AMD Instinct MI350P vs AMD Radeon Instinct MI300A Comparison

The AMD Instinct MI350P and AMD Radeon Instinct MI300A are both data center accelerators built for high throughput compute, but they target different segments of the workload spectrum. The MI350P is a newer, more power-efficient design based on a smaller process node, while the MI300A is a larger, older part with significantly more raw compute resources. The data in the database shows a clear split: the MI300A dominates in raw floating-point throughput and memory capacity, while the MI350P offers a more balanced power envelope with a lower thermal design point. Neither part has recorded benchmark scores in the database, so the analysis here relies entirely on the architectural specifications and their implications for real-world performance.

Where Each One Wins

The MI300A wins decisively in raw compute density. Its FP32 throughput of 81.72 TFLOPS is more than double the MI350P's 36.04 TFLOPS. For workloads that depend heavily on single-precision floating-point math, such as certain scientific simulations and general-purpose GPU compute, the MI300A holds a clear advantage. The MI300A also leads in FP16 performance, delivering 653.7 TFLOPS versus the MI350P's 36.04 TFLOPS. That is a substantial margin, and it points to the MI300A being the preferred option for mixed-precision training or inference tasks where half-precision math is used extensively.

The MI300A also wins on memory capacity and bandwidth. It carries 192 GB of HBM3 memory with a bandwidth of 10.3 TB/s, while the MI350P has 144 GB of HBM3e memory with 8.19 TB/s. The 48 GB capacity difference and the roughly 26% bandwidth advantage give the MI300A more headroom for large models or datasets that need to reside entirely in on-chip memory. For workloads where memory footprint is the limiting factor, the MI300A is the clear pick.

The MI350P, however, wins on power efficiency. Its 600 W TDP is 150 W lower than the MI300A's 750 W TDP. The database also lists a suggested PSU of 1000 W for the MI350P versus 1150 W for the MI300A. While neither part is efficient by consumer standards, the MI350P delivers its performance with a lower power draw, which can matter in dense server deployments where power and cooling are constrained. The MI350P also uses a 3 nm process node from TSMC, compared to the MI300A's 5 nm node, indicating a more advanced manufacturing technology that likely contributes to the lower power requirement.

The MI350P also has a more conventional physical form factor. It is a dual-slot card with a 1x 16-pin power connector and dimensions of 267 mm in length, 111 mm in height, and 40 mm in width. The MI300A, by contrast, is an OAM module with no power connectors and no listed dimensions. This means the MI350P can be installed in standard PCIe slots, while the MI300A requires an OAM carrier board, which is typically found in specialized systems.

Architecture Differences

The two accelerators are built on different generations of AMD's CDNA architecture. The MI350P uses CDNA 4.0, while the MI300A uses CDNA 3.0. This architectural generation gap is significant, as CDNA 4.0 is a newer design that likely includes optimizations for specific compute patterns, although the database does not provide details on those changes.

The process nodes differ as well. The MI350P is fabricated on a 3 nm process at TSMC, while the MI300A uses a 5 nm process at the same foundry. The transistor counts reflect this difference in a counterintuitive way. The MI300A has 153,000 million transistors, more than double the MI350P's 73,000 million transistors. The MI300A's die size is 1017 mm², slightly smaller than the MI350P's 1190 mm². This results in a transistor density of 150.4 million transistors per mm² for the MI300A, versus 61.3 million per mm² for the MI350P. The MI300A packs far more transistors into a smaller area, which explains its higher compute throughput.

The memory subsystems also differ. The MI350P uses HBM3e memory, while the MI300A uses HBM3. Both have an 8192-bit bus width, but the MI300A runs its memory at a higher effective speed of 10.1 Gbps, yielding a bandwidth of 10.3 TB/s. The MI350P's memory operates at 8 Gbps effective, producing 8.19 TB/s. The MI300A also has 192 GB of memory versus the MI350P's 144 GB.

The compute resources are heavily skewed toward the MI300A. It has 19,456 shading units, 1,216 texture mapping units, and 512 ROPs, although the pixel rate is listed as 0 MPixel/s for both parts, indicating they are not designed for rasterization. The MI350P has 8,192 shading units and 512 TMUs, also with 0 ROPs. The MI300A's FP32 throughput of 81.72 TFLOPS comes from this much larger shading unit count. The FP16 figures are even more divergent: the MI300A lists 653.7 TFLOPS with an 8:1 ratio, suggesting dedicated hardware for half-precision math, while the MI350P lists 36.04 TFLOPS at a 1:1 ratio, meaning it processes FP16 at the same rate as FP32.

Head-to-Head Benchmarks

The database does not contain any recorded head-to-head benchmark results for these two accelerators. The winsA and winsB fields are both zero, and the headToHeadBenchmarks array is empty. This means there are no measured performance comparisons available. The analysis must therefore rely on the specification differences to project where each part would lead in a direct comparison.

Based on those specifications, the MI300A would win any compute-bound benchmark that stresses FP32 or FP16 throughput. Its FP32 score of 81.72 TFLOPS is 2.27 times higher than the MI350P's 36.04 TFLOPS. In FP16, the MI300A's 653.7 TFLOPS is 18.1 times higher than the MI350P's 36.04 TFLOPS. These are massive margins, and they would translate to large wins in any benchmark that measures raw floating-point operations per second.

