AMD Radeon Instinct MI355X

The AMD Radeon Instinct MI355X is a data-center accelerator built on the CDNA 3.0 architecture, featuring the Aqua Vanjaram chip fabricated on TSMC’s 5 nm process. With 153,000 million transistors packed into a 1017 mm² die, this module targets compute-heavy workloads rather than traditional graphics output, as indicated by its zero display outputs and OAM slot width.

Benchmark Performance

The MI355X occupies the 50th percentile among all GPUs in the benchmark database, with an average benchmark score of 0. This percentile placement indicates that the card does not rank at the top of the overall GPU hierarchy, but the database contains no direct comparative scores for this accelerator. The absence of nearestRivals data means the analysis must rely on raw compute specifications to contextualize performance.

In terms of raw throughput, the MI355X delivers 78.64 TFLOPS of FP32 compute and 629.1 TFLOPS of FP16 compute, utilizing an 8:1 ratio between the two precision formats. That FP16 figure is roughly eight times the FP32 output, which is a typical pattern for accelerators designed for mixed-precision workloads. The texture rate stands at 2,457.6 GTexel/s, driven by 1,024 texture mapping units, while the pixel rate is 0 MPixel/s—consistent with a card that has no ROPs configured for rasterization output.

Memory bandwidth is a defining characteristic: the MI355X pairs 288 GB of HBM3e memory with an 8192-bit bus, achieving 8.19 TB/s of bandwidth. This bandwidth figure is exceptionally high, dwarfing what typical consumer GPUs offer, and it directly supports the large FP16 compute throughput. The effective memory clock is 2000 MHz, translating to 8 Gbps effective, which is modest per-pin but multiplied across the enormous bus width.

Benchmark results, as reflected by the percentile and average score, do not show this card competing with consumer or gaming GPUs in standard rasterization tests. Instead, the data suggests a specialized compute accelerator where memory capacity and bandwidth matter more than pixel output. The FP32 throughput of 78.64 TFLOPS positions it as a high-end compute part, but without rival scores, the relative standing remains qualitative.

How It Compares

The FACT PACK lists no nearest rivals for the MI355X, so direct percentage deltas cannot be calculated. The nearestRivals array is empty, meaning there are no named competitors with scores or deltaPct values to reference. Consequently, the comparison must be framed through the accelerator’s own specifications against the broader database of GPUs.

Against typical consumer GPUs, the MI355X lacks any display outputs and has a pixel rate of 0 MPixel/s, making it incomparable in rasterization benchmarks. The FP32 compute of 78.64 TFLOPS is far above what most desktop cards achieve, but the 1400 W TDP and 1800 W suggested PSU indicate a power envelope that excludes it from standard desktop configurations. The 288 GB memory capacity exceeds any gaming card, and the 8.19 TB/s bandwidth is similarly beyond consumer offerings.

Relative to other data-center accelerators, the MI355X’s 153,000 million transistors on a 1017 mm² die represent a high transistor density of 150.4M per mm². This density reflects the 5 nm process from TSMC, which allows more logic in the same area. The FP16 throughput of 629.1 TFLOPS suggests it is designed for AI training or inference, where half-precision math dominates. However, without rival data, the exact performance gap cannot be quantified.

The percentileVsAllGpus value of 50 indicates a median position in the database, but this is likely skewed by the inclusion of many consumer GPUs that the MI355X would not directly compete against. The average benchmark score of 0 reinforces that standard benchmark suites do not apply to this hardware, as it has no ROPs and no display outputs.

Who Should Consider It

Given the compute specifications, the MI355X is suited for environments that prioritize FP16 throughput and massive memory capacity over conventional graphics rendering. The 629.1 TFLOPS FP16 figure, combined with 288 GB of HBM3e memory at 8.19 TB/s bandwidth, makes it appropriate for large-scale AI model training or inference tasks where data fits within the memory pool.

For workloads that require FP32 precision, the 78.64 TFLOPS output is substantial, but the 8:1 FP16 ratio suggests the card is optimized for half-precision operations. Users running mixed-precision training—where FP16 accumulates into FP32—would see the most benefit from the high FP16 rate. The 8.19 TB/s bandwidth ensures that memory-bound operations, such as embedding lookups or large matrix multiplications, are not starved for data.

The 1400 W TDP and 1800 W suggested PSU imply a rack-mounted server environment with dedicated power delivery, not a workstation with a standard ATX supply. The OAM Module slot width confirms this, as OAM (Open Accelerator Module) is a server form factor. System integrators building AI clusters or high-performance computing nodes would be the target audience, especially those needing more than 100 GB of memory per accelerator.

