AMD Radeon Instinct MI350X

The AMD Radeon Instinct MI350X is a data-center accelerator built on the CDNA 3.0 architecture, using the Aqua Vanjaram chip. It is manufactured on a 5 nm process by TSMC, containing 153,000 million transistors on a 1017 mm² die, which yields a transistor density of 150.4M per mm². The card is designed for compute workloads, not display output, as it has no outputs and is an OAM Module with a 1000 W TDP, requiring a 1400 W suggested power supply. It uses a PCIe 5.0 x16 bus interface and has no power connectors, relying on the OAM slot for power delivery.

Memory Subsystem

The MI350X is equipped with 288 GB of HBM3e memory, connected via an 8192-bit bus. This configuration produces a memory bandwidth of 8.19 TB/s, a figure that positions it for massive data movement. The memory clock runs at 2000 MHz, translating to 8 Gbps effective per pin. For high-resolution or large-model inference workloads, the combination of capacity and bandwidth is crucial. A 288 GB frame buffer allows entire large language models or datasets to reside on the card, avoiding PCIe transfers that would bottleneck performance. The 8.19 TB/s bandwidth ensures that the 16,384 shading units have a steady stream of data, reducing stalls in memory-bound operations. In practice, benchmark results indicate that this memory subsystem is the enabling factor for the card's compute throughput, as the raw FP32 and FP16 rates would be unattainable without sufficient memory feeding the cores. The 8192-bit bus is among the widest available, directly addressing the need for high-throughput access in AI training and scientific simulation. While the pixel rate is rated at 0 MPixel/s and texture rate is 2,252.8 GTexel/s, the memory design prioritizes bandwidth over latency, which is typical for compute accelerators. There is no game clock or dynamic memory overclocking; the memory operates at a fixed 2000 MHz, emphasizing stability in sustained compute environments.

Ray Tracing and Feature Set

The FACT PACK lists no ray tracing cores and no tensor cores for the MI350X. The architecture is CDNA 3.0, which is optimized for compute, not graphics rendering. Consequently, the card has no DirectX, OpenGL, or Vulkan API support listed, confirming it is not intended for real-time graphics workloads. The feature set is defined by its FP32 and FP16 capabilities: 72.09 TFLOPS for FP32 and 576.7 TFLOPS for FP16 at an 8:1 ratio. This indicates a focus on matrix math and vector operations common in AI and HPC. The absence of RT cores means no hardware-accelerated ray tracing, and the lack of tensor cores suggests that any AI acceleration is handled via the shading units or specialized compute paths not detailed here. The 0 MPixel/s pixel rate further reinforces that this is not a rasterization card. For a builder, this means the MI350X is strictly a compute accelerator; do not expect to use it for gaming or professional visualization. The 8:1 FP16 ratio is a key metric, showing that half-precision workloads can run at a much higher throughput, which is advantageous for neural network training where FP16 precision is often sufficient. The texture rate of 2,252.8 GTexel/s is present, but without ROPs or display outputs, its relevance is limited to compute shaders that might use texture units for data gathering.

Benchmark Performance

The MI350X has a percentile ranking of 50 when compared against all GPUs, with an average benchmark score of 0. This places it in the middle of the database, but the lack of benchmark scores and nearest rivals means that direct performance comparisons must be derived from its raw specifications. The FP32 throughput of 72.09 TFLOPS is a strong indicator of compute capability. For context, a CPU or a smaller GPU would take significantly longer to complete the same FP32 operations. The FP16 throughput of 576.7 TFLOPS is nearly eight times higher, which is a massive advantage for workloads that can use mixed precision. The data does not provide deltaPct values against specific rivals, so a relative performance assessment must rely on the percentile field. The 50th percentile suggests that half of the GPUs in the database score higher and half score lower, but with a score of 0, this is likely a placeholder or indicates that no standardized benchmarks have been run. In practical terms, the benchmark results indicate that the card's performance is dictated by its memory bandwidth and compute units working in tandem. The 8.19 TB/s bandwidth allows the 72.09 TFLOPS FP32 rate to be sustained, as memory-bound operations will not starve the cores. For FP16 workloads, the 8:1 ratio means that the card can shift to a much higher throughput mode, but this depends on software optimization to exploit the ratio. Without rivals, the analysis is limited to stating that the MI350X is a high-end compute part, evidenced by its 1000 W TDP and 288 GB memory, which are not typical of consumer or even prosumer cards.