The MI300A would also win memory-bound benchmarks. Its 10.3 TB/s bandwidth is 25.8% higher than the MI350P's 8.19 TB/s, and its 192 GB capacity is 33.3% larger than the MI350P's 144 GB. For workloads that stream large amounts of data through the memory system, the MI300A would complete the task in less time.

The MI350P would win benchmarks that measure performance per watt. Its FP32 throughput per watt is 36.04 TFLOPS divided by 600 W, which equals 0.060 TFLOPS per watt. The MI300A's FP32 throughput per watt is 81.72 TFLOPS divided by 750 W, which equals 0.109 TFLOPS per watt. That calculation shows the MI300A is actually more efficient in FP32 per watt, so the MI350P does not win that particular metric. In FP16, the MI300A's 653.7 TFLOPS per 750 W gives 0.872 TFLOPS per watt, while the MI350P's 36.04 TFLOPS per 600 W gives 0.060 TFLOPS per watt. The MI300A is more efficient in both compute metrics per watt, despite its higher absolute power draw.

The MI350P would win benchmarks that measure power draw alone, since it has a 600 W TDP versus the MI300A's 750 W TDP. It would also win on the suggested PSU requirement, with 1000 W versus 1150 W. Neither part has display outputs, so there is no comparison in that area.

The Verdict

The data indicates a clear split in suitability. The MI300A is the stronger choice for workloads that demand maximum compute throughput and memory capacity. Its 81.72 TFLOPS FP32 and 653.7 TFLOPS FP16 figures, combined with 192 GB of HBM3 memory, make it suited for large-scale AI training, scientific computing, and any task where raw performance is the primary goal. The MI300A also has the higher transistor count at 153,000 million, which supports its expanded compute resources.

The MI350P is the more practical choice for deployments where power and physical space are constrained. Its 600 W TDP and dual-slot PCIe form factor allow it to be installed in standard server chassis without the need for OAM carrier boards. Its 3 nm process node and 73,000 million transistor count indicate a more modern design that may offer better performance per transistor, although the database does not provide measured efficiency data to confirm this. For clusters that need a mix of compute density and lower thermal output, the MI350P fits that niche.

Neither part has any recorded benchmark scores or rival comparisons in the database, so the verdict is based entirely on the architectural specifications. The MI300A is the performance leader by a wide margin, while the MI350P offers a lower-power alternative in a more conventional form factor.

FAQ

Q: Which accelerator has higher FP32 performance?

A: The Radeon Instinct MI300A leads with 81.72 TFLOPS, compared to the Instinct MI350P's 36.04 TFLOPS.

Q: What is the memory capacity difference?

A: The MI300A has 192 GB of HBM3 memory, while the MI350P has 144 GB of HBM3e memory, a difference of 48 GB.

Q: How do the process nodes compare?

A: The MI350P uses a 3 nm process from TSMC, while the MI300A uses a 5 nm process from the same foundry.

Q: Which part has a lower TDP?

A: The MI350P has a 600 W TDP, which is 150 W lower than the MI300A's 750 W TDP.

Q: Are there any display outputs on either accelerator?

A: No, both parts are listed with no display outputs.

Q: What is the transistor density of each part?

A: The MI300A has a transistor density of 150.4 million per mm², while the MI350P has 61.3 million per mm².

Specification Differences

| Field | AMD Instinct MI350P | AMD Radeon Instinct MI300A |

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

| Architecture | CDNA 4.0 | CDNA 3.0 |

| Process Node | 3 nm | 5 nm |

| Transistors | 73,000 million | 153,000 million |

| Die Size | 1190 mm² | 1017 mm² |

| Transistor Density | 61.3M / mm² | 150.4M / mm² |

| Base Clock | 1000 MHz | 1000 MHz |

| Boost Clock | 2200 MHz | 2100 MHz |

| Memory Clock | 2000 MHz, 8 Gbps effective | 2525 MHz, 10.1 Gbps effective |

| Memory Size | 144 GB | 192 GB |

| Memory Type | HBM3e | HBM3 |

| Memory Bus Width | 8192 bit | 8192 bit |

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

| Shading Units | 8192 | 19456 |

| TMUs | 512 | 1216 |

| ROPs | 0 | 0 |

| Pixel Rate | 0 MPixel/s | 0 MPixel/s |

| Texture Rate | 1,126.4 GTexel/s | 2,553.6 GTexel/s |

| FP32 | 36.04 TFLOPS | 81.72 TFLOPS |

| FP16 | 36.04 TFLOPS (1:1) | 653.7 TFLOPS (8:1) |

| TDP | 600 W | 750 W |

| Slot Width | Dual-slot | OAM Module |

| Power Connectors | 1x 16-pin | None |

| Suggested PSU | 1000 W | 1150 W |

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

| Display Outputs | No outputs | No outputs |

| Release Date | 2026-05-06 | 2023-12-05 |

| Predecessor | Radeon Instinct | FirePro Data Center |