NVIDIA A10G vs NVIDIA B300 SXM6 AC Comparison

Where Each One Wins

The benchmark data splits the two accelerators into entirely different performance tiers. The NVIDIA B300 SXM6 AC wins the only shared benchmark, Geekbench OpenCL, with a score of 369,831 against the A10G's 158,063 — a 134% advantage. That single result tells the story: the B300 SXM6 AC is designed to obliterate compute workloads, while the A10G occupies a more modest segment.

Looking at the B300 SXM6 AC's nearest rivals, it sits at the 100th percentile of all GPUs. Its OpenCL score beats the NVIDIA B200 by 7%, the NVIDIA H200 NVL by 10.4%, the AMD Instinct MI300X by 16.3%, and the NVIDIA L40S by 25%. These are not marginal wins; they represent a clear generational leap over even the most recent server accelerators. The A10G, by contrast, sits at the 97th percentile, which sounds close but is misleading. Its average benchmark score of 151,963 places it in a completely different league — it trails the AMD Radeon Pro W6800X by 5.4% and the NVIDIA A100 PCIe 40 GB by 6.5%, while only edging out the NVIDIA Tesla V100 PCIe 32 GB by 1.1% and the AMD Instinct MI100 by 9.3%.

The use-case split is stark. The B300 SXM6 AC wins on sheer compute throughput, which makes it suited for large-scale AI training and inference where every additional TFLOPS translates into faster iteration. The A10G wins on accessibility and efficiency in a practical sense: it is a single-slot card with a 150 W TDP, versus the B300's 1100 W TDP and SXM module form factor. The A10G can slot into existing PCIe 4.0 x16 servers with a 450 W suggested PSU; the B300 requires PCIe 6.0 x16 and a 1500 W suggested PSU. For workloads that fit within 24 GB of GDDR6 memory, the A10G remains viable, but for anything requiring the B300's 288 GB of HBM3e, there is no contest.

Architecture Differences

The two chips are separated by two full architecture generations and fundamentally different design philosophies. The B300 SXM6 AC uses the GB110 chip built on TSMC's 5 nm process, packing 208,000 million transistors onto a 1628 mm² die — a transistor density of 127.8 million per square millimeter. The A10G uses the GA102 chip on Samsung's 8 nm process, with 28,300 million transistors on a 628 mm² die, yielding 45.1 million per square millimeter. The B300 has nearly 7.4 times the transistor count and a die that is 2.6 times larger, which explains the massive performance gap.

The memory subsystems could not be more different. The B300 SXM6 AC features 288 GB of HBM3e on an 8192-bit bus, delivering 8.19 TB/s of bandwidth. The A10G has 24 GB of GDDR6 on a 384-bit bus, providing 600.2 GB/s. That is a 13.6-fold difference in bandwidth, which directly impacts memory-bound workloads. Clock speeds also differ: the B300 runs at a 1665 MHz base and 2032 MHz boost, while the A10G runs at 1320 MHz base and 1710 MHz boost. The B300's memory runs at 2000 MHz (8 Gbps effective), while the A10G's runs at 1563 MHz (12.5 Gbps effective).

Compute resources follow the same pattern. The B300 fields 18,944 shading units, 592 TMUs, and 592 tensor cores, but only 24 ROPs. The A10G has 9,216 shading units, 288 TMUs, 96 ROPs, and 72 RT cores. The B300 lacks dedicated RT cores, reflecting its server-focused design. Pixel rate goes to the A10G at 164.2 GPixel/s versus 48.77 GPixel/s for the B300, but texture rate favors the B300 at 1,202.9 GTexel/s versus 492.5 GTexel/s. FP32 and FP16 compute are both 76.99 TFLOPS (1:1) for the B300, while the A10G delivers 31.52 TFLOPS for both.

The B300 is built on Blackwell Ultra architecture in the Server Blackwell (Bxx) generation, while the A10G is Ampere in the Server Ampere (Axx) generation. The B300 supports no display outputs and has no DirectX, OpenGL, or Vulkan APIs, whereas the A10G supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4. The B300 is a pure compute accelerator; the A10G retains some graphics API compatibility despite having no display outputs.

FAQ

Q: Which card has higher memory bandwidth?

A: The NVIDIA B300 SXM6 AC delivers 8.19 TB/s from 288 GB of HBM3e on an 8192-bit bus. The NVIDIA A10G provides 600.2 GB/s from 24 GB of GDDR6 on a 384-bit bus — a 13.6-fold bandwidth advantage for the B300.

Q: How much faster is the B300 SXM6 AC in OpenCL?

A: The B300 scores 369,831 in Geekbench OpenCL versus 158,063 for the A10G, a 134% difference. The B300 also outperforms its nearest rival, the NVIDIA B200, by 7%, and the H200 NVL by 10.4%.

Q: What is the power consumption difference?

A: The B300 SXM6 AC has a 1100 W TDP and requires a 1500 W suggested PSU. The A10G has a 150 W TDP with a 450 W suggested PSU. The B300 consumes over seven times the power of the A10G.

Q: Can the A10G handle graphics workloads?

A: The A10G supports DirectX 12 Ultimate (12_2), OpenGL 4.6, and Vulkan 1.4, and has 72 RT cores. The B300 SXM6 AC has no graphics API support and no RT cores, making it purely compute-focused.

