NVIDIA A100 PCIe 40 GB vs NVIDIA B200 Comparison

# NVIDIA B200 vs NVIDIA A100 PCIe 40 GB

The NVIDIA B200 and NVIDIA A100 PCIe 40 GB sit at opposite ends of the server GPU spectrum, with the B200 representing the current Blackwell flagship while the A100 is an older Ampere part now marked end-of-life. The benchmark data shows a decisive performance gap: the B200 scores 345,482 in Geekbench OpenCL against the A100's 178,627, a 93.4% advantage. The B200 also holds a perfect 100th percentile ranking among all GPUs, while the A100 sits at the 97th percentile. This is not a close contest on raw compute, but the A100's lower power draw and established ecosystem may still serve specific workloads.

Head-to-Head Benchmarks

The only direct comparison available is the Geekbench OpenCL test, and it is a blowout. The B200 delivers 345,482 points, while the A100 manages 178,627. That works out to a 93.4% delta — nearly double the performance. For context, the B200's closest rival in the database is the NVIDIA B300 SXM6 AC at 369,831 points, which beats it by 6.6%, but the B200 still outpaces the NVIDIA H200 NVL (334,891) by 3.2% and the AMD Instinct MI300X (317,994) by 8.6%. Meanwhile, the A100's nearest rivals cluster tightly around it: the AMD Radeon Pro W6800X (160,671) is just 1.1% behind, the AMD Radeon PRO W7800 (164,894) is 1.4% ahead, the NVIDIA RTX A5500 (165,217) is 1.6% ahead, and the NVIDIA RTX 4500 Ada Generation (166,094) is 2.2% ahead. The A100 is competitive with modern workstation cards, but the B200 operates in an entirely different performance class.

The delta between the two is larger than any gap between the B200 and its own rivals. While the B200 beats the L40S by 16.8%, it beats the A100 by 93.4%. This suggests the A100 is not merely a generation behind — it is a fundamentally different tier of hardware. In raw OpenCL throughput, the B200's score is 1.93 times the A100's, meaning you would need roughly two A100s to match a single B200 in this workload. The A100's best result from its two recorded benchmarks is the OpenCL score; its Vulkan score of 146,380 is lower, which indicates the A100 is less optimized for that API. The B200 has no Vulkan result recorded, so cross-API comparisons are limited to OpenCL.

Where Each One Wins

The B200 wins the only head-to-head benchmark, and it wins decisively. Its 345,482 OpenCL score places it at the 100th percentile of all GPUs, which means no other GPU in the database scores higher on average. For compute-heavy tasks like large-scale AI training, scientific simulation, or high-throughput data processing, the B200 is the clear choice. Its 74.45 TFLOPS FP32 throughput is 3.8 times the A100's 19.49 TFLOPS, and its FP16 performance of 1,191.2 TFLOPS (16:1 ratio) dwarfs the A100's 77.97 TFLOPS (4:1 ratio). If your workload is bound by floating-point math, the B200 is in a different league.

The A100 wins in power efficiency and form factor flexibility. At 250 W TDP, it draws a quarter of the B200's 1000 W, and its suggested PSU is 600 W versus 1400 W. The A100 is a dual-slot PCIe card measuring 267 mm long and 111 mm tall, while the B200 is an SXM module — a completely different physical format. The A100 uses a standard 8-pin EPS power connector and PCIe 4.0 x16 interface, making it far easier to slot into existing server infrastructure. The B200 requires PCIe 5.0 and a module-based chassis. For organizations with legacy systems or power constraints, the A100 remains viable despite its lower performance. The A100's 40 GB of HBM2e memory is also sufficient for many inference workloads, even if it is less than half the B200's 90 GB.

FAQ

Q: How much faster is the B200 than the A100 in OpenCL?

A: The B200 scores 345,482 versus the A100's 178,627, a 93.4% advantage. This is the largest delta between either card and any of its nearest rivals.

Q: Does the A100 have any benchmark where it beats the B200?

A: No. The data records one head-to-head benchmark (Geekbench OpenCL), and the B200 wins it. The B200 also has 1 win and 0 losses in the head-to-head comparison.

Q: What is the B200's closest competitor?

A: The NVIDIA B300 SXM6 AC scores 369,831, which is 6.6% higher. The NVIDIA H200 NVL is 3.2% behind at 334,891, and the AMD Instinct MI300X is 8.6% behind at 317,994.

Q: How does the A100 compare to modern workstation GPUs?

A: The A100's average benchmark score of 162,504 places it near the NVIDIA RTX A5500 (165,217, 1.6% higher), the AMD Radeon PRO W7800 (164,894, 1.4% higher), and the NVIDIA RTX 4500 Ada Generation (166,094, 2.2% higher). It beats the AMD Radeon Pro W6800X by 1.1%.

