NVIDIA A10G vs NVIDIA H200 NVL Comparison

NVIDIA H200 NVL and NVIDIA A10G are both server accelerators, but they are separated by multiple generations of architecture and design philosophy. The benchmark data reveals a decisive performance gap, with the H200 NVL delivering over double the OpenCL score of the A10G. This analysis breaks down the head-to-head results, architectural differences, and the specific use cases where each GPU holds an advantage.

Head-to-Head Benchmarks

The single available head-to-head benchmark result shows a dominant victory for the NVIDIA H200 NVL. In the Geekbench OpenCL test, the H200 NVL scores 334,891 points, while the A10G manages 158,063 points. This translates to a delta of 111.9%, meaning the H200 NVL is more than twice as fast as the A10G in this compute workload.

Context from the nearest rivals list makes this margin even more striking. The H200 NVL outperforms the AMD Instinct MI300X by 5.3% and is within striking distance of the NVIDIA B200, which leads it by only 3.1%. The A10G, in contrast, sits in a completely different performance tier. Its average benchmark score of 151,963 places it just 1.1% above the older NVIDIA Tesla V100 PCIe 32 GB and 6.5% below the NVIDIA A100 PCIe 40 GB.

The percentile rankings reinforce this separation. The H200 NVL sits at the 100th percentile among all GPUs, meaning it outperforms every other device in the database. The A10G, while still strong, holds the 97th percentile. This two-percentile gap represents a significant absolute difference in raw compute throughput.

When examining the delta percentages between the two cards directly, the story is unambiguous. The H200 NVL does not merely edge out the A10G; it more than doubles its OpenCL performance. For any workload that scales with raw FP32 or FP16 throughput, this gap will be decisive.

Architecture Differences

The architectural gulf between these two GPUs is vast. The H200 NVL is built on the Hopper architecture using the GH100 chip, fabricated on a 5 nm process by TSMC. The A10G uses the older Ampere architecture with the GA102 chip, manufactured on Samsung's 8 nm process. This process node difference alone accounts for a massive disparity in transistor density: 98.3 million transistors per square millimeter on the H200 NVL versus 45.1 million on the A10G.

The chip scale reflects this difference. The H200 NVL packs 80,000 million transistors on an 814 mm² die. The A10G contains 28,300 million transistors on a 628 mm² die. Despite the A10G's smaller transistor count, its larger physical footprint per transistor means the H200 NVL integrates nearly three times more transistors in only 30% more silicon area.

Memory configuration represents another fundamental divergence. The H200 NVL ships with 141 GB of HBM3e memory on a 6144-bit bus, delivering 4.89 TB/s of bandwidth. The A10G offers 24 GB of GDDR6 on a 384-bit bus, with 600.2 GB/s of bandwidth. The H200 NVL provides over eight times the memory bandwidth, which is critical for large language models and data-intensive inference workloads.

Compute resources follow the same pattern. The H200 NVL contains 16,896 shading units, 528 TMUs, and 528 tensor cores, but only 24 ROPs. The A10G has 9,216 shading units, 288 TMUs, 96 ROPs, and 288 tensor cores. The H200 NVL's FP32 throughput is 60.32 TFLOPS, nearly double the A10G's 31.52 TFLOPS. In FP16, the gap widens further: the H200 NVL delivers 120.6 TFLOPS with a 2:1 ratio, while the A10G is capped at 31.52 TFLOPS with a 1:1 ratio.

Power and physical specifications also differ sharply. The H200 NVL draws 600 W and requires a dual-slot cooler with a 1000 W suggested PSU. The A10G is far more modest at 150 W, single-slot, with a 450 W suggested PSU. Both cards use an 8-pin EPS connector and have no display outputs. The H200 NVL runs on PCIe 5.0 x16, while the A10G uses PCIe 4.0 x16.

The H200 NVL's release date of November 2024 places it firmly in the current server generation, while the A10G's April 2021 launch and end-of-life production status mark it as legacy hardware. The H200 NVL's predecessor is Server Ada, with Server Blackwell as its successor, while the A10G follows Tesla Turing and is succeeded by Server Ada.

