NVIDIA A10G vs NVIDIA B200 Comparison

The NVIDIA B200 and NVIDIA A10G are both server accelerators from NVIDIA, but they occupy entirely different performance and efficiency tiers. In the single available head-to-head benchmark, the B200 delivers a decisive 118.6% higher Geekbench OpenCL score than the A10G, making it the undisputed performance leader. However, the A10G offers a unique combination of a compact single-slot design, a 150 W TDP, and a mature software ecosystem that still places it in the 97th percentile of all GPUs. The data shows these are not direct competitors but rather solutions for different workload scales, with the B200 targeting the highest-end AI training and the A10G serving as a capable, efficient inference or edge server option.

FAQ

Q: How much faster is the NVIDIA B200 than the A10G in the available benchmark?

A: In the Geekbench OpenCL test, the B200 scores 345,482 while the A10G scores 158,063. This represents a 118.6% performance advantage for the B200, meaning it is more than twice as fast in this specific compute test.

Q: What is the performance percentile ranking for each GPU?

A: The B200 sits at the 100th percentile of all GPUs in the database, while the A10G is at the 97th percentile. This indicates the B200 is at the absolute top of the performance spectrum, whereas the A10G is still exceptionally high-performing but not the absolute peak.

Q: What are the memory specifications for each card?

A: The B200 features 90 GB of HBM3e memory on a 4096-bit bus, delivering 4.10 TB/s of bandwidth. The A10G has 24 GB of GDDR6 memory on a 384-bit bus with 600.2 GB/s of bandwidth. The B200 offers 3.75 times the capacity and roughly 6.8 times the memory bandwidth.

Q: How do their power requirements and physical designs differ?

A: The B200 is an SXM module with a 1000 W TDP and requires a 1400 W suggested PSU. The A10G is a single-slot, 267 mm long card with a 150 W TDP, a suggested 450 W PSU, and uses an 8-pin EPS power connector.

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

A: The B200 achieves 74.45 TFLOPS FP32, while the A10G delivers 31.52 TFLOPS. This means the B200 has roughly 2.4 times the FP32 throughput of the A10G.

Q: What is the production status of each GPU?

A: The B200 is listed as "Active" in production, while the A10G is marked as "End-of-life." This suggests the A10G is being phased out in favor of newer architectures, whereas the B200 is a current flagship.

Where Each One Wins

The B200 wins decisively in raw compute performance. Its Geekbench OpenCL score of 345,482 is 118.6% higher than the A10G's 158,063. This advantage is further underscored by its 74.45 TFLOPS FP32 rate, which is more than double the A10G's 31.52 TFLOPS. The B200 also dominates in memory bandwidth at 4.10 TB/s versus 600.2 GB/s, making it the clear choice for memory-bandwidth-intensive workloads such as large-scale AI model training or massive data processing.

The A10G wins in efficiency and form factor. With a 150 W TDP compared to the B200's 1000 W, the A10G delivers its performance at a fraction of the power draw. Its single-slot design and 267 mm length make it suitable for dense server configurations where physical space is at a premium. The A10G also carries full API support including DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, whereas the B200's API fields are listed as null, suggesting the A10G is better suited for workloads requiring these graphics APIs.

For applications that prioritize power efficiency and physical footprint over absolute performance, the A10G is the winner. For applications that need the maximum compute throughput and memory capacity, the B200 is the definitive choice.

Architecture Differences

The two GPUs are built on fundamentally different architectures. The B200 uses the Blackwell architecture on a 5 nm process node fabricated by TSMC, while the A10G uses the Ampere architecture on an 8 nm node from Samsung. This process advantage is reflected in the transistor counts: the B200 packs 104,000 million transistors, compared to the A10G's 28,300 million. The B200's die size is not listed, but the A10G has a die size of 628 mm² with a transistor density of 45.1M per mm².

The B200's chip is the GB100, whereas the A10G uses the GA102. In terms of compute resources, the B200 has 18,944 shading units, 592 TMUs, and 592 tensor cores. The A10G has 9,216 shading units, 288 TMUs, and 288 tensor cores. Notably, the A10G has 72 ray tracing cores, a feature that is not listed for the B200. The B200 has 24 ROPs, while the A10G has 96 ROPs, a significant difference in pixel processing capability.

Memory architecture also diverges sharply. The B200 uses HBM3e memory across a 4096-bit bus, while the A10G uses GDDR6 on a 384-bit bus. The B200's memory clock is listed at 2000 MHz (8 Gbps effective), while the A10G's is 1563 MHz (12.5 Gbps effective). The B200's boost clock is 1965 MHz versus the A10G's 1710 MHz, though the B200's base clock is lower at 700 MHz compared to 1320 MHz.

The B200 is a product of the Server Blackwell generation, while the A10G belongs to Server Ampere. Their predecessors and successors also differ: the B200 follows Server Hopper and leads to Server Rubin, while the A10G follows Tesla Turing and leads to Server Ada.

