AMD Instinct MI350X vs AMD Radeon Instinct MI300A Comparison

Head-to-Head Benchmarks

The recorded data for the AMD Instinct MI350X and the AMD Radeon Instinct MI300A shows no completed benchmark runs in the database. Both parts have an average benchmark score of 0, and the head-to-head benchmark array is empty. Consequently, the win tally is 0 for each product. This lack of measured performance data means that direct comparisons of frame rates, compute throughput, or latency cannot be made from the database at this time.

However, the absence of benchmark scores does not preclude a meaningful analysis. The database provides a substantial set of architectural and specification data that can be used to infer relative performance capabilities. The MI350X is built on the CDNA 4.0 architecture, while the MI300A utilizes CDNA 3.0. The MI350X features a 3 nm process node from TSMC, whereas the MI300A is fabricated on a 5 nm node. This generational difference in process technology is significant.

The MI350X is equipped with 16384 shading units, 1024 texture mapping units, and a boost clock of 2200 MHz. The MI300A, conversely, has a higher count of 19456 shading units and 1216 texture mapping units, but its boost clock is lower at 2100 MHz. The MI300A’s greater number of compute units and slightly higher base clock of 1000 MHz (matching the MI350X) do not directly translate into a definitive performance lead, as the architectural efficiency of CDNA 4.0 in the MI350X may offset the raw unit count advantage.

The most striking difference is in FP16 throughput. The MI300A delivers 653.7 TFLOPS of FP16 performance, but this is achieved with an 8:1 ratio relative to its FP32 output of 81.72 TFLOPS. The MI350X, by contrast, offers a 1:1 ratio for FP16 and FP32, both rated at 72.09 TFLOPS. This indicates that the MI300A’s FP16 number is a peak rate that relies on specialized hardware or reduced precision paths, while the MI350X provides consistent throughput across both precisions. For workloads that require full FP16 precision or mixed-precision operations without the 8:1 penalty, the MI350X’s architecture is likely more robust.

In FP32, the MI300A holds a raw specification advantage with 81.72 TFLOPS versus the MI350X’s 72.09 TFLOPS. The MI300A also leads in texture rate at 2,553.6 GTexel/s, compared to 2,252.8 GTexel/s for the MI350X. These figures suggest that in pure FP32 compute tasks, the MI300A may have a nominal lead on paper. Yet, the MI350X’s newer architecture and process node could yield higher sustained performance or better power efficiency per FLOP, which the database does not directly measure in the absence of benchmark results.

Where Each One Wins

Based on the recorded specifications, the AMD Radeon Instinct MI300A appears positioned for peak raw compute throughput. Its FP32 rating of 81.72 TFLOPS exceeds the MI350X’s 72.09 TFLOPS by approximately 13.4%. Similarly, its FP16 figure of 653.7 TFLOPS is dramatically higher, though this is a peak rate with an 8:1 ratio that may not be sustainable for all workloads. The MI300A also has a higher texture rate, which can benefit certain scientific computing and rendering tasks that rely heavily on texture fetch operations.

The AMD Instinct MI350X, on the other hand, wins on architectural modernity and memory capacity. It uses a 3 nm process, which typically offers better energy efficiency than the 5 nm node of the MI300A. The MI350X also supports 288 GB of HBM3e memory, a substantial increase over the MI300A’s 192 GB of HBM3. While the MI300A has a higher memory bandwidth at 10.3 TB/s, the MI350X’s 8.19 TB/s is still a very high figure, and the larger capacity enables larger model residency. For workloads that require fitting massive datasets or neural network weights into on-package memory, the MI350X is the clear choice based on capacity alone.

The FP16 1:1 ratio on the MI350X is another differentiator. Where the MI300A’s FP16 peak is tied to an 8:1 conversion, the MI350X’s 72.09 TFLOPS FP16 output is on par with its FP32. This means the MI350X can handle FP16 workloads without the performance drop that the MI300A might experience when precision requirements do not allow the 8:1 reduction. In AI inference and training scenarios where FP16 is common, the MI350X’s consistent throughput could be more valuable than the MI300A’s higher peak but less flexible rate.

Architecture Differences

The architectural gap between the two accelerators is defined by their respective CDNA generations. The MI350X uses CDNA 4.0, which is a newer design than the CDNA 3.0 found in the MI300A. This generational shift is accompanied by a change in manufacturing process: the MI350X is produced on a 3 nm node at TSMC, while the MI300A uses a 5 nm node, also at TSMC. The transistor counts reflect this density difference. The MI350X packs 185,000 million transistors onto a die size of 2380 mm², resulting in a transistor density of 77.7 million per square millimeter. The MI300A has 153,000 million transistors on a smaller die of 1017 mm², yielding a higher density of 150.4 million per square millimeter. The smaller die and higher density of the MI300A are notable, but the larger die of the MI350X allows for more total transistors, which can be used for additional compute or memory resources.

