AMD Instinct MI300X vs AMD Instinct MI350X Comparison

Head-to-Head Benchmarks

The benchmark database currently contains a single recorded OpenCL score for the AMD Instinct MI300X, while the AMD Instinct MI350X has no benchmark entries recorded yet. This makes a direct numerical head-to-head comparison difficult, but the data that exists still provides useful reference points.

The MI300X records an OpenCL score of 317,994. This places it at the 100th percentile among all GPUs in the database, meaning it outperforms every other recorded GPU in this specific workload. Its closest recorded rival, the NVIDIA B200, scores 345,482, which is 8% higher than the MI300X. The NVIDIA H200 NVL scores 334,891, also ahead by 5%. On the other side, the MI300X leads the NVIDIA L40S by 7.5% (295,763 versus 317,994) and the NVIDIA RTX 6000 Ada Generation by 10.7% (287,237 versus 317,994).

The MI350X, however, shows an average benchmark score of 0 and a percentile rank of 50, which simply reflects that no OpenCL result has been submitted to the database. The database lists no nearest rivals for the MI350X and no head-to-head benchmark entries between the two AMD accelerators. Without a recorded score, the data cannot confirm whether the MI350X outperforms the MI300X in OpenCL, despite the architectural improvements detailed later in this analysis.

What the data does show is the MI300X's position among existing accelerators. It sits between the H200 NVL and the L40S, with a 5% deficit to the former and a 7.5% advantage over the latter. The gap to the B200 is the largest in either direction at 8%. These deltas give context for where the MI300X stands in the current accelerator landscape, even though the MI350X's absence prevents a direct comparison between the two AMD parts.

Architecture Differences

The two accelerators differ substantially at the silicon level. The MI300X uses the Aqua Vanjaram chip built on CDNA 3.0 architecture, while the MI350X uses the MI350 256CU chip on CDNA 4.0. The manufacturing process moves from 5 nm to 3 nm, both from TSMC. Transistor counts rise from 153,000 million on the MI300X to 185,000 million on the MI350X, despite the die size nearly doubling from 1017 mm² to 2380 mm². This results in a transistor density drop from 150.4M per mm² on the MI300X to 77.7M per mm² on the MI350X, reflecting the larger physical layout of the newer chip.

Memory configurations differ markedly. The MI300X carries 192 GB of HBM3 across an 8192-bit bus, delivering 5.32 TB/s of bandwidth. The MI350X expands to 288 GB of HBM3e on the same 8192-bit bus, with bandwidth rising to 8.19 TB/s. Memory clock rates also increase, from 1300 MHz (5.2 Gbps effective) on the MI300X to 2000 MHz (8 Gbps effective) on the MI350X.

Compute resources shift in an unexpected direction. The MI300X has 19,456 shading units, 1,216 texture mapping units, and a texture rate of 2,553.6 GTexel/s. The MI350X has fewer shading units at 16,384, fewer TMUs at 1,024, and a lower texture rate of 2,252.8 GTexel/s. Correspondingly, FP32 performance drops from 81.72 TFLOPS on the MI300X to 72.09 TFLOPS on the MI350X, and FP16 performance follows the same pattern, also at 81.72 TFLOPS (1:1) versus 72.09 TFLOPS (1:1). Both parts report 0 MPixel/s pixel rate and no ROPs, indicating their compute-focused design.

Clock speeds differ slightly. Both have a 1000 MHz base clock, but the boost clock rises from 2100 MHz on the MI300X to 2200 MHz on the MI350X. Power requirements scale up accordingly, from a 750 W TDP with a suggested PSU of 1150 W on the MI300X to a 1000 W TDP with a suggested PSU of 1400 W on the MI350X. Both use an OAM Module slot width with no power connectors and no display outputs. The bus interface remains PCIe 5.0 x16 on both. The MI350X has recorded physical dimensions of 102 mm by 165 mm, while the MI300X has no dimension entries.

Release dates place the MI300X in December 2023 and the MI350X in June 2025, both succeeding the Radeon Instinct line. Neither part has a recorded successor or a launch MSRP in the database.

Where Each One Wins

Based strictly on recorded data, the MI300X wins in raw compute throughput. Its FP32 and FP16 figures of 81.72 TFLOPS exceed the MI350X's 72.09 TFLOPS in both precision formats. The MI300X also has more shading units (19,456 versus 16,384) and more TMUs (1,216 versus 1,024), with a higher texture rate of 2,553.6 GTexel/s compared to 2,252.8 GTexel/s. For workloads that scale directly with shader count and FP32 throughput, the MI300X holds the advantage according to the specification data.

The MI350X wins decisively in memory capacity and bandwidth. Its 288 GB of HBM3e provides 50% more capacity than the MI300X's 192 GB of HBM3. Bandwidth jumps from 5.32 TB/s to 8.19 TB/s, a substantial increase that benefits memory-bound workloads such as large model inference and data-intensive training. The MI350X also uses faster memory, with 8 Gbps effective speed versus 5.2 Gbps on the MI300X.