Specification Differences

The two cards differ across nearly every major specification category. The B200 has a 5 nm process node versus the A10G's 8 nm, with the B200 featuring 104,000 million transistors to the A10G's 28,300 million. The A10G has a listed die size of 628 mm², while the B200's die size is not provided.

Clock speeds differ notably. The B200 has a 700 MHz base clock and a 1965 MHz boost clock, while the A10G has a 1320 MHz base and 1710 MHz boost. Memory configurations are vastly different: the B200 offers 90 GB of HBM3e with a 4096-bit bus and 4.10 TB/s bandwidth, while the A10G has 24 GB of GDDR6 on a 384-bit bus with 600.2 GB/s.

Compute units show the B200's superiority in most areas: 18,944 shading units versus 9,216, 592 TMUs versus 288, and 592 tensor cores versus 288. However, the A10G has more ROPs (96 versus 24) and includes 72 ray tracing cores that are not listed for the B200. Pixel rate favors the A10G at 164.2 GPixel/s versus 47.16 GPixel/s, while texture rate favors the B200 at 1,163.3 GTexel/s versus 492.5 GTexel/s.

FP32 performance is 74.45 TFLOPS for the B200 and 31.52 TFLOPS for the A10G. FP16 performance shows a massive divergence: the B200 achieves 1,191.2 TFLOPS with a 16:1 ratio, while the A10G achieves 31.52 TFLOPS with a 1:1 ratio. Power consumption differs by an order of magnitude: 1000 W for the B200 versus 150 W for the A10G, with suggested PSUs of 1400 W and 450 W respectively.

The B200 is an SXM module, while the A10G is a single-slot card measuring 267 mm by 112 mm. The A10G uses an 8-pin EPS power connector, while the B200's power connector is not specified. The A10G supports PCIe 4.0 x16, while the B200 uses PCIe 5.0 x16. The A10G has API support for DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, but the B200 lists no API support.

Head-to-Head Benchmarks

The only direct benchmark comparison available is the Geekbench OpenCL test. In this test, the B200 scores 345,482 against the A10G's 158,063. The B200 wins this head-to-head with a delta of 118.6%, meaning it more than doubles the A10G's score. This is a comprehensive victory that highlights the B200's architectural superiority in raw compute throughput.

The B200's performance relative to its own rivals provides additional context. It is 3.2% ahead of the NVIDIA H200 NVL, 8.6% ahead of the AMD Instinct MI300X, and 16.8% ahead of the NVIDIA L40S. However, it trails the NVIDIA B300 SXM6 AC by 6.6%. This places the B200 firmly at the top tier of current accelerators, with only the B300 surpassing it.

The A10G's position among its rivals shows a different competitive landscape. It is 1.1% ahead of the NVIDIA Tesla V100 PCIe 32 GB and 9.3% ahead of the AMD Instinct MI100. It trails the AMD Radeon Pro W6800X by 5.4% and the NVIDIA A100 PCIe 40 GB by 6.5%. This indicates the A10G is competitive with the previous generation of high-end accelerators but not in the same class as the B200.

The average benchmark score reinforces this division. The B200 has an average benchmark score of 345,482, while the A10G's average is 151,963. The B200's score is more than double the A10G's average, and the B200 is in the 100th percentile of all GPUs, whereas the A10G is in the 97th.

The Verdict

The data makes it clear that these are not competing products. The NVIDIA B200 is a flagship server accelerator designed for maximum performance, as evidenced by its 100th percentile ranking, 118.6% lead over the A10G in Geekbench OpenCL, and its position among rivals like the H200 NVL and MI300X. Its 90 GB of HBM3e memory and 4.10 TB/s bandwidth make it ideal for the most demanding AI training and high-performance computing workloads. The B200 is the correct choice for organizations that need the absolute highest compute throughput and have the power and cooling infrastructure to support a 1000 W TDP module.

The NVIDIA A10G, despite being end-of-life, remains a highly relevant option for specific use cases. Its 97th percentile ranking shows it is still a strong performer, and its 150 W TDP, single-slot form factor, and 267 mm length make it suitable for dense, power-constrained server deployments. With 24 GB of GDDR6 memory and support for DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, the A10G is better suited for inference workloads, rendering tasks, or applications that require graphics APIs that the B200 does not support.

The choice between them is straightforward. For maximum compute performance with no compromises, the B200 is the definitive winner. For a balanced solution that prioritizes efficiency, compactness, and API compatibility, the A10G is the appropriate pick. The benchmark data shows a two-fold performance gap, but the A10G's efficiency advantages make it a viable alternative for specific deployment scenarios. Ultimately, the B200 is for those who need the fastest acceleration available, while the A10G serves those who need capable performance in a smaller, more efficient package.