The Verdict

The data presents a clear hierarchy. The H200 NVL is a top-tier compute accelerator designed for maximum throughput in AI training and high-performance computing. Its 100th percentile ranking, combined with a 111.9% lead over the A10G in OpenCL, makes it the definitive choice for workloads where raw performance is the sole criterion.

The A10G, despite being significantly slower, occupies a valid niche. Its 97th percentile ranking shows it remains a capable performer, and its 150 W power draw allows for dense deployment in multi-GPU servers. The A10G's 24 GB of GDDR6 memory, while small compared to the H200 NVL's 141 GB HBM3e, is sufficient for many inference and rendering tasks.

For organizations requiring the absolute fastest compute for large-scale model training or data center AI workloads, the H200 NVL is the data-backed choice. Its nearest rivals, the B200 and B300 SXM6 AC, are slightly faster, but the H200 NVL remains competitive with a 3.1% deficit to the B200 and a 9.4% deficit to the B300.

For deployments where power efficiency, physical space, or legacy software compatibility matter more than peak performance, the A10G is the practical option. Its 150 W TDP versus the H200 NVL's 600 W means a single H200 NVL consumes the power of four A10Gs, though it delivers roughly twice the performance.

FAQ

Q: How much faster is the NVIDIA H200 NVL than the NVIDIA A10G in OpenCL?

A: The H200 NVL scores 334,891 in Geekbench OpenCL, while the A10G scores 158,063. This gives the H200 NVL a 111.9% performance advantage.

Q: What are the memory capacities of these two GPUs?

A: The H200 NVL has 141 GB of HBM3e memory with 4.89 TB/s bandwidth. The A10G has 24 GB of GDDR6 memory with 600.2 GB/s bandwidth.

Q: Which GPU has a higher FP32 compute throughput?

A: The H200 NVL delivers 60.32 TFLOPS FP32, while the A10G delivers 31.52 TFLOPS. The H200 NVL is roughly 91% faster in FP32.

Q: What is the power consumption difference between the two?

A: The H200 NVL has a 600 W TDP and requires a 1000 W suggested PSU. The A10G has a 150 W TDP and requires a 450 W suggested PSU.

Q: How do these GPUs compare to their nearest rivals?

A: The H200 NVL is 5.3% faster than the AMD Instinct MI300X and 3.1% slower than the NVIDIA B200. The A10G is 1.1% faster than the Tesla V100 PCIe 32 GB and 6.5% slower than the A100 PCIe 40 GB.

Q: Are both GPUs still in production?

A: No. The H200 NVL has an active production status, while the A10G is end-of-life.

Where Each One Wins

The H200 NVL wins decisively in every compute-heavy scenario. Its 141 GB of HBM3e memory and 4.89 TB/s bandwidth make it suitable for large language model training and inference, where the entire model and its activations must reside in GPU memory. The 120.6 TFLOPS FP16 throughput with a 2:1 ratio is optimized for tensor operations common in deep learning. The 100th percentile ranking confirms it belongs at the top of any server GPU comparison.

The A10G wins in scenarios constrained by power and space. Its 150 W TDP and single-slot design allow for higher density in servers, and its 450 W suggested PSU means it can be deployed in systems with less robust power delivery. The 31.52 TFLOPS FP32 and FP16 throughput, while modest compared to the H200 NVL, is still respectable for a 150 W card. The 96 ROPs and 164.2 GPixel/s pixel rate are notably higher than the H200 NVL's 24 ROPs and 42.84 GPixel/s, making the A10G better suited for graphics-adjacent workloads that require rasterization, though neither card has display outputs.

For organizations standardizing on a single server GPU, the H200 NVL is the performance leader. The data shows no benchmark where the A10G beats it. The A10G remains viable for cost-sensitive or power-constrained deployments where its lower power draw and smaller physical footprint enable configurations that a 600 W dual-slot card cannot match.