NVIDIA A100 SXM4 80 GB vs NVIDIA A10M Comparison

The NVIDIA A100 SXM4 80 GB and the NVIDIA A10M are both Ampere-generation server accelerators, but they are engineered for fundamentally different workloads. Benchmark data shows the A100 SXM4 80 GB sits in the 98th percentile of all GPUs with an average score of 183,725, while the A10M lands in the 96th percentile with 135,230. This 48,495-point gap (roughly 36% higher raw score) makes the A100 the clear performance leader, but the A10M’s lower power draw and physical footprint offer a distinct deployment advantage.

Head-to-Head Benchmarks

The data shows no direct head-to-head benchmark comparison between the two cards; they were tested under different API workloads. The A100 SXM4 80 GB was evaluated using Geekbench Vulkan, scoring 183,725, while the A10M was tested with Geekbench OpenCL, scoring 135,230. While these are different test suites, both measure raw compute throughput, and the A100’s score is 35.9% higher.

Looking at the A100’s nearest rivals, it trails the NVIDIA RTX 5000 Ada Generation by just 0.5% (184,664 vs 183,725) and the A100 SXM4 40 GB by 1.8% (187,147). It beats the RTX PRO 5000 Blackwell by 0.9% and the GeForce RTX 4090 D by 3.2%. This places the 80 GB A100 essentially at parity with the latest professional workstation flagships, despite being an older end-of-life product.

The A10M’s nearest rivals tell a different story. Its 135,230 OpenCL score is statistically identical to the RTX 4000 Ada Generation (135,218, 0% delta) and the AMD Radeon PRO W6800 (135,396, -0.1%). It trails the Radeon Pro W6800X Duo by 0.4% and the Radeon PRO V620 by 0.9%. The A10M is firmly in the mid-range professional segment, trading blows with cards that have similar memory configurations and thermal envelopes.

The A100’s advantage in raw compute is substantial: 19.49 TFLOPS FP32 versus the A10M’s 23.44 TFLOPS FP32. Wait — the data actually shows the A10M has higher FP32 throughput. This is a critical nuance. The A10M’s GA102 chip is a consumer-derived design with 7,168 shading units, while the A100’s GA100 has 6,912. In pure single-precision gaming-style workloads, the A10M would win, but the A100’s advantage lies in its 2.04 TB/s memory bandwidth versus the A10M’s 500.2 GB/s — a 4x difference that dominates large-data compute tasks.

Architecture Differences

The two chips share the Ampere architecture but diverge sharply in implementation. The A100 SXM4 80 GB uses the GA100 chip built on TSMC’s 7 nm process, packing 54,200 million transistors into an 826 mm² die. The A10M uses the GA102 chip on Samsung’s 8 nm node, with 28,300 million transistors on a 628 mm² die. This makes the A100 roughly 1.9x denser in transistor count (65.6M / mm² vs 45.1M / mm²).

Memory is the biggest architectural split. The A100 uses 80 GB of HBM2e on a 5120-bit bus, yielding 2.04 TB/s bandwidth. The A10M uses 20 GB of GDDR6 on a 320-bit bus, yielding 500.2 GB/s. The A100 has 4x the memory capacity and 4x the bandwidth, but the A10M’s GDDR6 operates at a higher effective clock (12.5 Gbps vs 3.2 Gbps effective).

Compute resources differ in kind, not just quantity. The A100 has 432 tensor cores and no dedicated RT cores; the A10M has 224 tensor cores and 56 RT cores. The A100’s FP16 throughput is 77.97 TFLOPS (4:1 ratio), while the A10M’s is 23.44 TFLOPS (1:1 ratio). The A100’s tensor-core-focused design is built for AI training, while the A10M’s RT cores enable ray tracing — a feature the A100 lacks entirely.

The A100 is an OAM module with no power connectors, drawing 400 W TDP. The A10M is a single-slot PCIe card with an 8-pin EPS connector, drawing 150 W TDP. The A100 requires an 800 W suggested PSU; the A10M needs only 450 W. The A10M also has display outputs (none listed for the A100) and supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4 — the A100 lists no API support.

FAQ

Q: Which card has higher raw FP32 performance?

A: The A10M. It delivers 23.44 TFLOPS FP32 versus the A100’s 19.49 TFLOPS. This is due to the A10M’s higher shading unit count (7,168 vs 6,912) and higher boost clock (1635 MHz vs 1410 MHz).

Q: Why does the A100 score higher in benchmarks despite lower FP32?

A: The A100’s Geekbench Vulkan score of 183,725 vs the A10M’s OpenCL score of 135,230 reflects different test conditions, but the A100’s 2.04 TB/s memory bandwidth and 80 GB capacity give it a massive advantage in memory-bound compute tasks that dominate professional workloads.

Q: Can the A10M do ray tracing?

A: Yes. The A10M has 56 RT cores and supports DirectX 12 Ultimate (12_2), making it capable of hardware-accelerated ray tracing. The A100 has no RT cores listed, so it cannot perform dedicated ray tracing.

Q: What is the memory bandwidth difference?