At lower resolutions or for gaming, the MI355X is entirely unsuitable due to the 0 MPixel/s pixel rate and lack of display outputs. For scientific computing that relies on FP64, the FACT PACK does not list FP64 performance, so that use case cannot be evaluated. The data indicates a compute-only accelerator, so any recommendation must center on FP16-heavy workloads with large memory footprints.

Architecture and Design

The MI355X is built on the CDNA 3.0 architecture, which is AMD’s compute-focused design lineage, distinct from the RDNA series used in gaming cards. The chip, codenamed Aqua Vanjaram, is manufactured by TSMC on a 5 nm process node. The die measures 1017 mm², which is large by any standard, and houses 153,000 million transistors, yielding a transistor density of 150.4M per mm². This density is a direct result of the 5 nm lithography, allowing more compute units per area than older nodes.

The core configuration includes 16,384 shading units and 1,024 texture mapping units, but zero ROPs. This configuration is unusual for a GPU—ROPs handle pixel output, and their absence confirms the card does not rasterize images. The texture rate of 2,457.6 GTexel/s derives from the TMU count and clock speeds, but the pixel rate of 0 MPixel/s means no display or render target output.

Clocks are set at a base of 1000 MHz and a boost of 2400 MHz, which are moderate frequencies for such a large die. The memory clock is 2000 MHz, with 8 Gbps effective transfer rate per pin. The 8192-bit bus width is the key enabler of the 8.19 TB/s bandwidth, as each pin transfers at a relatively low rate but the aggregate width is enormous.

Power delivery is specified at 1400 W TDP, with no power connectors listed—typical for OAM modules that receive power through the socket rather than external cables. The suggested PSU is 1800 W, indicating a system-level power budget that accounts for the rest of the server. The bus interface is PCIe 5.0 x16, which provides ample host bandwidth for data transfer, though the 8.19 TB/s memory bandwidth far exceeds PCIe capabilities, meaning the accelerator relies on local memory for most operations.

Ray Tracing and Feature Set

The FACT PACK lists no ray tracing cores and no tensor cores for the MI355X. The rtCores and tensorCores fields are both null, indicating that the accelerator does not implement dedicated hardware for these features. This aligns with its CDNA 3.0 architecture, which prioritizes matrix math for compute rather than real-time graphics effects.

API support is similarly absent: the directx, opengl, and vulkan fields are all null. This means the MI355X does not expose standard graphics APIs, reinforcing its role as a compute-only device. Without Vulkan or DirectX support, no gaming or real-time rendering workloads can run on this hardware. The lack of display outputs (No outputs) further confirms that it is not designed to drive monitors.

The feature set, therefore, consists solely of the raw compute capabilities: FP32 at 78.64 TFLOPS, FP16 at 629.1 TFLOPS, and memory bandwidth at 8.19 TB/s. The absence of tensor cores does not preclude AI workloads—CDNA 3.0 can handle matrix operations through shader cores—but the 8:1 FP16 ratio suggests the card is tuned for half-precision throughput. The predecessor is listed as FirePro Data Center, which indicates a lineage of compute accelerators rather than gaming GPUs.

For ray tracing, there is no data to analyze. The card has no RT cores, no pixel rate, and no graphics API support, so ray-traced workloads are impossible. The focus remains on dense compute, such as linear algebra, neural network inference, or simulation, where memory bandwidth and FP16 throughput are the limiting factors.

FAQ

Q: What is the FP32 compute performance of the AMD Radeon Instinct MI355X?

A: The MI355X delivers 78.64 TFLOPS of FP32 compute, based on its 16,384 shading units and boost clock of 2400 MHz.

Q: How much memory does the MI355X have, and what is its bandwidth?

A: It has 288 GB of HBM3e memory on an 8192-bit bus, achieving 8.19 TB/s of memory bandwidth at an effective 8 Gbps per pin.

Q: Does the MI355X support display outputs or graphics APIs?

A: No. The card has no display outputs, and its DirectX, OpenGL, and Vulkan support are all listed as null, making it compute-only.

Q: What is the transistor count and die size of the MI355X?

A: The chip contains 153,000 million transistors on a 1017 mm² die, produced on TSMC’s 5 nm process, yielding a density of 150.4M transistors per mm².

Q: What is the power requirement for the MI355X?

A: The TDP is 1400 W, and the suggested PSU is 1800 W. The card uses an OAM Module slot width and has no power connectors, drawing power from the module socket.

Q: Does the MI355X have ray tracing or tensor cores?

A: The FACT PACK lists null for both ray tracing cores and tensor cores, indicating no dedicated hardware for either feature. The card focuses on FP32 and FP16 compute instead.

Q: How does the MI355X rank among all GPUs in the benchmark database?

A: It sits at the 50th percentile among all GPUs, with an average benchmark score of 0, reflecting that standard graphics benchmarks do not apply to this compute accelerator.