AMD Instinct MI325X vs NVIDIA B300 Comparison

Head-to-Head Benchmarks

The recorded data for both accelerators shows no direct benchmark scores, average scores of zero, and identical percentile rankings at 50. This means the database has no measured performance results for either the AMD Instinct MI325X or the NVIDIA B300. Without empirical head-to-head results, ranking them by actual workload performance is not possible. The absence of benchmark data does not indicate parity; it simply leaves the comparison to architectural and specification analysis.

What the data does provide is a clear picture of peak theoretical throughput. The AMD Instinct MI325X delivers 81.72 TFLOPS of FP32 compute. The NVIDIA B300 delivers 76.99 TFLOPS of FP32. That puts the MI325X approximately 6.1% ahead in single-precision floating-point throughput. It is a modest lead, not a dominant one, and it reflects the raw shading unit count rather than real-world application scaling.

The FP16 comparison is far more dramatic. The MI325X lists 81.72 TFLOPS of FP16 at a 1:1 ratio. The B300 lists 1,231.8 TFLOPS of FP16 at a 16:1 ratio. The B300 shows a massive advantage in mixed-precision work, approximately 15 times the FP16 throughput of the MI325X. This is the single largest numeric gap between the two parts. It indicates that the B300 is designed for dense FP16 tensor workloads, while the MI325X keeps FP16 at the same rate as FP32.

Texture rate also favors the MI325X. The AMD part reaches 2,553.6 GTexel/s, while the B300 reaches 1,202.9 GTexel/s. The MI325X is roughly 2.1 times faster in texture fill. That aligns with its much higher TMU count of 1216 versus 592 on the B300. Pixel rate favors the B300, which posts 48.77 GPixel/s against 0 MPixel/s for the MI325X. The AMD part has no ROP throughput listed, which is typical for a compute-focused accelerator with no display path.

Neither part has any recorded wins in the head-to-head section. The wins count is zero for both. This is consistent with the empty benchmark array. The database offers no workload-specific victories, so any claim of superiority must rest on hardware specifications, not measured results.

Architecture Differences

The AMD Instinct MI325X uses the Aqua Vanjaram chip built on CDNA 3.0 architecture. The NVIDIA B300 uses the GB110 chip built on Blackwell Ultra architecture. Both are fabricated on a 5 nm process at TSMC. The MI325X packs 153,000 million transistors on a 1017 mm² die. The B300 packs 104,000 million transistors, with no die size listed. That gives the MI325X a substantially larger transistor budget, roughly 47% more transistors than the B300. The transistor density for the MI325X is 150.4M per mm²; no density figure is recorded for the B300.

The MI325X has 19,456 shading units and 1,216 texture mapping units. The B300 has 18,944 shading units and 592 TMUs. The AMD part has no ROPs listed; the B300 has 24 ROPs. The B300 is the only one of the two with tensor cores, listing 592 of them. The MI325X has no tensor core count in the database. That is a fundamental architectural split: AMD leans on general-purpose shading units, NVIDIA adds dedicated tensor hardware.

The B300 also lists a 16:1 FP16 ratio, meaning its FP16 throughput is 16 times its FP32 rate. The MI325X lists a 1:1 ratio, meaning FP16 and FP32 throughput are identical. This is the clearest architectural difference between the two. The B300 is built for tensor-heavy computation, while the MI325X treats FP16 as a direct extension of FP32.

Memory architecture differs sharply. The MI325X uses 256 GB of HBM3e on an 8192-bit bus, yielding 6.14 TB/s of bandwidth. The B300 uses 144 GB of HBM3e on a 4096-bit bus, yielding 4.10 TB/s. The MI325X offers 112 GB more capacity and roughly 50% more bandwidth. The B300 compensates with a higher memory clock: 2000 MHz at 8 Gbps effective versus 1500 MHz at 6 Gbps effective for the AMD part, but the narrower bus limits total throughput.

Both parts use PCIe 5.0 x16 and have no display outputs. The MI325X is an OAM module with no power connectors and a 1000 W TDP. The B300 is an SXM module with a 1400 W TDP. The B300 has a higher base clock of 1665 MHz versus 1000 MHz, and a slightly lower boost clock of 2032 MHz versus 2100 MHz.

Where Each One Wins

Based on the recorded specifications, the AMD Instinct MI325X wins in several measurable categories. It has more memory capacity at 256 GB versus 144 GB. It has more memory bandwidth at 6.14 TB/s versus 4.10 TB/s. It has a wider memory bus at 8192 bit versus 4096 bit. It has more shading units at 19,456 versus 18,944. It has more TMUs at 1,216 versus 592. It has higher FP32 compute at 81.72 TFLOPS versus 76.99 TFLOPS. It has higher texture rate at 2,553.6 GTexel/s versus 1,202.9 GTexel/s. It also has a lower TDP at 1000 W versus 1400 W, making it the less power-hungry part on paper.

