AMD Radeon Instinct MI100
AMD graphics card specifications and benchmark scores
At a Glance
AMDAMD Radeon Instinct MI100 Specifications
Radeon Instinct MI100 GPU Core
Shader units and compute resources
The AMD Radeon Instinct MI100 GPU core specifications define its raw processing power for graphics and compute workloads. Shading units (also called CUDA cores, stream processors, or execution units depending on manufacturer) handle the parallel calculations required for rendering. TMUs (Texture Mapping Units) process texture data, while ROPs (Render Output Units) handle final pixel output. Higher shader counts generally translate to better GPU benchmark performance, especially in demanding games and 3D applications.
Instinct MI100 Clock Speeds
GPU and memory frequencies
Clock speeds directly impact the Radeon Instinct MI100's performance in GPU benchmarks and real-world gaming. The base clock represents the minimum guaranteed frequency, while the boost clock indicates peak performance under optimal thermal conditions. Memory clock speed affects texture loading and frame buffer operations. The Radeon Instinct MI100 by AMD dynamically adjusts frequencies based on workload, temperature, and power limits to maximize performance while maintaining stability.
AMD's Radeon Instinct MI100 Memory
VRAM capacity and bandwidth
VRAM (Video RAM) is dedicated memory for storing textures, frame buffers, and shader data. The Radeon Instinct MI100's memory capacity determines how well it handles high-resolution textures and multiple displays. Memory bandwidth, measured in GB/s, affects how quickly data moves between the GPU and VRAM. Higher bandwidth improves performance in memory-intensive scenarios like 4K gaming. The memory bus width and type (GDDR6, GDDR6X, HBM) significantly influence overall GPU benchmark scores.
Radeon Instinct MI100 by AMD Cache
On-chip cache hierarchy
On-chip cache provides ultra-fast data access for the Instinct MI100, reducing the need to fetch data from slower VRAM. L1 and L2 caches store frequently accessed data close to the compute units. AMD's Infinity Cache (L3) dramatically increases effective bandwidth, improving GPU benchmark performance without requiring wider memory buses. Larger cache sizes help maintain high frame rates in memory-bound scenarios and reduce power consumption by minimizing VRAM accesses.
Instinct MI100 Theoretical Performance
Compute and fill rates
Theoretical performance metrics provide a baseline for comparing the AMD Radeon Instinct MI100 against other graphics cards. FP32 (single-precision) performance, measured in TFLOPS, indicates compute capability for gaming and general GPU workloads. FP64 (double-precision) matters for scientific computing. Pixel and texture fill rates determine how quickly the GPU can render complex scenes. While real-world GPU benchmark results depend on many factors, these specifications help predict relative performance levels.
CDNA 1.0 Architecture & Process
Manufacturing and design details
The AMD Radeon Instinct MI100 is built on AMD's CDNA 1.0 architecture, which defines how the GPU processes graphics and compute workloads. The manufacturing process node affects power efficiency, thermal characteristics, and maximum clock speeds. Smaller process nodes pack more transistors into the same die area, enabling higher performance per watt. Understanding the architecture helps predict how the Instinct MI100 will perform in GPU benchmarks compared to previous generations.
AMD's Radeon Instinct MI100 Power & Thermal
TDP and power requirements
Power specifications for the AMD Radeon Instinct MI100 determine PSU requirements and thermal management needs. TDP (Thermal Design Power) indicates the heat output under typical loads, guiding cooler selection. Power connector requirements ensure adequate power delivery for stable operation during demanding GPU benchmarks. The suggested PSU wattage accounts for the entire system, not just the graphics card. Efficient power delivery enables the Radeon Instinct MI100 to maintain boost clocks without throttling.
Radeon Instinct MI100 by AMD Physical & Connectivity
Dimensions and outputs
Physical dimensions of the AMD Radeon Instinct MI100 are critical for case compatibility. Card length, height, and slot width determine whether it fits in your chassis. The PCIe interface version affects bandwidth for communication with the CPU. Display outputs define monitor connectivity options, with modern cards supporting multiple high-resolution displays simultaneously. Verify these specifications against your case and motherboard before purchasing to ensure a proper fit.
AMD API Support
Graphics and compute APIs
API support determines which games and applications can fully utilize the AMD Radeon Instinct MI100. DirectX 12 Ultimate enables advanced features like ray tracing and variable rate shading. Vulkan provides cross-platform graphics capabilities with low-level hardware access. OpenGL remains important for professional applications and older games. CUDA (NVIDIA) and OpenCL enable GPU compute for video editing, 3D rendering, and scientific applications. Higher API versions unlock newer graphical features in GPU benchmarks and games.
