NVIDIA A100 SXM4 40 GB vs NVIDIA B200 Comparison

The NVIDIA B200 and NVIDIA A100 SXM4 40 GB represent two distinct generations of NVIDIA's server AI accelerators, separated by a significant architectural leap. The data shows the B200 delivers a dominant performance advantage, while the A100 remains a capable, albeit older, option. The benchmark results indicate a clear generational shift in compute capability, memory bandwidth, and overall throughput.

FAQ

Q: What is the performance difference between the NVIDIA B200 and the A100 SXM4 40 GB in the available benchmark?

A: In the Geekbench OpenCL test, the B200 scores 345,482, which is 71.8% higher than the A100's score of 201,096. This makes the B200 the clear winner in this head-to-head comparison.

Q: How does the memory capacity and bandwidth compare between these two cards?

A: The B200 is equipped with 90 GB of HBM3e memory, delivering a bandwidth of 4.10 TB/s. In contrast, the A100 SXM4 40 GB has 40 GB of HBM2e memory with a bandwidth of 1.56 TB/s. The B200 offers more than double the capacity and over 2.6 times the bandwidth.

Q: What are the core specifications that differentiate these GPUs?

A: The B200 features 18,944 shading units, 592 TMUs, and 592 tensor cores, based on a 5 nm process. The A100 has 6,912 shading units, 432 TMUs, and 432 tensor cores, built on a 7 nm process. The B200 also has a higher boost clock of 1965 MHz compared to the A100's 1410 MHz.

Q: Which GPU has a higher FP32 (single-precision) performance?

A: The B200 delivers 74.45 TFLOPS of FP32 performance. This is significantly higher than the A100's 19.49 TFLOPS, representing an approximate 3.8x advantage for the B200 in this metric.

Q: What is the difference in power consumption and power delivery requirements?

A: The B200 has a TDP of 1000 W and requires a suggested PSU of 1400 W. The A100 SXM4 40 GB has a TDP of 400 W and a suggested PSU of 800 W. The B200's higher performance comes with a substantially higher power draw.

Q: How do these GPUs compare to their closest rivals in terms of average benchmark scores?

A: The B200's average score of 345,482 places it 3.2% ahead of the NVIDIA H200 NVL and 8.6% ahead of the AMD Instinct MI300X. The A100's average score of 187,147 places it 1.3% ahead of the NVIDIA RTX 5000 Ada Generation and 1.9% ahead of the NVIDIA A100 SXM4 80 GB.

The Verdict

The data is unequivocal: the NVIDIA B200 is designed for workloads demanding maximum throughput. Its 71.8% lead in OpenCL, combined with a 3.8x advantage in FP32 performance and a 2.6x advantage in memory bandwidth, makes it the superior choice for training frontier-scale AI models or running the most demanding high-performance computing (HPC) simulations. The B200's 90 GB memory capacity is a critical asset for handling datasets that do not fit in the A100's 40 GB frame buffer.

Conversely, the NVIDIA A100 SXM4 40 GB, now end-of-life, is positioned as a legacy workhorse. Its lower 400 W TDP and 800 W suggested PSU make it a less power-hungry option for existing infrastructure. Its performance, while far below the B200, is still competitive with modern workstation cards, as shown by its 1.3% lead over the RTX 5000 Ada Generation. The A100 is a reasonable choice for organizations with established Ampere-generation software stacks or for inference tasks where the higher power draw of the B200 is not justified.

The primary deciding factor is workload scale and power budget. For new deployments pushing the boundaries of AI, the B200 is the data-backed choice. For cost-sensitive or power-constrained environments running established models, the A100 remains a functional, if outdated, option.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test, and it shows a decisive victory for the NVIDIA B200. The B200 scored 345,482, while the A100 SXM4 40 GB scored 201,096. This represents a delta of 71.8%, meaning the B200 is nearly three-quarters faster in this general-purpose compute test. This score is a composite of many compute operations, and the margin reflects the B200's architectural advantages in raw throughput.

The B200's superiority is further contextualized by its average score of 345,482, which places it in the 100th percentile of all GPUs. It is 16.8% ahead of the NVIDIA L40S, a modern workstation card, and 8.6% ahead of the AMD Instinct MI300X, a direct competitor. The A100's average score of 187,147 places it in the 98th percentile, but its nearest rivals are much closer in performance, with deltas of just 1.3% to 3.7%. This indicates that the A100 is at the edge of its performance class, while the B200 is at the top.

