AMD Instinct MI355X vs AMD Radeon Instinct MI300X Comparison

FAQ

Q: What is the primary difference in memory capacity between the two accelerators?

A: The AMD Instinct MI355X carries 288 GB of HBM3e memory, while the AMD Radeon Instinct MI300X carries 192 GB of HBM3. Both use an 8192-bit memory bus.

Q: How do the boost clocks compare?

A: The MI355X boosts to 2400 MHz, whereas the MI300X boosts to 2100 MHz. Both have the same 1000 MHz base clock.

Q: Which device uses a more advanced manufacturing process?

A: The MI355X is built on a 3 nm process at TSMC, while the MI300X uses a 5 nm process, also at TSMC. The MI355X also has a larger die at 2380 mm² versus 1017 mm² for the MI300X.

Q: How does the FP32 compute performance differ?

A: The MI300X delivers 81.72 TFLOPS of FP32, which is higher than the MI355X's 78.64 TFLOPS. In FP16, the MI300X reaches 653.7 TFLOPS (8:1 ratio), while the MI355X delivers 78.64 TFLOPS (1:1 ratio).

Q: What are the power requirements for each unit?

A: The MI355X has a TDP of 1400 W and a suggested PSU of 1800 W. The MI300X has a TDP of 750 W and a suggested PSU of 1150 W.

Q: When were these accelerators released?

A: The MI355X was released on 2025-06-11, and the MI300X was released on 2023-12-05.

Architecture Differences

The two accelerators represent successive generations of AMD's CDNA architecture. The MI355X uses CDNA 4.0, while the MI300X uses CDNA 3.0. This generational shift brings a change in manufacturing process: the MI355X is fabricated on a 3 nm node at TSMC, whereas the MI300X uses a 5 nm node at the same foundry.

The chip designs differ substantially. The MI355X uses a chip labeled "MI350 256CU," while the MI300X uses a chip called "Aqua Vanjaram." Transistor counts reflect the process and design changes: the MI355X packs 185,000 million transistors, compared to 153,000 million on the MI300X. Die size also grew, from 1017 mm² on the MI300X to 2380 mm² on the MI355X, although transistor density is lower on the newer part (77.7M / mm² versus 150.4M / mm²).

Memory architecture shows clear evolution. The MI355X uses HBM3e memory totaling 288 GB, while the MI300X uses HBM3 totaling 192 GB. Both share the same 8192-bit bus width, but the effective memory clock differs: 2000 MHz (8 Gbps effective) on the MI355X versus 2525 MHz (10.1 Gbps effective) on the MI300X. Interestingly, the MI300X achieves higher raw bandwidth at 10.3 TB/s compared to 8.19 TB/s on the MI355X, despite the latter's larger capacity.

The compute unit configurations also diverge. The MI355X has 16384 shading units, 1024 TMUs, and 0 ROPs. The MI300X has 19456 shading units, 1216 TMUs, and 0 ROPs. This means the MI300X has more shader and texture hardware, which partially explains its higher FP32 throughput. The FP16 situation is more complex: the MI355X lists FP16 as 78.64 TFLOPS (1:1 ratio), indicating symmetric FP16 and FP32 rates, while the MI300X lists FP16 as 653.7 TFLOPS (8:1 ratio), indicating a heavily accelerated packed FP16 path.

Both accelerators use OAM Module slot widths, have no display outputs, and use PCIe 5.0 x16 interfaces. Neither has any power connectors listed, and both rely on external power delivery through the module. The MI355X has physical dimensions of 102 mm length and 165 mm width, while the MI300X dimensions are not recorded.

The Verdict

The data indicates a clear generational trade-off rather than a straightforward winner. The MI355X brings a newer architecture (CDNA 4.0), a smaller process node (3 nm versus 5 nm), and significantly more memory capacity (288 GB versus 192 GB). The MI300X counters with higher FP32 throughput (81.72 TFLOPS versus 78.64 TFLOPS), dramatically higher FP16 performance (653.7 TFLOPS versus 78.64 TFLOPS), and higher memory bandwidth (10.3 TB/s versus 8.19 TB/s).

For workloads that depend on large memory capacity, the MI355X is the clear choice. Its 288 GB of HBM3e allows fitting larger models or datasets in memory compared to the MI300X's 192 GB. The newer 3 nm process and higher boost clock (2400 MHz versus 2100 MHz) suggest improved architectural efficiency, though the recorded data does not include benchmark scores to confirm this.

For workloads that rely on raw FP16 throughput, the MI300X is substantially ahead. Its 653.7 TFLOPS FP16 (8:1) dwarfs the MI355X's 78.64 TFLOPS FP16 (1:1). Similarly, the MI300X offers higher FP32 compute, which may benefit certain scientific or HPC applications that do not use packed math.

Neither device shows a benchmark advantage in the database: both have zero benchmark scores, zero wins in head-to-head comparisons, and a 50th percentile ranking among all GPUs. The average benchmark score for both is 0, meaning no performance data has been recorded. This limits the verdict to architectural and specification-based analysis.

The MI355X also requires substantially more power: 1400 W TDP versus 750 W TDP, and a suggested PSU of 1800 W versus 1150 W. This higher power draw accompanies the larger die and newer process, but the trade-off in operational requirements is notable.