Radeon Instinct MI100 Product Information
Release and pricing details
The AMD Radeon Instinct MI100 is manufactured by AMD as part of their graphics card lineup. Release date and launch pricing provide context for comparing GPU benchmark results with competing products from the same era. Understanding the product lifecycle helps evaluate whether the Radeon Instinct MI100 by AMD represents good value at current market prices. Predecessor and successor information aids in tracking generational improvements and planning future upgrades.
Radeon Instinct MI100 Benchmark Scores
No benchmark data available for this GPU.
About AMD Radeon Instinct MI100
AMD Radeon Instinct MI100 is a data-center accelerator built on the CDNA 1.0 architecture, fabricated on TSMC's 7 nm process. With a 50th percentile standing among all GPUs, it occupies a precise midpoint in the performance spectrum, a position that becomes more meaningful when examining its raw computational metrics rather than any single benchmark score.
Benchmark Performance
The MI100's compute potential is defined by its FP32 throughput of 23.07 TFLOPS and a markedly higher FP16 rate of 184.6 TFLOPS, achieved via an 8:1 ratio. This 8:1 ratio is a telling architectural choice, signaling that the card is engineered for workloads where reduced precision is acceptable and throughput is paramount. The FP16 figure is not merely a doubling but an eightfold increase, which strongly suggests a design optimized for machine learning inference and training scenarios that leverage mixed-precision arithmetic.
In the context of the full GPU landscape, the 50th percentile ranking places the MI100 exactly at the median, meaning half of all tracked GPUs deliver lower raw performance and half deliver higher. This is a neutral position, but the nature of the workload matters. The data shows a card that is a specialist, not a generalist. Its FP32 output of 23.07 TFLOPS is substantial for compute tasks, yet the FP16 capability is the headline number. The card's pixel rate of 96.13 GPixel/s and texture rate of 721.0 GTexel/s are derived from its 64 ROPs and 480 TMUs, respectively, and these figures are adequate for data visualization but not the primary selling point. The absence of any benchmark scores in the fact pack means that relative performance against rivals cannot be quantified in terms of percentage deltas; instead, the analysis relies on these absolute throughput metrics and the percentile rank.
The lack of nearestRivals data in the fact pack is itself a data point. It suggests that at the time of analysis, no direct competitor was close enough in this specific metric mix to warrant a comparison entry. This does not mean the MI100 exists in a vacuum, but rather that its performance profile — high FP16, modest FP32, and specific memory characteristics — may not align neatly with any single rival's scoring pattern. The 50th percentile is an aggregate measure, and the MI100's true standing is likely higher in FP16-focused tasks and lower in traditional graphics workloads.
Memory Subsystem
The MI100 is equipped with 32 GB of HBM2 memory, connected via a 4096-bit bus. This configuration yields a bandwidth of 1.23 TB/s, a figure that is critical for data-center workloads where memory access patterns are often large and contiguous. The 4096-bit bus is exceptionally wide, which is a direct consequence of using HBM2, and this width is the primary driver of the 1.23 TB/s bandwidth. For high-resolution inference or training on large models, this bandwidth is the lifeline; the compute units cannot work faster than the memory can feed them.
The effective memory clock is listed as 2.4 Gbps, which, when multiplied by the bus width and divided by 8 (to convert bits to bytes), yields the stated 1.23 TB/s. This is a straightforward calculation, but the implications are significant. A 32 GB capacity is substantial, allowing for large batch sizes or high-resolution input data to reside entirely on the card without spilling to host memory. The HBM2 type is also relevant; it is a stacked memory solution that offers lower power consumption per bit transferred compared to GDDR6, which is beneficial for a 300 W TDP envelope.
The combination of 32 GB and 1.23 TB/s is a sweet spot for many scientific computing and AI tasks. The capacity prevents out-of-memory errors, while the bandwidth ensures that the 7680 shading units are kept busy. There is no ambiguity here: this is a memory subsystem built for throughput, not latency. The 4096-bit bus is a physical testament to the card's data-centric design, and the 1.23 TB/s bandwidth is among the highest available for any accelerator, ensuring that high-resolution data sets are not a bottleneck.
How It Compares
Given the absence of nearestRivals entries in the fact pack, a direct comparative analysis against named competitors is not possible from the provided data. The percentile rank of 50 against all GPUs offers a general reference point, but it is an aggregate of all GPUs, including gaming cards, professional graphics, and other accelerators. What can be stated is that the MI100's performance is exactly at the median of this broad population. This means that in a mixed workload environment, it would perform better than half of all devices and worse than the other half.
The lack of rival data is not a flaw in the card but a reflection of the fact pack's scope. The MI100 is an end-of-life product, and its successor has been released, which may explain why no current rivals are listed. The predecessor is noted as FirePro Data Center, and the MI100 represents a significant architectural shift to CDNA, which is dedicated to compute rather than graphics. This positions it differently from a hypothetical rival that might be based on a graphics-first architecture. The data suggests that the MI100 is a purpose-built compute engine, and any comparison would need to focus on FP16 throughput and memory bandwidth, where it excels, rather than on rasterization or ray tracing, where it does not have dedicated hardware.