A: The A100 SXM4 80 GB offers 2.04 TB/s from HBM2e, while the A10M offers 500.2 GB/s from GDDR6. That is a 4.08x difference in favor of the A100.

Q: Which card has better API support?

A: The A10M explicitly lists DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4 support. The A100 lists no API support in the data, suggesting it is intended for compute-only deployments without graphics APIs.

Q: What are the physical form factor differences?

A: The A100 is an OAM module with no power connectors, designed for SXM4 server sockets. The A10M is a single-slot, 267 mm × 112 mm PCIe card with an 8-pin EPS connector.

Specification Differences

| Specification | NVIDIA A100 SXM4 80 GB | NVIDIA A10M |

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

| Chip | GA100 | GA102 |

| Process Node | 7 nm (TSMC) | 8 nm (Samsung) |

| Transistors | 54,200 million | 28,300 million |

| Die Size | 826 mm² | 628 mm² |

| Transistor Density | 65.6M / mm² | 45.1M / mm² |

| Base Clock | 1275 MHz | 975 MHz |

| Boost Clock | 1410 MHz | 1635 MHz |

| Memory Clock | 1593 MHz (3.2 Gbps effective) | 1563 MHz (12.5 Gbps effective) |

| Memory Size | 80 GB HBM2e | 20 GB GDDR6 |

| Memory Bus | 5120 bit | 320 bit |

| Memory Bandwidth | 2.04 TB/s | 500.2 GB/s |

| Shading Units | 6912 | 7168 |

| TMUs | 432 | 224 |

| ROPs | 160 | 80 |

| RT Cores | None | 56 |

| Tensor Cores | 432 | 224 |

| Pixel Rate | 225.6 GPixel/s | 130.8 GPixel/s |

| Texture Rate | 609.1 GTexel/s | 366.2 GTexel/s |

| FP32 | 19.49 TFLOPS | 23.44 TFLOPS |

| FP16 | 77.97 TFLOPS (4:1) | 23.44 TFLOPS (1:1) |

| TDP | 400 W | 150 W |

| Slot Width | OAM Module | Single-slot |

| Power Connectors | None | 8-pin EPS |

| Suggested PSU | 800 W | 450 W |

| Dimensions | Not listed | 267 mm × 112 mm |

| APIs | None listed | DirectX 12 Ultimate, OpenGL 4.6, Vulkan 1.4 |

Where Each One Wins

The A100 SXM4 80 GB wins decisively in memory-bound and AI-accelerated workloads. Its 80 GB HBM2e with 2.04 TB/s bandwidth is 4x the capacity and 4x the bandwidth of the A10M. This makes it the superior choice for large language model training, scientific simulations, and any workload where dataset size exceeds 20 GB. Its 432 tensor cores and 77.97 TFLOPS FP16 (4:1) throughput are optimized for mixed-precision AI training.

The A10M wins in power efficiency and physical integration. At 150 W TDP, it consumes only 37.5% of the A100’s 400 W envelope. It is a single-slot PCIe card that fits in standard server chassis without special OAM sockets or power cabling. Its 23.44 TFLOPS FP32 and 7,168 shading units make it better suited for traditional graphics or compute tasks that rely on single-precision throughput.

The A10M also wins on graphics features. It has 56 RT cores and full DirectX 12 Ultimate support, meaning it can handle ray-traced rendering or Vulkan-based workloads. The A100 has no RT cores and no listed API support, making it unsuitable for graphics-intensive tasks. The A10M’s 12.5 Gbps effective memory clock also indicates faster per-pin data transfer, though total bandwidth is lower.

The A100 wins on memory capacity and bandwidth without contest. The A10M wins on raw FP32 compute and API compatibility. These are not competing products; they serve different segments of the server market.

The Verdict

The data points to a clear split: choose the NVIDIA A100 SXM4 80 GB for maximum compute throughput in memory-heavy AI and scientific workloads. Its benchmark score of 183,725 places it in the 98th percentile, statistically tied with the RTX 5000 Ada Generation (-0.5%) and ahead of the RTX 4090 D by 3.2%. The 80 GB of HBM2e memory is the defining feature — no rival in its nearest list matches that bandwidth figure.

Choose the NVIDIA A10M for power-constrained deployments or graphics-capable compute. Its 135,230 OpenCL score is 96th percentile, exactly matching the RTX 4000 Ada Generation (0% delta). The 150 W TDP and single-slot form factor allow dense server configurations that the 400 W OAM-module A100 cannot match. The A10M’s 23.44 TFLOPS FP32 and RT core support make it a more versatile accelerator for mixed graphics and compute workloads.

The A100’s end-of-life status and lack of display outputs signal it is a pure compute accelerator for data centers. The A10M’s API support and lower power draw signal a more flexible edge or inference card. For AI training with massive datasets, the A100’s 2.04 TB/s bandwidth is non-negotiable. For real-time inference or ray tracing, the A10M’s feature set is more appropriate. The benchmark gap of 48,495 points is real, but it reflects different optimization targets: the A100 for throughput, the A10M for efficiency and compatibility.