AMD Instinct MI100

AMD Instinct MI100 is a data-center accelerator built on the CDNA 1.0 architecture, using the Arcturus chip manufactured on TSMC's 7 nm process. Its primary benchmark result is a Geekbench OpenCL score of 139,035, placing it in the 96th percentile of all GPUs, with a single average benchmark score of 139,035.

Benchmark Performance

The AMD Instinct MI100 delivers a Geekbench OpenCL score of 139,035, which positions it in the 96th percentile of all GPUs in the database. This is a high-ranking score, indicating that the accelerator sits comfortably among the top tier of computing hardware, though it does not claim the absolute top spot. The score itself is the sole benchmark data point, so analysis relies on its relationship to the nearest rivals in the database.

Comparing to the closest competitors, the MI100 leads the NVIDIA Tesla V100 PCIe 16 GB by a margin of 0.7%, as indicated by the deltaPct value. This is a narrow lead, suggesting that in raw OpenCL compute performance, the two cards are essentially peers, with the AMD part holding a slight edge. Against the NVIDIA Tesla V100 SXM2 32 GB, the advantage grows marginally to 0.9%, again showing a very tight competition where the MI100's performance is nearly identical to that of the higher-memory V100 variant.

The gap widens somewhat when compared to AMD's own Radeon PRO V620, where the MI100 is 1.9% ahead. This is still a modest difference, but it indicates that the MI100 outperforms this workstation card in the OpenCL workload. The largest delta among the listed rivals is against the AMD Radeon Pro W6800X Duo, where the MI100 leads by 2.4%. While this is the biggest percentage advantage in the group, it remains a relatively small performance gap, meaning that the MI100 does not dramatically outclass its nearest rivals in this specific test.

Benchmark results indicate that the MI100's performance is consistent and competitive. The percentile ranking of 96% underscores that it outperforms the vast majority of GPUs in the database, but the narrow deltas against its nearest rivals suggest that the performance tier it occupies is crowded. The data does not show a runaway victory; instead, it shows a card that is marginally faster than each of its four closest competitors, with the deltas ranging from 0.7% to 2.4%. This implies that for users comparing these specific accelerators, the MI100 offers a slight, measurable performance advantage in OpenCL, but not a transformative one.

Who Should Consider It

Given its compute-oriented design and lack of display outputs, the AMD Instinct MI100 is not intended for traditional gaming or consumer desktop use. The data shows a card with no display outputs, meaning it cannot connect to monitors directly. Instead, this accelerator is suited for server environments or compute clusters where rendering to a screen is unnecessary.

The performance profile, anchored by the 139,035 OpenCL score, suggests that it is best deployed in workloads that leverage raw compute throughput. The FP32 performance of 23.07 TFLOPS and FP16 performance of 46.14 TFLOPS (with a 2:1 ratio) indicate that the card is designed for high-precision floating-point calculations, making it relevant for scientific simulation, machine learning training, or data center applications. The 96th percentile ranking further supports that this is a high-end compute part, not a mid-range option.

For resolution and settings-based recommendations, the data does not provide gaming frame rates or traditional graphics benchmarks. However, the FP16 performance is notably higher than FP32, suggesting that workloads which can utilize half-precision arithmetic, common in AI inference and certain HPC tasks, will see a performance benefit. The 32 GB of HBM2 memory and 1.23 TB/s bandwidth also make it suitable for large datasets that exceed the memory capacity of typical graphics cards, such as big model training or large-scale data analysis. Users with workloads that fit these characteristics, large memory footprints and heavy compute, are the primary candidates. Those seeking a card for rasterized gaming or workstation graphics should look elsewhere, as the MI100's architecture and feature set are clearly geared toward computation rather than display output.

Power and Cooling

The AMD Instinct MI100 has a thermal design power (TDP) of 300 W, which is a substantial power requirement. The data specifies a suggested power supply unit (PSU) of 700 W, which is the recommended system power capacity to support the card under load. Power is delivered via two 8-pin connectors, a standard high-power interface for accelerators. The card occupies a dual-slot form factor, meaning it will take up two expansion slots in a chassis, and its physical dimensions are a length of 267 mm (10.5 inches) and a height of 111 mm (4.4 inches). These dimensions and slot width are important for server chassis compatibility, as space is often limited. There is no mention of a specific cooler type in the data, only the dual-slot design, which implies an active cooling solution sufficient for the 300 W TDP. The absence of display outputs reinforces that this is a compute card designed for rack-mounted systems with adequate airflow, not a consumer desktop with a side panel window.

FAQ

Q: What is the Geekbench OpenCL score of the AMD Instinct MI100?

A: The AMD Instinct MI100 scores 139,035 in the Geekbench OpenCL benchmark.

Q: How does the MI100 compare to the NVIDIA Tesla V100 PCIe 16 GB?

A: The MI100 is 0.7% faster than the NVIDIA Tesla V100 PCIe 16 GB in average benchmark score.

Q: What is the memory configuration of the MI100?

A: The card has 32 GB of HBM2 memory with a 4096-bit bus width and a bandwidth of 1.23 TB/s.

Q: What power supply is recommended for the MI100?

A: A 700 W power supply is suggested, and the card requires two 8-pin power connectors.

Q: Does the MI100 have any display outputs?