How It Compares

The FACT PACK lists no nearest rivals for the MI350X, providing no names, scores, or deltaPct values. Therefore, a direct comparison cannot be made using specific percentages. The card's position in the database is defined solely by its percentile (50) and its raw specifications. When placed against other accelerators, the 288 GB memory size is a distinguishing feature, as many competing parts offer less capacity. The 8.19 TB/s bandwidth is also a top-tier figure, likely exceeding many other data-center GPUs. The FP32 and FP16 TFLOPS numbers are competitive but not necessarily class-leading. The absence of a launch MSRP and any rival data means that any positioning is speculative. The card is clearly designed for a niche: high-memory, high-bandwidth compute. If a rival has similar memory but lower bandwidth, the MI350X would have an advantage in memory-bound tasks. Conversely, if a rival has higher FP32 throughput but less memory, the MI350X would be better suited for models that exceed the rival's memory capacity. The data does not support any specific claims beyond these general principles. The 50th percentile is a neutral score, suggesting that the card is not an outlier in either direction, but this could be due to a lack of benchmark data rather than actual performance parity.

FAQ

Q: What is the memory size and type of the MI350X?

A: It has 288 GB of HBM3e memory, with an 8192-bit bus and 8.19 TB/s bandwidth.

Q: Does the MI350X have ray tracing or tensor cores?

A: No, the FACT PACK lists no ray tracing cores and no tensor cores; it is a compute-only accelerator.

Q: What is the FP16 performance of the card?

A: The FP16 throughput is 576.7 TFLOPS at an 8:1 ratio compared to FP32.

Q: What is the power requirement for the MI350X?

A: The card has a 1000 W TDP and requires a 1400 W suggested power supply.

Q: What is the transistor count and die size?

A: It contains 153,000 million transistors on a 1017 mm² die, using a 5 nm process from TSMC.

Q: Does the MI350X support DirectX or Vulkan?

A: No, there is no DirectX, OpenGL, or Vulkan API support listed; it has no display outputs.

Architecture and Design

The MI350X is built on the CDNA 3.0 architecture, specifically using the Aqua Vanjaram chip. The chip is manufactured on a 5 nm process at TSMC, with 153,000 million transistors packed into a 1017 mm² die. This results in a transistor density of 150.4M per mm², which is a measure of how tightly the transistors are packed. The card has 16,384 shading units, 1,024 texture mapping units, and 0 ROPs, which is consistent with its compute-focused design. The clock speeds are set at a base of 1000 MHz and a boost of 2200 MHz. The boost clock is a significant overclock from the base, allowing for a 220% increase in frequency under load. The FP32 performance of 72.09 TFLOPS is calculated from the shading units and clock speed. The FP16 performance of 576.7 TFLOPS is achieved via an 8:1 ratio, meaning for every FP32 operation, the card can perform eight FP16 operations, likely through specialized hardware or a different execution path. The card has no power connectors, relying on the OAM module interface for power delivery, and is a single-slot OAM Module form factor. The bus interface is PCIe 5.0 x16, which provides high-speed host communication. The predecessor is listed as "FirePro Data Center," which indicates a lineage of professional compute products. The card has no display outputs, reinforcing its role as a pure compute accelerator. The architecture is designed for throughput, not latency, with the 8.19 TB/s memory bandwidth being the key enabler. The 1000 W TDP is a substantial power draw, requiring robust cooling solutions, but the OAM form factor is designed for dense server deployments with active cooling.