Q: Which card has more tensor cores?

A: The B300 SXM6 AC has 592 tensor cores, while the A10G has 288 tensor cores — exactly double. Both deliver FP16 at 1:1 ratio with FP32, but the B300's FP16 is 76.99 TFLOPS versus 31.52 TFLOPS for the A10G.

Q: What are the form factor differences?

A: The B300 SXM6 AC is an SXM module with no display outputs and no power connectors listed. The A10G is a single-slot card measuring 267 mm in length and 112 mm in height, with an 8-pin EPS power connector.

Specification Differences

| Specification | NVIDIA B300 SXM6 AC | NVIDIA A10G |

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

| Architecture | Blackwell Ultra | Ampere |

| Process Node | 5 nm (TSMC) | 8 nm (Samsung) |

| Transistors | 208,000 million | 28,300 million |

| Die Size | 1628 mm² | 628 mm² |

| Transistor Density | 127.8M / mm² | 45.1M / mm² |

| Base Clock | 1665 MHz | 1320 MHz |

| Boost Clock | 2032 MHz | 1710 MHz |

| Memory Clock | 2000 MHz (8 Gbps effective) | 1563 MHz (12.5 Gbps effective) |

| Memory Size | 288 GB | 24 GB |

| Memory Type | HBM3e | GDDR6 |

| Memory Bus | 8192 bit | 384 bit |

| Memory Bandwidth | 8.19 TB/s | 600.2 GB/s |

| Shading Units | 18,944 | 9,216 |

| TMUs | 592 | 288 |

| ROPs | 24 | 96 |

| RT Cores | None | 72 |

| Tensor Cores | 592 | 288 |

| Pixel Rate | 48.77 GPixel/s | 164.2 GPixel/s |

| Texture Rate | 1,202.9 GTexel/s | 492.5 GTexel/s |

| FP32 | 76.99 TFLOPS | 31.52 TFLOPS |

| FP16 | 76.99 TFLOPS (1:1) | 31.52 TFLOPS (1:1) |

| TDP | 1100 W | 150 W |

| Slot Width | SXM Module | Single-slot |

| Power Connectors | None listed | 8-pin EPS |

| Suggested PSU | 1500 W | 450 W |

| Bus Interface | PCIe 6.0 x16 | PCIe 4.0 x16 |

| Display Outputs | No outputs | No outputs |

| DirectX | N/A | 12 Ultimate (12_2) |

| OpenGL | N/A | 4.6 |

| Vulkan | N/A | 1.4 |

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

| Release Date | 2025-09-10 | 2021-04-11 |

Head-to-Head Benchmarks

The only shared benchmark is Geekbench OpenCL, and it is a landslide. The B300 SXM6 AC scores 369,831, while the A10G scores 158,063. The delta is 134% — the B300 more than doubles the A10G's output. To put that in perspective, the B300's nearest rival, the NVIDIA B200, scores 345,482, which is 7% lower. The A10G's nearest rival, the NVIDIA Tesla V100 PCIe 32 GB, scores 150,305, only 1.1% behind. The gap between the B300 and A10G is larger than the gap between the A10G and a GPU from two generations prior.

The A10G does have a Vulkan score of 145,863 in its own benchmark suite, but the B300 has no Vulkan support at all, so no comparison is possible. The wins tally is 1-0 in favor of the B300. The A10G cannot claim a single benchmark victory, and the data suggests none would materialize on any compute-focused test.

Looking at the percentile data reinforces the gulf. The B300 sits at the 100th percentile of all GPUs — a perfect score. The A10G sits at the 97th percentile, which is excellent by any historical measure but pales next to the B300's absolute dominance. The B300's average benchmark score of 369,831 is more than double the A10G's average of 151,963.

The Verdict

The data supports only one conclusion for raw compute: the NVIDIA B300 SXM6 AC is in a class of its own. It leads every nearest rival by at least 7%, sits at the 100th percentile, and delivers 134% more OpenCL performance than the A10G. Any workload that can utilize 288 GB of HBM3e memory and 76.99 TFLOPS of FP32 compute will see massive gains. The B300 is the clear choice for frontier AI training, large-scale inference, and scientific computing where time-to-solution is paramount.

The NVIDIA A10G, however, has a distinct role. It is end-of-life but remains active in the 97th percentile, with a 150 W TDP that allows deployment in dense servers without special cooling or power infrastructure. Its single-slot form factor and PCIe 4.0 compatibility make it drop-in ready for existing infrastructure. For workloads that fit within 24 GB of GDDR6 memory and do not require the B300's extreme bandwidth, the A10G offers a practical path — especially in scenarios where the B300's 1100 W TDP and SXM module requirement are prohibitive.

The decision hinges on scale and infrastructure. If the workload demands the absolute fastest compute and the data center can support a 1500 W PSU per module, the B300 is the only rational choice. If the workload is modest, latency-tolerant, or constrained by physical space and power, the A10G remains a serviceable option despite its age. Both cards have no display outputs, so neither is suited for graphics work in the traditional sense, though the A10G's API support and RT cores give it at least theoretical graphics capability.

There is no nuance in the benchmark numbers: the B300 wins decisively. The only question is whether the operational overhead of the B300 — higher power, larger footprint, newer platform requirements — is justified by the performance. The data says yes for high-end compute, no for edge or density-constrained deployments.