A: No, the data lists display outputs as "No outputs," indicating it is a compute-only accelerator.

Q: What is the FP16 performance of the MI100?

A: The FP16 performance is 46.14 TFLOPS, which is double the FP32 rate of 23.07 TFLOPS due to a 2:1 ratio.

Ray Tracing and Feature Set

The data for the AMD Instinct MI100 lists no ray tracing cores and no tensor cores, and the API support is marked as "N/A" for DirectX, OpenGL, and Vulkan. This indicates that the card does not include dedicated hardware for ray tracing or AI tensor operations, and it lacks traditional graphics API support. The feature set is therefore focused on compute, as evidenced by the CDNA 1.0 architecture, which is designed specifically for data center compute workloads rather than graphics rendering. The absence of these features means that the MI100 is not suitable for applications that rely on ray-traced graphics or tensor-core-accelerated AI, such as real-time ray tracing in games or certain deep learning frameworks that depend on tensor cores. Instead, the card's capabilities are rooted in its raw FP32 and FP16 compute throughput, which can still be leveraged for AI workloads via general-purpose compute, but without the specialized acceleration that tensor cores would provide. The lack of API support further confirms that this is not a graphics card; it is a compute accelerator meant to be accessed via programming models like OpenCL, which is the benchmark used in the data.

Memory Subsystem

The memory subsystem of the AMD Instinct MI100 is a significant component of its design. It features 32 GB of HBM2 memory, which is a high-bandwidth memory type typically used in server accelerators. The bus width is 4096 bits, which is exceptionally wide, and this contributes to a memory bandwidth of 1.23 TB/s. This bandwidth figure is among the highest available, and it is crucial for compute workloads that require large amounts of data to be fed to the processing units quickly. The memory clock is listed as 1200 MHz with a 2.4 Gbps effective rate, which, combined with the wide bus, yields the 1.23 TB/s throughput.

For high-resolution or large-dataset scenarios, this memory configuration is advantageous. The 32 GB capacity allows for holding large models or datasets entirely in memory, reducing the need for slower system memory transfers. The 1.23 TB/s bandwidth ensures that the compute units are not starved of data, which is often a bottleneck in memory-intensive tasks. In the context of its benchmark score, the memory subsystem supports the high OpenCL score by enabling efficient data movement. The 4096-bit bus width is particularly notable, as it is much wider than typical consumer graphics cards, reflecting the card's data center pedigree where memory bandwidth is a critical performance factor.

How It Compares

Against the NVIDIA Tesla V100 PCIe 16 GB, the MI100 holds a 0.7% performance advantage in the average benchmark score. This is a minimal lead, indicating that the two accelerators are effectively tied in raw compute performance, with the MI100 edging ahead by a small margin. The V100 is a well-known data center card, and the MI100's slight edge suggests that it is at least on par with its NVIDIA counterpart in OpenCL workloads.

Comparing to the NVIDIA Tesla V100 SXM2 32 GB, the MI100 is 0.9% faster. The SXM2 variant typically has higher memory bandwidth than the PCIe version, but the benchmark data shows that the MI100 still comes out ahead, albeit by a similarly narrow margin. This reinforces that the MI100's performance is competitive with the high-end V100 offerings.

The AMD Radeon PRO V620 is next, with the MI100 leading by 1.9%. This is a larger delta than against the V100 parts, but still a modest gap. The PRO V620 is a workstation card, and the MI100's advantage shows that the compute-focused architecture delivers better raw OpenCL performance than a card designed for professional graphics.

Finally, the MI100 is 2.4% ahead of the AMD Radeon Pro W6800X Duo. This is the largest delta among the nearest rivals, but it remains a relatively small difference. The W6800X Duo is a dual-GPU card, yet the MI100 still outperforms it in this benchmark, highlighting the efficiency of the single-chip MI100 design. Overall, the MI100's position is clear: it is the top performer among these four rivals, but the margins are thin, making it a marginal winner rather than a dominant one.

Architecture and Design

The AMD Instinct MI100 is built on the CDNA 1.0 architecture, which is a dedicated compute architecture from AMD, distinct from their graphics-oriented RDNA line. The chip is codenamed Arcturus, and it is manufactured by TSMC on a 7 nm process node. The die size is 750 mm², and it contains 25,600 million transistors, resulting in a transistor density of 34.1 million per square millimeter. This is a large, complex chip designed for maximum compute throughput.

The core configuration includes 7,680 shading units, 480 texture mapping units (TMUs), and 64 raster operation units (ROPs). The shading units are the primary compute engines, and their high count contributes to the card's FP32 and FP16 performance. The texture rate is 721.0 GTexel/s, and the pixel rate is 96.13 GPixel/s. The clock speeds are a base of 1000 MHz and a boost of 1502 MHz. The architecture does not include ray tracing or tensor cores, aligning with its compute-only focus.

The card uses a PCIe 4.0 x16 bus interface, which is standard for high-performance accelerators. The production status is end-of-life, with a release date of November 15, 2020, and it is the successor to the Radeon Instinct line. The MI100 is a dual-slot card, measuring 267 mm in length and 111 mm in height. The combination of the 7 nm process, large die, and high transistor count indicates a design that prioritizes raw compute power, with the 300 W TDP reflecting the energy required to drive such a large chip. The lack of a launch MSRP in the data means no pricing information is available.