The delta between the two cards is not incremental; it is a generational gap. The B200's score is not just an improvement, but a redefinition of what is expected from a server accelerator. The A100's score, while respectable, is firmly rooted in the previous generation's performance envelope.

Specification Differences

The core specifications show the B200 as a massive upgrade over the A100. The B200's chip, the GB100, is built on a 5 nm process at TSMC, while the A100's GA100 chip uses a 7 nm process. The B200 integrates 104,000 million transistors, nearly double the A100's 54,200 million. The A100's die size is listed as 826 mm², but the B200's die size is not provided.

Clock speeds differ significantly. The B200 has a base clock of 700 MHz and a boost clock of 1965 MHz. The A100 has a higher base clock of 1095 MHz but a much lower boost clock of 1410 MHz. The memory clocks also differ, with the B200 running at 2000 MHz (8 Gbps effective) and the A100 at 1215 MHz (2.4 Gbps effective).

The memory subsystems are in different leagues. The B200 features 90 GB of HBM3e on a 4096-bit bus, yielding 4.10 TB/s of bandwidth. The A100 has 40 GB of HBM2e on a wider 5120-bit bus, but its bandwidth is only 1.56 TB/s. The B200's newer memory technology more than compensates for its narrower bus.

The bus interface has also been updated. The B200 uses PCIe 5.0 x16, while the A100 uses PCIe 4.0 x16. In terms of power, the B200's TDP is 1000 W with a 1400 W suggested PSU, whereas the A100's TDP is 400 W with an 800 W suggested PSU. The A100 has no power connectors listed, while the B200's are not specified. Both are SXM modules with no display outputs.

Architecture Differences

The NVIDIA B200 is based on the Blackwell architecture, while the A100 SXM4 40 GB is based on the older Ampere architecture. This is the fundamental difference that drives all performance gaps. Blackwell is designed for the next generation of AI and HPC, focusing on extreme scale and throughput.

The compute resources reflect this. The B200 has 18,944 shading units, 592 TMUs, and 592 tensor cores. The A100 has 6,912 shading units, 432 TMUs, and 432 tensor cores. The B200's tensor core count is significantly higher, which is crucial for AI matrix math. The B200's FP16 performance is listed as 1,191.2 TFLOPS (16:1), while the A100's is 77.97 TFLOPS (4:1), showing a massive increase in tensor throughput.

Memory technology is another key architectural difference. The B200 uses HBM3e, a modern high-bandwidth memory standard, while the A100 uses HBM2e. The B200's 4.10 TB/s bandwidth is essential for feeding its powerful compute cores. The A100's 1.56 TB/s bandwidth is a bottleneck by comparison.

The manufacturing process also differs. The B200 is built on a 5 nm node, allowing for a higher transistor density (104,000 million transistors) than the A100's 7 nm node (54,200 million transistors). This is a core reason for the B200's higher performance and efficiency per transistor, despite its higher overall power draw. The B200's predecessor is listed as "Server Hopper," while the A100's is "Tesla Turing," and the B200's successor is "Server Rubin," while the A100's is "Server Ada."

Where Each One Wins

NVIDIA B200 wins decisively in every performance metric available. It is the clear choice for:

• Large-scale AI training: The 90 GB memory capacity and 4.10 TB/s bandwidth allow it to handle massive models and datasets without sharding, and its 71.8% lead in OpenCL shows superior compute throughput.
• FP32 compute: With 74.45 TFLOPS, it is 3.8x faster than the A100, making it ideal for HPC simulations and scientific computing that require high single-precision performance.
• High-performance inference: The B200's raw speed and high tensor core count enable faster response times for complex, large-batch inference workloads.
• Future-proofing: As a current, active product, it is designed for modern software stacks and will be supported for years to come.

NVIDIA A100 SXM4 40 GB wins in specific operational and legacy scenarios:

• Power-constrained environments: With a 400 W TDP and 800 W suggested PSU, it consumes far less power than the B200, making it easier to integrate into existing clusters with lower power budgets.
• Legacy software compatibility: As a mature Ampere product, it may have more established software optimizations and driver support in certain specialized environments.
• Existing infrastructure: For organizations already using Ampere-based systems, the A100 offers a drop-in upgrade path for nodes that do not require the B200's extreme performance.
• Specific workloads: Its higher pixel rate of 225.6 GPixel/s and higher ROP count of 160, compared to the B200's 47.16 GPixel/s and 24 ROPs, suggest it may be more suited for certain graphics or rasterization tasks, though both cards have no display outputs.

The benchmark data shows no scenario where the A100 outperforms the B200 in compute throughput. The A100's wins are purely in the domains of power efficiency and integration with older systems.