NVIDIA A100 SXM4 80 GB vs NVIDIA B200 Comparison

The NVIDIA B200 and NVIDIA A100 SXM4 80 GB represent two distinct generations of NVIDIA's server-class accelerators, separated by a significant architectural leap. The benchmark data available places the B200 in the top percentile of all GPUs, while the A100, despite being an older design, remains a highly capable compute engine. This analysis breaks down their positions based on the provided specifications and performance metrics.

Where Each One Wins

The B200 is the clear performance leader in raw compute throughput. Its Geekbench OpenCL score of 345,482 places it at the 100th percentile of all GPUs, meaning it outperforms every other device in the database for that test. This is its primary domain: absolute computational horsepower for the most demanding workloads. The data shows it holds a 3.2% advantage over the NVIDIA H200 NVL and an 8.6% lead over the AMD Instinct MI300X, establishing it as the top-tier option in its immediate competitive set.

The A100 SXM4 80 GB wins in a different category: efficiency and legacy compatibility. Its Geekbench Vulkan score of 183,725 puts it at the 98th percentile, which is still exceptional. More importantly, its performance profile is closely matched with a different set of rivals, including the RTX 5000 Ada Generation (delta of -0.5%) and the RTX PRO 5000 Blackwell (delta of 0.9%). This suggests the A100 operates in a sweet spot where it competes with workstation-class parts, offering a balance of compute capability and power draw that the B200 does not. The A100 also wins on power consumption, with a TDP of 400 W compared to the B200’s 1000 W, making it the more reasonable choice for systems with power constraints.

Architecture Differences

The two chips are built on fundamentally different process nodes and microarchitectures. The B200 uses the GB100 chip based on the Blackwell architecture, manufactured on a 5 nm process at TSMC. It packs 104,000 million transistors. In contrast, the A100 uses the GA100 chip based on the older Ampere architecture, built on a 7 nm process, also at TSMC, with 54,200 million transistors. This nearly doubles transistor count for the B200, enabling its massive compute scaling.

Memory technology is another major divergence. The B200 is equipped with 90 GB of HBM3e memory on a 4096-bit bus, delivering a bandwidth of 4.10 TB/s. The A100 comes with 80 GB of HBM2e memory on a wider 5120-bit bus, but its bandwidth is only 2.04 TB/s. While the A100 has a broader memory bus, the B200’s newer HBM3e standard provides double the bandwidth, which is critical for memory-bound workloads.

Clock speeds tell a counterintuitive story. The A100 has a higher base clock of 1275 MHz and a boost clock of 1410 MHz. The B200 has a much lower base clock of 700 MHz but a significantly higher boost clock of 1965 MHz. This wide boost range on the B200 suggests it relies heavily on power management and thermal headroom to reach its peak performance. The memory clocks also differ, with the B200’s memory running at 2000 MHz (8 Gbps effective) versus the A100’s 1593 MHz (3.2 Gbps effective).

The compute resources are starkly different in configuration. The B200 has 18,944 shading units, 592 TMUs, and 24 ROPs. The A100 has 6,912 shading units, 432 TMUs, and 160 ROPs. The B200 has far more shading units and TMUs, but its ROP count is drastically lower. This suggests a design optimized for shader and texture-heavy compute rather than traditional rasterization. Both cards have no dedicated RT cores, but they do have Tensor Cores: 592 on the B200 and 432 on the A100.

Head-to-Head Benchmarks

Direct head-to-head benchmark comparisons between the B200 and A100 are not available in the data. However, their individual benchmark scores and rival comparisons allow for meaningful inference. The B200’s Geekbench OpenCL score of 345,482 is its only recorded benchmark, and it stands alone as the top score in the database. The A100’s Geekbench Vulkan score is 183,725, which is a different test, making a direct numerical comparison invalid. However, the percentile rankings are revealing: the B200 is at the 100th percentile, while the A100 is at the 98th.

Looking at the B200’s nearest rivals provides context for its performance level. It is 16.8% ahead of the NVIDIA L40S, a substantial margin. It is 8.6% ahead of the AMD Instinct MI300X. Against the NVIDIA H200 NVL, it is 3.2% faster, and it trails the newer NVIDIA B300 SXM6 AC by 6.6%. These deltas indicate that the B200 is positioned at the top of the current product stack, with only its direct successor being faster.