Memory subsystems diverge significantly. The MI350X is equipped with 288 GB of HBM3e memory, using a 8192-bit bus width and achieving a bandwidth of 8.19 TB/s. The memory clock is rated at 2000 MHz with 8 Gbps effective speed. The MI300A has 192 GB of HBM3 memory, also on an 8192-bit bus, but its bandwidth is higher at 10.3 TB/s. Its memory clock is 2525 MHz with 10.1 Gbps effective speed. This means the MI300A has faster memory, but the MI350X has more of it. The larger capacity of the MI350X is likely a deliberate choice for workloads that need to hold larger working sets.

Compute resources also differ in configuration. The MI350X has 16384 shading units and 1024 TMUs. The MI300A has 19456 shading units and 1216 TMUs. Neither product has ROPs, as both are rated at 0 MPixel/s pixel rate, which is typical for compute-focused accelerators without display outputs. Both cards are OAM modules with no display outputs, and both use a PCIe 5.0 x16 bus interface. Neither has dedicated power connectors, as they are designed for server integration.

Power and thermal specifications show a trade-off. The MI350X has a TDP of 1000 W and a suggested PSU of 1400 W. The MI300A has a lower TDP of 750 W and a suggested PSU of 1150 W. The MI350X consumes more power, which is consistent with its larger transistor count and die size, but its 3 nm process may mitigate some of the efficiency penalty. The physical dimensions of the MI350X are listed as 102 mm in length and 165 mm in width, while the MI300A’s dimensions are not recorded in the database.

The Verdict

The database does not contain any benchmark scores for either product, so a definitive performance verdict cannot be issued. The nearest rivals arrays are empty, and the percentile rankings are both at 50, indicating no comparative standing against other GPUs. The average benchmark score of 0 for both confirms that no measurements have been logged.

Given the specification data, the choice between the two depends on the priority of the user. The AMD Radeon Instinct MI300A offers higher peak FP32 performance at 81.72 TFLOPS, higher memory bandwidth at 10.3 TB/s, and a higher texture rate. It also has a lower TDP of 750 W, which may be preferable for dense server deployments where power is a constraint. Its FP16 peak of 653.7 TFLOPS is impressive, but the 8:1 ratio means that it is not a direct equivalent to the MI350X’s 1:1 FP16 capability.

The AMD Instinct MI350X, with its 3 nm process and CDNA 4.0 architecture, is positioned as the newer generation. It offers 288 GB of HBM3e memory, which is 50% more than the MI300A’s 192 GB. For AI workloads that require large model weights or massive datasets to be resident on the accelerator, this capacity advantage is decisive. Its FP16 throughput is lower in peak terms, but the 1:1 ratio provides consistent performance across FP32 and FP16, which is valuable for mixed-precision training and inference. The higher TDP of 1000 W is a trade-off for this capability.

From the recorded data, the MI300A is the choice for raw FP32 compute throughput and memory speed, while the MI350X is the choice for memory capacity and architectural generation. Without benchmark results, the database cannot confirm which product delivers better real-world performance in specific applications. The release dates are recorded as 2025-06-11 for the MI350X and 2023-12-05 for the MI300A, indicating that the MI350X is a more recent release, but this does not automatically confer a performance lead.

FAQ

Q: Which GPU has higher FP32 compute performance?

A: The AMD Radeon Instinct MI300A has a higher FP32 rating at 81.72 TFLOPS, compared to the AMD Instinct MI350X’s 72.09 TFLOPS.

Q: What is the memory capacity difference between the two?

A: The MI350X has 288 GB of HBM3e memory, while the MI300A has 192 GB of HBM3 memory. The MI350X offers 96 GB more capacity.

Q: How does the FP16 performance compare?

A: The MI300A has a peak FP16 rate of 653.7 TFLOPS with an 8:1 ratio to FP32. The MI350X has an FP16 rate of 72.09 TFLOPS with a 1:1 ratio to FP32, meaning its FP16 output matches its FP32 output.

Q: What are the process nodes for each product?

A: The MI350X is manufactured on a 3 nm process node at TSMC, while the MI300A is manufactured on a 5 nm process node, also at TSMC.

Q: What is the TDP difference?

A: The MI350X has a TDP of 1000 W, and the MI300A has a TDP of 750 W. The MI300A has a lower power draw.

Q: Do both GPUs have the same memory bus width?

A: Yes, both the MI350X and the MI300A use an 8192-bit memory bus width. The MI350X uses HBM3e memory, and the MI300A uses HBM3 memory.