The MI350X's 3 nm process node offers a density improvement per transistor, though the overall transistor count is higher at 185,000 million versus 153,000 million. The larger die size of 2380 mm² suggests more physical space for memory stacks and logic, which aligns with its higher capacity and bandwidth figures.

The MI300X has the only recorded benchmark score, giving it an empirical win in this category for now. Its OpenCL result of 317,994 places it above the L40S and RTX 6000 Ada, though below the H200 NVL and B200. The MI350X has no recorded score, so the data cannot award it a benchmark win.

For power efficiency, the MI300X requires less power at 750 W TDP versus 1000 W TDP for the MI350X, and its suggested PSU is lower at 1150 W compared to 1400 W. This gives the MI300X an advantage in systems where power delivery is constrained.

The Verdict

The data supports different selections depending on the workload priority. For compute-bound tasks where FP32 or FP16 throughput is the limiting factor, the MI300X delivers 81.72 TFLOPS in both formats, outperforming the MI350X's 72.09 TFLOPS. Its higher shading unit count and texture rate reinforce this position. The MI300X also has the only verified benchmark score in the database, with an OpenCL result of 317,994 that places it ahead of several NVIDIA competitors.

For memory-bound workloads, particularly large language model inference or training with massive batch sizes, the MI350X is the stronger choice. Its 288 GB of HBM3e at 8.19 TB/s bandwidth provides 96 GB more capacity and 2.87 TB/s more bandwidth than the MI300X. The faster 8 Gbps effective memory speed further reduces latency bottlenecks.

The MI350X's higher boost clock of 2200 MHz versus 2100 MHz does not compensate for its lower shader count in raw throughput, but the architectural shift to CDNA 4.0 and the 3 nm process may offer efficiency benefits not captured in the current specification fields. The database does not record any benchmark performance for the MI350X, so any claims about its real-world speed remain unverified.

Users who already operate within a 750 W power envelope will find the MI300X easier to integrate, as its suggested PSU of 1150 W is lower than the MI350X's 1400 W requirement. Users who need maximum memory capacity and bandwidth should accept the higher power draw of the MI350X.

Given the absence of MI350X benchmark data, the MI300X remains the only AMD accelerator in this comparison with empirical performance evidence. The MI350X's specifications suggest it targets a different segment, prioritizing memory scale over compute density, but the data cannot confirm its performance until benchmarks are recorded.

FAQ

Q: Which GPU has a higher OpenCL benchmark score?

A: Only the MI300X has a recorded OpenCL score in the database, at 317,994. The MI350X has no benchmark entries, so no comparison is possible.

Q: How does the MI300X compare to NVIDIA accelerators in OpenCL?

A: The MI300X scores 317,994, which is 8% behind the NVIDIA B200 (345,482), 5% behind the H200 NVL (334,891), 7.5% ahead of the L40S (295,763), and 10.7% ahead of the RTX 6000 Ada Generation (287,237).

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

A: The MI300X has 192 GB of HBM3, while the MI350X has 288 GB of HBM3e, a difference of 96 GB.

Q: How much faster is the MI350X memory bandwidth?

A: The MI350X delivers 8.19 TB/s, while the MI300X delivers 5.32 TB/s, making the MI350X 2.87 TB/s faster.

Q: Which accelerator has higher FP32 performance?

A: The MI300X achieves 81.72 TFLOPS FP32, while the MI350X achieves 72.09 TFLOPS, giving the MI300X a higher raw throughput.

Q: What are the power requirements for each?

A: The MI300X has a 750 W TDP with a suggested PSU of 1150 W, while the MI350X has a 1000 W TDP with a suggested PSU of 1400 W.

Specification Differences

| Specification | AMD Instinct MI300X | AMD Instinct MI350X |

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

| Chip | Aqua Vanjaram | MI350 256CU |

| Architecture | CDNA 3.0 | CDNA 4.0 |

| Process Node | 5 nm | 3 nm |

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

| Die Size | 1017 mm² | 2380 mm² |

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

| Base Clock | 1000 MHz | 1000 MHz |

| Boost Clock | 2100 MHz | 2200 MHz |

| Memory Clock | 1300 MHz, 5.2 Gbps effective | 2000 MHz, 8 Gbps effective |

| Memory Size | 192 GB | 288 GB |

| Memory Type | HBM3 | HBM3e |

| Memory Bus Width | 8192 bit | 8192 bit |

| Memory Bandwidth | 5.32 TB/s | 8.19 TB/s |

| Shading Units | 19,456 | 16,384 |

| TMUs | 1,216 | 1,024 |

| Texture Rate | 2,553.6 GTexel/s | 2,252.8 GTexel/s |

| FP32 Performance | 81.72 TFLOPS | 72.09 TFLOPS |

| FP16 Performance | 81.72 TFLOPS (1:1) | 72.09 TFLOPS (1:1) |

| TDP | 750 W | 1000 W |

| Suggested PSU | 1150 W | 1400 W |

| Slot Width | OAM Module | OAM Module |

| Power Connectors | None | None |

| Bus Interface | PCIe 5.0 x16 | PCIe 5.0 x16 |

| Display Outputs | No outputs | No outputs |

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

| Dimensions | Not recorded | 102 mm by 165 mm |