For the A100, its nearest rivals show a much tighter cluster of performance. It is essentially tied with the RTX 5000 Ada Generation, being just 0.5% slower. It is 0.9% faster than the RTX PRO 5000 Blackwell. It is 1.8% slower than the A100 SXM4 40 GB variant, which is a curiosity given the 80 GB model has more memory. It also leads the GeForce RTX 4090 D by 3.2%. This grouping suggests that the A100’s performance is well-balanced and remains competitive with much newer workstation parts, even if it is not in the same league as the B200.

The FP32 and FP16 throughput figures highlight the generational gap. The B200 delivers 74.45 TFLOPS of FP32 performance and 1,191.2 TFLOPS of FP16 (16:1). The A100 delivers 19.49 TFLOPS of FP32 and 77.97 TFLOPS of FP16 (4:1). This represents a roughly 3.8x improvement in FP32 for the B200 and a 15.3x improvement in FP16, though the ratio difference (16:1 vs 4:1) indicates a different approach to mixed-precision computing. The B200’s architecture clearly favors tensor-heavy FP16 operations, while the A100’s FP16 ratio is more conservative.

The Verdict

Choosing between these two accelerators depends entirely on workload requirements and system constraints. For users who need the absolute maximum compute throughput, the B200 is the definitive choice. Its 100th percentile ranking, combined with its substantial leads over the H200 and MI300X, makes it the undisputed performance leader. Its high bandwidth and massive transistor count are designed for current-generation AI training and large-scale simulation where every bit of performance matters.

The A100 SXM4 80 GB, while older and marked as end-of-life, remains a highly relevant option. Its 98th percentile performance is nothing to dismiss, and its 400 W power draw is significantly lower than the B200’s 1000 W. This makes it a more manageable component for dense server deployments where power and cooling are limited. Its performance parity with newer workstation cards like the RTX 5000 Ada Generation shows it still delivers competitive results for a wide range of compute tasks. The data suggests its 80 GB of HBM2e memory, while slower, is still generous for many inference and data-processing workloads.

FAQ

Q: Which GPU has the higher benchmark score?

A: The NVIDIA B200 has a Geekbench OpenCL score of 345,482, while the NVIDIA A100 SXM4 80 GB has a Geekbench Vulkan score of 183,725. These are different tests, but the B200’s score places it at the 100th percentile, while the A100 is at the 98th.

Q: How much faster is the B200 than the AMD Instinct MI300X?

A: The B200 is 8.6% faster than the AMD Instinct MI300X based on average benchmark scores.

Q: What is the memory bandwidth difference?

A: The B200 has a memory bandwidth of 4.10 TB/s using HBM3e, while the A100 has a bandwidth of 2.04 TB/s using HBM2e. The B200 offers double the bandwidth.

Q: Does the A100 still compete with modern GPUs?

A: Yes, the A100’s average score of 183,725 is only 0.5% lower than the NVIDIA RTX 5000 Ada Generation and 0.9% higher than the RTX PRO 5000 Blackwell, indicating it remains competitive with newer workstation parts.

Q: What are the power consumption figures?

A: The B200 has a TDP of 1000 W, while the A100 has a TDP of 400 W. The A100 consumes significantly less power.

Q: Which GPU has more Tensor Cores?

A: The B200 has 592 Tensor Cores, while the A100 has 432 Tensor Cores.

Specification Differences

| Specification | NVIDIA B200 | NVIDIA A100 SXM4 80 GB |

|---|---|---|

| Chip | GB100 | GA100 |

| Architecture | Blackwell | Ampere |

| Process Node | 5 nm | 7 nm |

| Transistors | 104,000 million | 54,200 million |

| Die Size | Not specified | 826 mm² |

| Base Clock | 700 MHz | 1275 MHz |

| Boost Clock | 1965 MHz | 1410 MHz |

| Memory Size | 90 GB | 80 GB |

| Memory Type | HBM3e | HBM2e |

| Memory Bus | 4096 bit | 5120 bit |

| Memory Bandwidth | 4.10 TB/s | 2.04 TB/s |

| Shading Units | 18,944 | 6,912 |

| TMUs | 592 | 432 |

| ROPs | 24 | 160 |

| Tensor Cores | 592 | 432 |

| FP32 Performance | 74.45 TFLOPS | 19.49 TFLOPS |

| FP16 Performance | 1,191.2 TFLOPS (16:1) | 77.97 TFLOPS (4:1) |

| TDP | 1000 W | 400 W |

| Slot Width | SXM Module | OAM Module |

| Suggested PSU | 1400 W | 800 W |

| Bus Interface | PCIe 5.0 x16 | PCIe 4.0 x16 |

| Production Status | Active | End-of-life |

| Release Date | Not specified | 2020-11-15 |

| Predecessor | Server Hopper | Tesla Turing |