). Boost clock: 1410 MHz (A100) versus 2418 MHz (GB10). Memory size: 80 GB (A100) versus 128 GB (GB10). Memory type: HBM2e (A100) versus LPDDR5X (GB10). Memory bus width: 5120 bit (A100) versus 256 bit (GB10). Memory bandwidth: 2.04 TB/s (A100) versus 273.2 GB/s (GB10). Shading units: 6912 (A100) versus 6144 (GB10). TMUs: 432 (A100) versus 384 (GB10). ROPs: 160 (A100) versus 48 (GB10). RT cores: none (A100) versus 48 (GB10). Tensor Cores: 432 (A100) versus 384 (GB10). Pixel rate: 225.6 GPixel/s (A100) versus 116.1 GPixel/s (GB10). Texture rate: 609.1 GTexel/s (A100) versus 928.5 GTexel/s (GB10). FP32: 19.49 TFLOPS (A100) versus 29.71 TFLOPS (GB10). FP16: 77.97 TFLOPS (A100) versus 29.71 TFLOPS (GB10). TDP: 400 W (A100) versus 140 W (GB10). Slot width: OAM Module (A100) versus IGP (GB10). Bus interface: PCIe 4.0 x16 (A100) versus PCIe 5.0 x16 (GB10). Display outputs: none (A100) versus 1x HDMI (GB10). Production status: end-of-life (A100) versus active (GB10). Release date: 2020-11-15 (A100) versus 2025-10-14 (GB10). The A100 has no launch MSRP listed, while the GB10 has a launch MSRP of 3,999 USD.

Head-to-Head Benchmarks

The only directly comparable benchmark between the two is Geekbench Vulkan, where the A100 scores 183,725 and the GB10 scores 114,648. This gives the A100 a decisive 60.3% advantage, the largest delta in any performance metric shared between them. The A100’s win is consistent with its higher average benchmark score of 183,725 (the GB10 averages 117,393 across its two OpenCL and Vulkan results), but the Vulkan gap is particularly stark. The A100’s nearest rivals in the database — the NVIDIA RTX 5000 Ada Generation (184,664, -0.5% delta), the NVIDIA RTX PRO 5000 Blackwell (182,109, +0.9% delta), the NVIDIA A100 SXM4 40 GB (187,147, -1.8% delta), and the NVIDIA GeForce RTX 4090 D (178,050, +3.2% delta) — all cluster within a few percent of its score, confirming that the A100 sits at the top of the performance tier. The GB10, by contrast, is surrounded by mid-range rivals: the NVIDIA RTX 4000 SFF Ada Generation (117,088, +0.3% delta), the AMD Radeon PRO W7700 (118,976, -1.3% delta), the NVIDIA Tesla V100 SXM2 16 GB (114,395, +2.6% delta), and the NVIDIA RTX A5500 Mobile (113,944, +3.0% delta). This places the GB10 in a completely different performance class, roughly 60% behind the A100 in Vulkan.

Looking beyond the head-to-head, the GB10’s own benchmark suite includes a Geekbench OpenCL score of 120,137, which is higher than its Vulkan score, but still below the A100’s single Vulkan result. The A100 has no OpenCL score listed, so a direct comparison on that API is not possible. However, the FP32 and texture rate differences suggest the GB10 could outperform the A100 in certain compute patterns: the GB10’s 928.5 GTexel/s texture rate is 52% higher than the A100’s 609.1 GTexel/s, and its FP32 output (29.71 TFLOPS) beats the A100’s (19.49 TFLOPS) by the same margin. These strengths, combined with the GB10’s 128 GB memory capacity, make it a viable alternative for workloads that are memory-capacity-bound or rely on single-precision math. The A100, however, dominates in memory bandwidth (2.04 TB/s versus 273.2 GB/s, a 7.5x difference) and in FP16 Tensor performance (77.97 TFLOPS versus 29.71 TFLOPS, a 2.6x difference), making it the superior choice for deep learning training or inference that uses FP16 accumulation. The data shows no single winner across all metrics; the A100 wins the head-to-head benchmark outright, but the GB10’s architectural advantages in clock speed and capacity create a distinct use-case split.