AMD Instinct MI200

The AMD Instinct MI200 is a compute-oriented accelerator built for server racks, not for desktop gaming. Based on the CDNA 2.0 architecture on a 6 nm TSMC process, this Aldebaran chip packs 58,200 million transistors into a 724 mm² die and targets high-throughput workloads like AI training and scientific simulation. Its benchmark percentile of 50 places it exactly at the median of all GPUs tracked, and its zero average benchmark score reflects that no standard gaming or synthetic graphics tests were run on this hardware. The most important takeaway for any builder or system integrator is that this card is end-of-life, has no display outputs, and supports no graphics APIs, it is purely a data-center compute part.

Who Should Consider It

The Instinct MI200 is not a product for gamers or workstation users who need to see a screen. With no display outputs and no DirectX, OpenGL, or Vulkan support, it cannot render frames for a monitor. The data shows zero pixel fill rate, which reinforces that this card never touches a framebuffer. Instead, consider this card if your workload is pure number crunching: large matrix operations, deep learning inference, or HPC simulations that can leverage its 22.63 TFLOPS of FP32 and 45.26 TFLOPS of FP16 (2:1) throughput. For resolution and settings-based recommendations, the concept does not apply, there are no game scores, no rasterization metrics, and no texture filtering results to base a gaming tier on. If you are building a server node for compute, the MI200’s 64 GB of HBM2e memory and 1.64 TB/s bandwidth make it suitable for datasets that exceed what most GPUs can hold locally, but only if your software stack is written for CDNA 2.0 and can operate without any graphics API layer.

For those who need to run a display alongside compute tasks, this card fails immediately. The lack of any video output means you will need a separate GPU for display, which adds cost and complexity to a server build. The 300 W TDP and 700 W suggested PSU are modest for a compute accelerator, but the OAM Module slot width means it does not fit standard PCIe slots in most consumer cases. The bus interface is PCIe 4.0 x16, so it will connect to a motherboard, but the physical form factor is a major obstacle. In short, consider the MI200 only if you are building a dedicated compute node with no need for graphics, and you have already verified that your workload can utilize its specific architecture.

Ray Tracing and Feature Set

There are no ray tracing cores, no tensor cores, and no RT acceleration hardware listed in the specifications. The architecture is CDNA 2.0, which is designed for compute rather than graphics, so the absence of RT units is expected. The API support is entirely absent: DirectX is N/A, OpenGL is N/A, and Vulkan is N/A. This means no consumer gaming applications, no professional graphics software, and no Vulkan-based compute frameworks will run on this card. The feature set is strictly compute-oriented: 6,656 shading units, 416 texture mapping units, and zero ROPs. The texture rate is 707.2 GTexel/s, but with no pixel output, that texture rate is only useful for compute shaders that process textures in memory, not for rendering to a screen. The memory subsystem supports 64 GB of HBM2e at 1600 MHz with 3.2 Gbps effective speed, which is the primary feature that distinguishes this card, massive capacity and bandwidth for data-heavy workloads.

For developers, the lack of tensor cores means that AI acceleration relies on the FP16 and FP32 compute units rather than dedicated matrix multiplication hardware. The FP16 performance of 45.26 TFLOPS (2:1) is double the FP32 rate, indicating that mixed-precision training is a strong point, but it is not a dedicated tensor path. The card supports no hardware ray tracing, so any ray-marching or path-tracing algorithm must run as general compute code, which will be slower than on dedicated RT hardware. The production status is end-of-life, meaning no further software optimizations or driver updates are likely from AMD. For any modern workload, this card is a legacy part that requires a very specific software environment.

Benchmark Performance

The benchmark data is sparse: the average benchmark score is zero, and the percentile versus all GPUs is exactly 50. This percentile is not a performance score but a statistical position, half of all tracked GPUs rank above it and half below, but with no actual benchmark results, the percentile is likely a placeholder rather than a measured outcome. The nearest rivals list is empty, so there are no direct comparison scores or delta percentages to analyze. This absence of data is itself informative: the MI200 was never subjected to the standard benchmark suites used for consumer or workstation GPUs, confirming that it is outside the normal testing ecosystem. The FP32 compute rate of 22.63 TFLOPS is a theoretical peak, not a measured benchmark, and it should be interpreted as the maximum achievable throughput under ideal conditions. In practice, real workloads will see lower utilization due to memory access patterns, kernel launch overhead, and software optimization levels.

Without rival scores, the only internal comparison is between FP32 and FP16. The FP16 rate of 45.26 TFLOPS is exactly 2:1 over FP32, which matches the specification for CDNA 2.0’s packed math capability. This means that for FP16 workloads, the card can process twice as many operations per second as for FP32, assuming the software uses packed instructions. For any workload that requires FP64 or integer operations, those rates are not listed, so no conclusions can be drawn. The texture rate of 707.2 GTexel/s is high, but with zero ROPs, it does not translate to any fill-rate advantage for graphics. The benchmark performance of this card is effectively unmeasurable by standard tools, so any purchase decision must rely on the theoretical compute specs and the memory bandwidth, not on third-party test scores.

How It Compares

There are no nearest rivals listed in the data. The nearestRivals array is empty, meaning no competitor names, scores, or delta percentages are available for comparison. This is unusual for a benchmark database, but it reflects the card’s niche status. The only predecessor mentioned is the Radeon Instinct, but no specifications or performance numbers for that product are provided. The successor field is empty, so there is no direct lineage to compare against. Without rival data, any positional analysis is impossible. The percentile of 50 is the only comparative metric, but it lacks context because no other GPUs are listed in relation to it. For a builder, this means you cannot use this database to gauge how the MI200 stacks up against NVIDIA’s compute accelerators or newer AMD Instinct models. The only factual comparison is internal: it has more memory (64 GB) than most consumer GPUs, but that is not a benchmark score.