FAQ
Q: What is the peak FP32 performance of the MI100?
A: The peak FP32 performance is 23.07 TFLOPS, which represents the card's throughput for standard single-precision compute tasks.
Q: How much memory bandwidth does the card provide?
A: The 4096-bit HBM2 bus delivers 1.23 TB/s of bandwidth, which is derived from a 1200 MHz memory clock running at 2.4 Gbps effective.
Q: What is the FP16 performance ratio?
A: The FP16 performance is 184.6 TFLOPS, which is exactly 8 times the FP32 figure, indicating an 8:1 ratio for mixed-precision workloads.
Q: What is the card's physical size?
A: The MI100 is 267 mm in length (10.5 inches) and 111 mm in height (4.4 inches), making it a dual-slot card.
Q: Does the card have any display outputs?
A: No, the MI100 has no display outputs, confirming its role as a compute-only accelerator rather than a graphics card.
Q: What is the production status?
A: The production status is End-of-life, with a release date of November 15, 2020, and a predecessor of FirePro Data Center.
Ray Tracing and Feature Set
The MI100 has no dedicated ray tracing cores and no tensor cores listed in its specifications. This is a deliberate omission, as the CDNA 1.0 architecture is optimized for matrix math and compute throughput, not for the real-time graphics features found in consumer GPUs. The absence of these cores means the card is not designed for ray-traced rendering or for workloads that rely on Tensor Core-specific instructions, such as certain deep learning operations that use proprietary formats.
The API support is entirely absent from the fact pack, with null values for DirectX, OpenGL, and Vulkan. This is consistent with a compute card that does not interface with traditional graphics APIs. The lack of display outputs further reinforces this; the card is not meant to drive a monitor. The feature set is therefore focused on raw compute, with the 7680 shading units serving as the primary execution units. These units handle FP32 and FP16 operations, and the 8:1 FP16 ratio suggests a hardware path for rapid half-precision execution, which is a key feature for AI training. The PCIe 4.0 x16 interface is present, providing a modern connection for data transfer to and from the host system. The card's capabilities are thus defined by its compute units and memory subsystem, with no auxiliary features for graphics or specialized AI acceleration beyond the standard FP16 path.
Power and Cooling
The MI100 has a TDP of 300 W, which is a high figure but not extreme for a data-center accelerator. The card requires two 8-pin power connectors, which is a standard configuration for this power class. The suggested PSU is 700 W, which provides a reasonable headroom above the card's own consumption, accounting for the rest of the system's components. The dual-slot design indicates a substantial cooler, likely a passive heatsink or a blower-style fan, given the lack of display outputs and the data-center form factor.
The 300 W TDP is a thermal limit that the cooling solution must manage. The card's length of 267 mm and height of 111 mm are typical for a dual-slot PCIe card, and the power connectors are positioned on the top edge. The absence of a specific cooler description in the fact pack means the analysis is limited to the TDP and the slot width. The 700 W PSU recommendation is a clear guideline for system builders, ensuring that the entire platform has sufficient power delivery. The power draw is a direct consequence of the 25,600 million transistors on a 750 mm² die, and the 7 nm process helps keep this within the 300 W envelope. The cooling solution must dissipate this heat in a rack-mounted environment, which typically relies on high static pressure fans to push air through the chassis.
Who Should Consider It
The MI100 is a compute accelerator for environments where FP32 and FP16 throughput are the primary metrics. The 23.07 TFLOPS FP32 performance is suitable for scientific simulations, while the 184.6 TFLOPS FP16 performance is geared towards machine learning training and inference. The 32 GB of HBM2 memory with 1.23 TB/s bandwidth makes it viable for large models that need to reside in high-speed memory. The card is not for gaming or desktop graphics, as it has no display outputs and no graphics API support.
Given the 50th percentile standing, it is not a top-tier performer in all possible workloads, but it is a balanced compute device. For users running mixed-precision workloads where FP16 is acceptable, the 8:1 ratio provides a significant advantage. For those needing to process large datasets at high resolution, the memory subsystem is a key asset. The card is end-of-life, which means it is not a current-generation purchase, but its specifications remain relevant for legacy systems or specific compute tasks. The absence of ray tracing and tensor cores means it is not suitable for tasks that require those specific hardware paths. The data suggests it is a specialist tool, best suited for HPC clusters and AI research where raw FP16 throughput and memory bandwidth are the deciding factors. The 300 W TDP and 700 W PSU requirement are manageable in a server context. Ultimately, the MI100 is for users who know they need a compute card with high FP16 output and a wide memory bus, and who are operating within the constraints of a 300 W power budget.
The NVIDIA Equivalent of Radeon Instinct MI100
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