Specification Differences

| Field | MI355X | MI300X |

|---|---|---|

| Architecture | CDNA 4.0 | CDNA 3.0 |

| Chip | MI350 256CU | Aqua Vanjaram |

| Process Node | 3 nm | 5 nm |

| Transistors | 185,000 million | 153,000 million |

| Die Size | 2380 mm² | 1017 mm² |

| Transistor Density | 77.7M / mm² | 150.4M / mm² |

| Boost Clock | 2400 MHz | 2100 MHz |

| Memory Clock | 2000 MHz (8 Gbps) | 2525 MHz (10.1 Gbps) |

| Memory Size | 288 GB | 192 GB |

| Memory Type | HBM3e | HBM3 |

| Bandwidth | 8.19 TB/s | 10.3 TB/s |

| Shading Units | 16384 | 19456 |

| TMUs | 1024 | 1216 |

| Texture Rate | 2,457.6 GTexel/s | 2,553.6 GTexel/s |

| FP32 | 78.64 TFLOPS | 81.72 TFLOPS |

| FP16 | 78.64 TFLOPS (1:1) | 653.7 TFLOPS (8:1) |

| TDP | 1400 W | 750 W |

| Suggested PSU | 1800 W | 1150 W |

| Release Date | 2025-06-11 | 2023-12-05 |

| Predecessor | Radeon Instinct | FirePro Data Center |

Fields that are identical include base clock (1000 MHz), memory bus width (8192 bit), pixel rate (0 MPixel/s), ROPs (0), slot width (OAM Module), power connectors (None), bus interface (PCIe 5.0 x16), display outputs (No outputs), and API support (all N/A or null). Neither has a launch MSRP recorded.

Head-to-Head Benchmarks

The database contains no recorded head-to-head benchmarks between these two accelerators. Both have empty benchmark arrays, zero wins each, and no nearest rivals listed. The average benchmark score for both is 0, and both sit at the 50th percentile among all GPUs.

This absence of measured data means the comparison must rely entirely on the specification differences documented above. The recorded numbers do, however, allow for several meaningful comparisons.

The most striking difference is in FP16 throughput. The MI300X delivers 653.7 TFLOPS, which is approximately 8.3 times the MI355X's 78.64 TFLOPS. This is a massive gap, reflecting the 8:1 FP16 ratio on the MI300X versus the 1:1 ratio on the MI355X. For any workload that uses FP16 tensor operations, the MI300X appears overwhelmingly faster on paper.

In FP32, the gap narrows but still favors the MI300X: 81.72 TFLOPS versus 78.64 TFLOPS, a difference of about 3.9%. The MI300X also leads in texture rate (2,553.6 GTexel/s versus 2,457.6 GTexel/s) and memory bandwidth (10.3 TB/s versus 8.19 TB/s). These advantages stem from the MI300X's larger number of shading units (19456 versus 16384) and TMUs (1216 versus 1024), despite its lower boost clock.

The MI355X counters with a higher boost clock (2400 MHz versus 2100 MHz, a 14.3% advantage) and more memory capacity (288 GB versus 192 GB, a 50% advantage). Its memory bandwidth is lower, but the larger pool of HBM3e memory may be more valuable for capacity-bound workloads.

Transistor count and die size differences are substantial. The MI355X uses 185,000 million transistors on a 2380 mm² die, while the MI300X uses 153,000 million on a 1017 mm² die. The MI300X achieves higher density (150.4M / mm² versus 77.7M / mm²), which is expected given the older, less complex process node.

Power consumption shows a major divergence. The MI355X draws 1400 W TDP, nearly double the MI300X's 750 W. The suggested PSU scales accordingly: 1800 W versus 1150 W. This means the MI355X requires significantly more power infrastructure, which could influence deployment decisions independent of compute performance.

Where Each One Wins

The MI355X wins in scenarios that prioritize memory capacity. Its 288 GB of HBM3e provides 96 GB more memory than the MI300X, which is a 50% increase. Larger memory pools directly benefit workloads that need to hold massive models, large batch sizes, or extensive datasets in GPU memory without spilling to host memory. The newer CDNA 4.0 architecture and 3 nm process also suggest architectural improvements, though no benchmark data confirms this.

The MI355X also wins on boost clock (2400 MHz versus 2100 MHz) and transistor count (185,000 million versus 153,000 million). These factors may translate to better single-threaded or latency-sensitive performance, though the recorded data does not include measurements to verify this.

The MI300X wins in raw compute throughput in both FP32 and FP16. Its FP32 advantage (81.72 TFLOPS versus 78.64 TFLOPS) is modest but consistent. Its FP16 advantage is enormous: 653.7 TFLOPS versus 78.64 TFLOPS, a more than 8-fold difference. Any workload that can exploit packed FP16 operations, such as certain AI training or inference paths, would see a substantial theoretical speedup on the MI300X.

The MI300X also wins on memory bandwidth (10.3 TB/s versus 8.19 TB/s), which benefits memory-bound operations even with a smaller total pool. Its higher texture rate (2,553.6 GTexel/s versus 2,457.6 GTexel/s) and greater number of shading units and TMUs provide additional compute resources.

Power efficiency, measured as performance per watt, favors the MI300X in both FP32 and FP16. For FP32, the MI300X delivers 81.72 TFLOPS at 750 W versus 78.64 TFLOPS at 1400 W for the MI355X. This means the MI300X achieves higher FP32 throughput with nearly half the power draw. For FP16, the MI300X's advantage is even more pronounced given its 653.7 TFLOPS at 750 W.

The MI355X's release date (2025-06-11) places it later than the MI300X (2023-12-05), suggesting it represents AMD's newer platform direction. However, the recorded data does not include any performance measurements, so the practical impact of this newer architecture remains unquantified in the database.