Given the lack of rivals, the practical advice is to treat the MI200 as a standalone compute part. If your software is written for CDNA 2.0 and requires 64 GB of HBM2e, then this card is the only one in the data with those exact specs. If you need higher FP16 throughput, you might look elsewhere, but no alternative is listed. The end-of-life status suggests that newer parts exist, but they are not named, and no specifications are provided. The absence of comparison data should be a red flag: this is a legacy product with no support ecosystem, and any new deployment should be carefully validated against the specific software requirements.

Power and Cooling

The thermal design power is 300 W, which is a fixed specification and not a measured consumption figure. The suggested power supply is 700 W, meaning AMD recommends that the entire system have at least that capacity, not that the card itself draws 700 W. The power connector is a single 8-pin, which is unusual for a 300 W card, typically, a 300 W part would use an 8-pin and a 6-pin or a 12-pin connector, but the data lists only one 8-pin. This suggests that the card may be designed for server power delivery through the OAM Module slot, with the 8-pin as a supplemental connection. The slot width is OAM Module, which is not a standard PCIe slot width; it is a mezzanine form factor used in accelerators. This means you cannot install this card in a typical desktop case without an adapter or a specialized server chassis. The cooling solution is not specified, no heatsink type, fan count, or liquid cooling details are given. You must plan for adequate airflow in a server environment, as the 300 W TDP will generate significant heat, but without a cooler specification, you cannot assume a reference design exists.

For a power supply, the 700 W suggestion is a system-level recommendation, not a card-level requirement. If you are building a dual-GPU server, you would need to scale the PSU accordingly, but the data does not provide multi-card power guidance. The single 8-pin connector is the only power input listed, so ensure your PSU has an available 8-pin CPU or PCIe power cable. The lack of display outputs means no power is drawn for video signal generation, which is negligible anyway. The end-of-life status means that power efficiency is not a selling point; a modern card at the same TDP would likely offer higher performance per watt, but that comparison is not in the data. For cooling, the OAM form factor often uses passive heatsinks with server fans, but without a cooler spec, you must design your own thermal solution. The 6 nm process and 724 mm² die size suggest a dense chip, so effective cooling is critical for sustained compute loads.

FAQ

Q: Can I use this card for gaming?

A: No. The card has no display outputs, supports no DirectX, OpenGL, or Vulkan, and has a zero pixel fill rate. It cannot render frames for a monitor.

Q: What is the maximum memory bandwidth?

A: The memory bandwidth is 1.64 TB/s, using 64 GB of HBM2e across a 4096-bit bus at 1600 MHz with 3.2 Gbps effective speed.

Q: Does this card support ray tracing?

A: No. There are no ray tracing cores listed, and the API support for DirectX, OpenGL, and Vulkan is N/A, so no hardware-accelerated ray tracing is available.

Q: What power supply do I need?

A: The suggested PSU is 700 W, and the card uses a single 8-pin power connector. The TDP is 300 W, but the PSU recommendation accounts for the rest of the system.

Q: Is this card still in production?

A: No. The production status is end-of-life, and the release date is November 7, 2021. No successor is listed.

Q: What is the FP16 performance?

A: The FP16 performance is 45.26 TFLOPS, which is a 2:1 ratio over the FP32 rate of 22.63 TFLOPS.

Memory Subsystem

The memory subsystem is the defining feature of the Instinct MI200. It has 64 GB of HBM2e memory, which is a very large capacity for a single GPU, most consumer cards offer 8 to 24 GB. The bus width is 4096 bits, which is four times wider than typical 1024-bit HBM setups, and the memory runs at 1600 MHz with a 3.2 Gbps effective data rate. This combination yields a bandwidth of 1.64 TB/s, which is exceptionally high. For high-resolution compute workloads, this bandwidth matters more than raw compute: large matrix multiplications, neural network training with big batches, and scientific simulations that stream data through memory will all benefit from the ability to move 1.64 TB per second. The 64 GB capacity means you can hold large datasets in GPU memory without resorting to host memory transfers, which are typically over PCIe and much slower. The PCIe 4.0 x16 interface is the only external connection, but for workloads that fit in the 64 GB, the card can operate independently of the host after initial data loading.

The memory type is HBM2e, which is stacked memory that sits on the same package as the GPU die, reducing trace lengths and power consumption compared to GDDR6. The 4096-bit bus width is the widest in the data, and it is what enables the 1.64 TB/s bandwidth. For comparison, a 256-bit GDDR6 bus at 16 Gbps would offer around 512 GB/s, so the MI200’s bandwidth is over three times that, though that comparison is not in the FACT PACK. The zero ROPs and pixel rate of 0 MPixel/s mean that the memory is never used for framebuffer access, so all bandwidth is available for compute reads and writes. In practice, the memory subsystem is the primary reason to choose this card: if your workload requires more than 32 GB of GPU memory or needs to move more than 1 TB/s of data, the MI200 is a strong fit. However, with no display outputs, the memory cannot be used for texture rendering or video output, so its utility is strictly for compute kernels. The end-of-life status means that the memory is not the latest generation, HBM3 or newer exists but is not in the pack, but the 1.64 TB/s bandwidth is still competitive for server workloads that rely on memory-bound operations.