The NVIDIA B300 wins in FP16 throughput by a wide margin, 1,231.8 TFLOPS versus 81.72 TFLOPS. It has tensor cores, which the MI325X lacks entirely. It has a higher base clock at 1665 MHz versus 1000 MHz. It has a higher memory clock at 2000 MHz versus 1500 MHz. It has pixel rate capability at 48.77 GPixel/s, while the MI325X lists none. It also has a higher boost clock at 2032 MHz versus 2100 MHz, though the AMD part is slightly higher here.

The use-case split follows these numbers. The MI325X is positioned for workloads that need large memory pools and high FP32 throughput, such as traditional HPC simulation or large model inference where capacity matters more than tensor density. The B300 is positioned for FP16 tensor workloads, where its 16:1 ratio and dedicated tensor cores give it an overwhelming theoretical edge.

Specification Differences

| Specification | AMD Instinct MI325X | NVIDIA B300 |

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

| Chip | Aqua Vanjaram | GB110 |

| Architecture | CDNA 3.0 | Blackwell Ultra |

| Transistors | 153,000 million | 104,000 million |

| Die Size | 1017 mm² | Not listed |

| Base Clock | 1000 MHz | 1665 MHz |

| Boost Clock | 2100 MHz | 2032 MHz |

| Memory Size | 256 GB | 144 GB |

| Memory Clock | 1500 MHz 6 Gbps effective | 2000 MHz 8 Gbps effective |

| Memory Bus | 8192 bit | 4096 bit |

| Memory Bandwidth | 6.14 TB/s | 4.10 TB/s |

| Shading Units | 19,456 | 18,944 |

| TMUs | 1,216 | 592 |

| ROPs | 0 | 24 |

| Tensor Cores | Not listed | 592 |

| Pixel Rate | 0 MPixel/s | 48.77 GPixel/s |

| Texture Rate | 2,553.6 GTexel/s | 1,202.9 GTexel/s |

| FP32 | 81.72 TFLOPS | 76.99 TFLOPS |

| FP16 | 81.72 TFLOPS (1:1) | 1,231.8 TFLOPS (16:1) |

| TDP | 1000 W | 1400 W |

| Slot Width | OAM Module | SXM Module |

| Suggested PSU | 1400 W | 1800 W |

The MI325X has a transistor density of 150.4M per mm²; the B300 has no density figure. Both use 5 nm TSMC process, PCIe 5.0 x16, HBM3e memory, no display outputs, and have no launch MSRP recorded.

FAQ

Q: Which accelerator has more memory bandwidth?

A: The AMD Instinct MI325X. It lists 6.14 TB/s of bandwidth on an 8192-bit bus. The NVIDIA B300 lists 4.10 TB/s on a 4096-bit bus.

Q: Does the NVIDIA B300 have tensor cores?

A: Yes. The B300 lists 592 tensor cores. The AMD MI325X has no tensor core count recorded in the database.

Q: How do the two compare in FP32 compute?

A: The MI325X leads with 81.72 TFLOPS. The B300 trails at 76.99 TFLOPS, a difference of about 6.1%.

Q: Which part uses more power?

A: The NVIDIA B300 has a 1400 W TDP and a suggested PSU of 1800 W. The AMD MI325X has a 1000 W TDP and a suggested PSU of 1400 W.

Q: What is the FP16 performance difference?

A: The B300 lists 1,231.8 TFLOPS of FP16 at a 16:1 ratio. The MI325X lists 81.72 TFLOPS at a 1:1 ratio. The B300 is roughly 15 times higher in FP16 throughput.

Q: Are there any measured benchmark results for either card?

A: No. The database has empty benchmark arrays, an average benchmark score of zero, and zero recorded wins for both parts. The percentile rank for each is 50.

The Verdict

The data supports a split decision based on workload type. The AMD Instinct MI325X is the stronger choice for FP32 compute, memory capacity, and memory bandwidth. It offers 256 GB of HBM3e, 6.14 TB/s of bandwidth, 81.72 TFLOPS of FP32, and a lower 1000 W TDP. These specifications point to large-scale HPC and inference tasks where memory footprint and single-precision throughput dominate.

The NVIDIA B300 is the stronger choice for FP16 and tensor-heavy workloads. Its 1,231.8 TFLOPS of FP16 throughput at a 16:1 ratio, combined with 592 tensor cores, gives it a theoretical advantage of roughly 15 times in mixed-precision compute. It also has a higher base clock and a pixel rate that the MI325X does not list.

The absence of benchmark scores means the verdict rests entirely on specifications. The MI325X wins on capacity, bandwidth, FP32, and power efficiency. The B300 wins on tensor throughput and FP16 density. Neither part shows a measured performance lead in the database. The choice depends on whether the workload is FP32 memory-bound or FP16 tensor-bound.