AMD Instinct MI308X vs AMD Radeon Instinct MI300A Comparison

Both accelerators share the same physical foundation, yet the recorded data reveals a clear split in compute capabilities. The most significant difference lies in the FP16 throughput figures, where the MI300A delivers 653.7 TFLOPS (8:1) compared to the MI308X’s 81.72 TFLOPS (1:1). This represents an 8x advantage in raw half-precision arithmetic for the MI300A. In FP32, both cards are identical at 81.72 TFLOPS, and the texture rate is the same at 2,553.6 GTexel/s. The MI300A also doubles the effective memory speed, running at 10.1 Gbps effective versus the MI308X’s 5.2 Gbps effective, which translates to a memory bandwidth of 10.3 TB/s against 5.32 TB/s. The MI308X holds no performance lead in any recorded benchmark metric; the data shows a zero-win result for the MI308X and a zero-win result for the MI300A in the head-to-head benchmark array, which is empty. The percentile ranking against all GPUs is 50 for both, and the average benchmark score is 0 for each.

Head-to-Head Benchmarks

The benchmark database contains no recorded head-to-head scores for these two accelerators. The wins array shows zero wins for each product, and the nearest rivals list is empty. Without benchmark entries, the comparison rests entirely on the specification-level data. The FP32 performance is identical: both cards output 81.72 TFLOPS. The texture rate matches at 2,553.6 GTexel/s. The pixel rate is 0 MPixel/s for both, which is expected for compute accelerators without display output. The meaningful divergence appears in memory bandwidth and FP16 throughput. The MI300A’s memory runs at 10.1 Gbps effective, yielding 10.3 TB/s. The MI308X’s memory runs at 5.2 Gbps effective, yielding 5.32 TB/s. That is nearly a 2x gap in memory bandwidth. The FP16 numbers are even more pronounced: the MI300A’s 653.7 TFLOPS is an 8x multiple of the MI308X’s 81.72 TFLOPS. The data indicates that any workload sensitive to half-precision math or memory bandwidth will favor the MI300A by a wide margin. Workloads that rely on FP32 or texture operations will see no difference, as the core counts and clock speeds are identical.

Architecture Differences

Both chips are built on the same Aqua Vanjaram silicon, using the CDNA 3.0 architecture. The process node is 5 nm at TSMC, with 153,000 million transistors on a 1017 mm² die. Transistor density is 150.4M per mm². The base clock is 1000 MHz and the boost clock is 2100 MHz for both. Shading units number 19,456, and TMUs number 1,216. ROPs are 0 for both. There are no ray tracing cores or tensor cores listed in the database. The memory configuration is identical in capacity and bus width: 192 GB of HBM3 on an 8192-bit bus. The difference is the memory clock. The MI308X runs at 1300 MHz with 5.2 Gbps effective, while the MI300A runs at 2525 MHz with 10.1 Gbps effective. This doubles the bandwidth from 5.32 TB/s to 10.3 TB/s. The FP16 execution mode also differs: the MI308X reports 81.72 TFLOPS (1:1), meaning it processes FP16 at the same rate as FP32. The MI300A reports 653.7 TFLOPS (8:1), meaning it processes FP16 at eight times the FP32 rate through a different packing scheme. Both cards use an OAM module slot width, have no power connectors, and list a suggested PSU of 1150 W. The TDP is 750 W for both. The bus interface is PCIe 5.0 x16 for both, and neither has display outputs. The API support is marked as N/A for the MI308X, while the MI300A lists null values for DirectX, OpenGL, and Vulkan. The release date is the same, 2023-12-05, for both products.

FAQ

Q: Which card has higher FP16 performance?

A: The MI300A delivers 653.7 TFLOPS (8:1), which is 8x the MI308X’s 81.72 TFLOPS (1:1).

Q: Do the two accelerators share the same memory capacity?

A: Yes, both have 192 GB of HBM3 on an 8192-bit bus.

Q: Is the memory bandwidth identical?

A: No. The MI308X provides 5.32 TB/s, while the MI300A provides 10.3 TB/s.

Q: What are the clock speeds for each card?

A: Both have a base clock of 1000 MHz and a boost clock of 2100 MHz. The memory clock differs: 1300 MHz (5.2 Gbps effective) for the MI308X, and 2525 MHz (10.1 Gbps effective) for the MI300A.

Q: Are there any differences in power requirements?

A: No. Both have a TDP of 750 W and a suggested PSU of 1150 W.

Q: What is the transistor count and die size?

A: Both use 153,000 million transistors on a 1017 mm² die, with a density of 150.4M per mm².

The Verdict

The data points to a straightforward choice for compute-heavy deployments. The MI300A is the superior part for mixed-precision AI training and inference, given its 8x FP16 throughput and 10.3 TB/s memory bandwidth. The MI308X matches the MI300A in FP32, texture rate, clock speeds, and memory capacity, but it cannot compete in the metrics that matter most for modern machine learning workloads. The MI308X is essentially the same silicon with a memory clock cut in half and FP16 processing limited to a 1:1 ratio. The recorded data shows no scenario where the MI308X outperforms the MI300A. For workloads that require FP32 precision at scale, the two are interchangeable, as the FP32 TFLOPS and texture rates are identical. For workloads that depend on half-precision arithmetic or high memory bandwidth, the MI300A is the clear choice. The MI308X may be suitable for FP32-centric compute tasks where the extra memory bandwidth and FP16 throughput are not relevant, but the absence of any benchmark wins means the MI300A is the safer selection based on the specification sheet alone.

Specification Differences

The two cards differ in memory clock, effective memory speed, memory bandwidth, FP16 performance, and API support fields. The MI308X lists a memory clock of 1300 MHz with 5.2 Gbps effective, while the MI300A lists 2525 MHz with 10.1 Gbps effective. Bandwidth is 5.32 TB/s versus 10.3 TB/s. FP16 is 81.72 TFLOPS (1:1) for the MI308X, versus 653.7 TFLOPS (8:1) for the MI300A. The API fields are N/A for the MI308X and null for the MI300A. All other fields are identical: 5 nm process, 153,000 million transistors, 1017 mm² die, 150.4M per mm² density, 1000 MHz base clock, 2100 MHz boost clock, 192 GB HBM3, 8192-bit bus, 19,456 shading units, 1,216 TMUs, 0 ROPs, 0 MPixel/s pixel rate, 2,553.6 GTexel/s texture rate, 81.72 TFLOPS FP32, 750 W TDP, OAM Module slot width, no power connectors, 1150 W suggested PSU, PCIe 5.0 x16 bus interface, no display outputs, and release date 2023-12-05.

Where Each One Wins

The MI300A wins in every scenario that involves FP16 math or memory-intensive data movement. Its 653.7 TFLOPS FP16 output makes it suitable for deep learning training loops that use mixed precision. Its 10.3 TB/s bandwidth supports large model parameter loading and high-throughput data streaming. The MI308X offers no unique advantage in the recorded data. It matches the MI300A in FP32 compute, texture rate, and memory capacity, but it does not exceed it in any field. The MI308X is effectively a lower-bandwidth, lower-FP16 variant of the same chip. Use cases that require pure FP32 compute, such as certain scientific simulations or legacy HPC codes, will see identical performance from either card, since the FP32 TFLOPS are the same. Use cases that rely on FP16 tensor operations should use the MI300A exclusively. The MI308X’s only potential role is as a drop-in replacement in systems already configured for its memory profile, but the data does not show any performance benefit from choosing it over the MI300A. The MI300A is the stronger accelerator across the board, with the FP16 and bandwidth advantages being the defining factors.