Q: Is the A100 still in production?

A: No. The A100 PCIe 40 GB is marked as end-of-life, with a release date of June 21, 2020. The B200 is listed as active production.

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

A: The B200 has 90 GB of HBM3e memory on a 4096-bit bus with 4.10 TB/s bandwidth. The A100 has 40 GB of HBM2e on a 5120-bit bus with 1.56 TB/s bandwidth.

Specification Differences

The B200 and A100 differ in nearly every measurable specification. The B200 uses a GB100 chip on TSMC's 5 nm process with 104,000 million transistors, while the A100 uses a GA100 chip on 7 nm with 54,200 million transistors. The B200 has nearly double the transistor count. The B200's base clock is 700 MHz with a 1965 MHz boost, whereas the A100 runs at 765 MHz base and 1410 MHz boost. Despite the A100's higher base clock, the B200's boost clock is 39% higher.

Shading units favor the B200 at 18,944 versus 6,912 — a 2.7x difference. Texture mapping units are 592 versus 432, but the A100 has more ROPs at 160 versus 24. Tensor cores are 592 on the B200 against 432 on the A100. The B200's FP32 throughput is 74.45 TFLOPS versus 19.49 TFLOPS, and its FP16 output is 1,191.2 TFLOPS versus 77.97 TFLOPS. Pixel rate reverses the trend: the A100 achieves 225.6 GPixel/s against the B200's 47.16 GPixel/s, because the A100's higher ROP count and pixel clock matter more for rasterized output — though neither card has display outputs.

Memory is another major split. The B200 packs 90 GB of HBM3e at 4.10 TB/s, while the A100 offers 40 GB of HBM2e at 1.56 TB/s. The B200's bus is 4096-bit, narrower than the A100's 5120-bit bus, but the newer memory type delivers 2.6 times the bandwidth. Power and cooling differ completely: the B200 is a 1000 W SXM module with a 1400 W suggested PSU, while the A100 is a 250 W dual-slot card using an 8-pin EPS connector and a 600 W PSU. The B200 uses PCIe 5.0 x16; the A100 uses PCIe 4.0 x16.

Architecture Differences

The architectural gap is generational. The B200 is built on the Blackwell architecture (GB100 chip), while the A100 is Ampere (GA100). Blackwell is fabricated on a 5 nm process at TSMC, compared to Ampere's 7 nm node. This process shrink, combined with the larger transistor budget, explains much of the B200's performance lead. The B200 has 104,000 million transistors versus 54,200 million — a 92% increase.

Memory technology also differs. The B200 uses HBM3e with 8 Gbps effective speed, while the A100 uses HBM2e at 2.4 Gbps effective. The B200's 4.10 TB/s bandwidth is 2.6 times the A100's 1.56 TB/s. The B200's FP16 ratio is 16:1, meaning it performs 16 FP16 operations per clock per shader, while the A100's ratio is 4:1. This makes the B200 disproportionately faster for mixed-precision AI workloads. The A100 has a larger die at 826 mm² and a transistor density of 65.6M per mm², but the B200's die size is not recorded — the 5 nm process likely allows a smaller or more densely packed die. The B200's predecessor is listed as "Server Hopper" and its successor as "Server Rubin," while the A100's predecessor is "Tesla Turing" and its successor is "Server Ada." The B200 has no display outputs, matching the A100, which also has none.

The Verdict

The data is unambiguous: the B200 is the far more capable GPU for compute-intensive workloads. It scores 93.4% higher in OpenCL, holds the 100th percentile ranking, and offers 3.8 times the FP32 throughput and 15.3 times the FP16 throughput of the A100. Its 90 GB HBM3e memory with 4.10 TB/s bandwidth is built for large models and datasets that would exhaust the A100's 40 GB capacity. If you are deploying for AI training, large-scale inference, or scientific computing and have the power budget and chassis support, the B200 is the obvious choice — it outperforms every GPU in the database except the B300 SXM6 AC, which beats it by 6.6%.

The A100 is for legacy deployments and constrained environments. It is end-of-life, but its 250 W TDP, dual-slot PCIe form factor, and standard 8-pin EPS power connector make it drop-in compatible with older servers. Its 97th percentile ranking is still respectable, and it trades blows with modern workstation cards like the RTX A5500 and RTX 4500 Ada Generation. The A100's 40 GB of HBM2e is sufficient for mid-sized inference workloads, and its 1.56 TB/s bandwidth is adequate for many batch processing tasks. If you already own A100s, the data does not suggest they are obsolete — but if you are buying new hardware, the B200's benchmark lead justifies its higher power requirements. Choose the B200 for maximum throughput; choose the A100 only when power, cooling, or PCIe compatibility forces your hand.