NVIDIA A100 SXM4 80 GB vs NVIDIA L40 Comparison

The NVIDIA L40 and the NVIDIA A100 SXM4 80 GB represent two distinct approaches to server acceleration: one optimized for raw graphics and modern compute pipelines, the other for massive memory capacity and AI throughput. The data in this comparison is limited but telling, revealing a clear performance hierarchy in the one shared benchmark while highlighting fundamental architectural divergences that dictate their respective roles in a data center.

Where Each One Wins

Based on the available benchmark data, the NVIDIA L40 is the clear winner in the only head-to-head test recorded. In the Geekbench Vulkan benchmark, the L40 scores 237,295 against the A100 SXM4 80 GB's 183,725, a substantial 29.2% advantage. This win is significant because Vulkan is a low-level graphics and compute API, an area where the L40's consumer-derived architecture excels. The L40's design, built for real-time rendering and graphics workloads, gives it a natural edge in these scenarios.

The A100 SXM4 80 GB, conversely, does not win any of the recorded comparisons. However, its strengths lie in areas not captured by the provided benchmarks. With its 80 GB of HBM2e memory and 2.04 TB/s of bandwidth, the A100 is designed for memory-bound AI training and inference tasks where capacity and throughput are paramount. The data does not include any FP16 or memory-specific tests, so its theoretical advantages in those domains are not quantified here. The L40 wins the execution race, while the A100's value proposition is rooted in its enormous memory pool and high-bandwidth design, which are simply not measured in the Geekbench Vulkan test.

Architecture Differences

The two GPUs are built on fundamentally different architectures from different generations. The L40 is based on the Ada Lovelace architecture, specifically the AD102 chip, fabricated on a 5 nm process at TSMC. It packs 76,300 million transistors into a 609 mm² die, achieving a transistor density of 125.3 million per square millimeter. The A100, in contrast, uses the older Ampere architecture with the GA100 chip, built on a 7 nm process. It contains 54,200 million transistors on a much larger 826 mm² die, resulting in a lower density of 65.6 million per square millimeter. This generational leap in manufacturing allows the L40 to pack more compute units into a smaller space.

The compute configurations diverge sharply. The L40 features 18,176 shading units, 568 TMUs, and 192 ROPs, alongside 142 RT cores and 568 Tensor Cores. The A100 has a much lower count of 6,912 shading units, 432 TMUs, and 160 ROPs. Notably, the A100 has no RT cores listed in the fact pack, while it does have 432 Tensor Cores. The L40's Tensor Core count is higher, but the A100's FP16 performance is reported as 77.97 TFLOPS (4:1) versus the L40's 90.52 TFLOPS (1:1). This suggests different ratios for Tensor Core operations, with the L40 offering a 1:1 ratio for FP16 and the A100 using a 4:1 ratio to achieve its peak. The A100's architecture is focused on AI acceleration, while the L40's is a more balanced design that includes dedicated hardware for graphics.

Memory architecture is another major divider. The L40 uses 48 GB of GDDR6 memory on a 384-bit bus, providing 864.0 GB/s of bandwidth. The A100 uses 80 GB of HBM2e on a massive 5120-bit bus, delivering 2.04 TB/s. The A100's memory bandwidth is more than double that of the L40, a critical factor for large datasets. The L40 also has a dual-slot form factor with a 16-pin power connector and display outputs, while the A100 is an OAM module with no display outputs, indicating its purpose is purely for compute in a server chassis.

Head-to-Head Benchmarks

The only direct benchmark comparison provided is Geekbench Vulkan, and it is a decisive victory for the L40. The L40 scores 237,295, while the A100 SXM4 80 GB scores 183,725. This represents a 29.2% delta in favor of the L40. This is a substantial performance gap in a compute and graphics API test. The result indicates that for workloads leveraging Vulkan, the L40 is significantly faster.

This single data point paints a clear picture: the L40 is over a quarter faster in this specific test. The A100's architecture, while powerful for AI, does not translate to Vulkan performance in the same way. The L40's higher clock speeds and newer architecture provide a significant advantage in this scenario. The fact that the A100 has no DirectX, OpenGL, or Vulkan API entries in its specifications further suggests that its primary design goals do not include graphics-centric APIs, making this benchmark a particularly favorable one for the L40.

FAQ

Q: Which GPU is faster in the Geekbench Vulkan benchmark?

A: The NVIDIA L40 is faster, scoring 237,295 compared to the A100 SXM4 80 GB's 183,725, a 29.2% difference.

Q: How much more memory does the A100 have compared to the L40?

A: The A100 SXM4 80 GB has 80 GB of HBM2e memory, while the L40 has 48 GB of GDDR6 memory, a difference of 32 GB.

Q: What are the architectural generations of each GPU?

A: The L40 is based on the Ada Lovelace architecture, while the A100 is based on the older Ampere architecture.

Q: Does the A100 have any display outputs?

A: No, the A100 SXM4 80 GB has no display outputs, while the L40 has 4x DisplayPort 1.4a.

Q: What is the difference in their process nodes?

A: The L40 is fabricated on a 5 nm process, while the A100 uses a 7 nm process.

Q: Which GPU has a higher FP32 performance?

A: The L40 has a significantly higher FP32 performance at 90.52 TFLOPS, compared to the A100's 19.49 TFLOPS.

Specification Differences

The two cards differ in nearly every measurable specification. The most obvious difference is in memory: the L40 has 48 GB of GDDR6 on a 384-bit bus, while the A100 has 80 GB of HBM2e on a 5120-bit bus. This leads to a bandwidth discrepancy, with the A100 hitting 2.04 TB/s versus the L40's 864.0 GB/s. The compute units also differ, with the L40 having 18,176 shading units and the A100 having 6,912. The L40 has 142 RT cores, while the A100 has none listed. The L40's clock speeds are much higher, with a boost of 2490 MHz versus the A100's 1410 MHz, which helps explain its FP32 advantage of 90.52 TFLOPS versus 19.49 TFLOPS.

Other key differences include the process node (5 nm vs 7 nm), transistor count (76,300 million vs 54,200 million), and die size (609 mm² vs 826 mm²). The power profiles also differ, with the L40 rated at 300 W and the A100 at 400 W. The form factors are distinct: the L40 is a dual-slot card with a 16-pin power connector, while the A100 is an OAM module with no power connectors listed. The L40 has display outputs, while the A100 has none. Finally, the L40 supports DirectX 12 Ultimate, OpenGL 4.6, and Vulkan 1.4, while the A100 has no API support listed. The release dates also differ, with the L40 launching later in 2022 compared to the A100's 2020.

The Verdict

The data suggests a clear split in purpose. For workloads that leverage graphics APIs like Vulkan, the NVIDIA L40 is the definitive choice. Its 29.2% lead in the Geekbench Vulkan test, combined with its support for modern graphics APIs and display outputs, makes it the superior option for rendering, visualization, and any compute tasks that can utilize the Vulkan API. Its higher FP32 performance and clock speeds also make it more versatile for general compute.

The NVIDIA A100 SXM4 80 GB, despite losing the only benchmark, is not without merit. Its 80 GB memory capacity and 2.04 TB/s bandwidth are unmatched by the L40. This makes it the better candidate for projects that require massive memory footprints, such as training large language models or processing massive datasets that exceed the L40's 48 GB capacity. The A100's FP16 performance of 77.97 TFLOPS (4:1) also indicates a strong focus on AI workloads. The choice comes down to what the data measures versus what it does not. The L40 wins in the measured benchmark, but the A100's specialized memory and AI features are not captured in that single test. For a general-purpose server GPU with graphics capabilities, the L40 is the winner. For a memory-capacity-focused AI accelerator, the A100's strengths, while not benchmarked here, are evident in its specifications.