AMD Instinct MI350X vs NVIDIA H100 CNX Comparison

FAQ

Q: What are the core architectural differences between the AMD Instinct MI350X and the NVIDIA H100 CNX?

A: The AMD Instinct MI350X uses the CDNA 4.0 architecture on a 3 nm TSMC process, while the NVIDIA H100 CNX uses the Hopper architecture on a 5 nm TSMC process. The MI350X packs 185,000 million transistors on a 2380 mm² die, whereas the H100 CNX contains 80,000 million transistors on an 814 mm² die.

Q: How do the memory subsystems compare between these two accelerators?

A: The MI350X features 288 GB of HBM3e memory with a 8192-bit bus and 8.19 TB/s bandwidth. The H100 CNX offers 80 GB of HBM2e memory with a 5120-bit bus and 2.04 TB/s bandwidth. The MI350X provides 3.6 times the memory capacity and roughly 4 times the bandwidth.

Q: Which card has higher FP32 throughput?

A: The AMD Instinct MI350X delivers 72.09 TFLOPS of FP32 performance, which is about 34% higher than the NVIDIA H100 CNX's 53.84 TFLOPS. However, in FP16 workloads, the H100 CNX outputs 215.4 TFLOPS (4:1 ratio) versus the MI350X's 72.09 TFLOPS (1:1 ratio).

Q: What are the power requirements for each accelerator?

A: The MI350X has a TDP of 1000 W and suggests a 1400 W power supply, using an OAM module form factor with no power connectors. The H100 CNX has a TDP of 350 W, suggests a 750 W power supply, uses a dual-slot design, and draws power through an 8-pin EPS connector.

Q: What are the physical dimensions of these cards?

A: The MI350X measures 102 mm in length and 165 mm in width. The H100 CNX is substantially longer at 267 mm, with a height of 111 mm. Both cards use a PCIe 5.0 x16 bus interface and have no display outputs.

Q: When were these products released?

A: The NVIDIA H100 CNX was released on 2023-03-20 and remains in active production, with a successor named Server Blackwell. The AMD Instinct MI350X was released on 2025-06-11, succeeding the Radeon Instinct line.

Architecture Differences

The AMD Instinct MI350X and NVIDIA H100 CNX represent fundamentally different design philosophies for data center acceleration. The MI350X is built on CDNA 4.0, AMD's compute-optimized architecture, fabricated on a 3 nm TSMC process. The H100 CNX uses NVIDIA's Hopper architecture on a 5 nm TSMC process. This process advantage allows AMD to pack 185,000 million transistors onto a 2380 mm² die, a massive chip that dwarfs the H100 CNX's 80,000 million transistors on an 814 mm² die. Interestingly, the H100 CNX achieves a higher transistor density at 98.3M per mm² compared to the MI350X's 77.7M per mm², despite using an older process node.

The memory architectures diverge sharply. The MI350X uses HBM3e with 288 GB capacity, an 8192-bit bus, and 8.19 TB/s bandwidth. The H100 CNX uses HBM2e with 80 GB capacity, a 5120-bit bus, and 2.04 TB/s bandwidth. This represents a generational leap in memory technology for AMD, delivering quadruple the bandwidth and more than triple the capacity.

Compute resources differ in configuration. The MI350X contains 16,384 shading units, 1024 TMUs, and no ROPs, resulting in a texture rate of 2,252.8 GTexel/s and 0 MPixel/s pixel rate. The H100 CNX has 14,592 shading units, 456 TMUs, and 24 ROPs, yielding 841.3 GTexel/s texture rate and 44.28 GPixel/s pixel rate. The MI350X's FP32 throughput reaches 72.09 TFLOPS, while its FP16 performance matches at 72.09 TFLOPS with a 1:1 ratio. The H100 CNX, by contrast, delivers 53.84 TFLOPS FP32 but 215.4 TFLOPS FP16 with a 4:1 ratio, indicating NVIDIA's focus on mixed-precision tensor workloads. The H100 CNX also includes 456 tensor cores, a feature absent from the MI350X specification.

Clock speeds reflect different power envelopes. The MI350X runs at a 1000 MHz base and 2200 MHz boost, while the H100 CNX operates at 690 MHz base and 1845 MHz boost. The MI350X memory runs at 2000 MHz (8 Gbps effective), versus 1593 MHz (3.2 Gbps effective) for the H100 CNX. The power disparity is significant: 1000 W TDP for the MI350X versus 350 W for the H100 CNX.

Where Each One Wins

The benchmark data in the database shows no recorded wins for either accelerator, as both have an average benchmark score of 0 and hold the 50th percentile against all GPUs. However, the recorded specifications allow for reasoned analysis of workload suitability.

The AMD Instinct MI350X wins decisively in memory-bound applications. Its 288 GB HBM3e capacity and 8.19 TB/s bandwidth make it suitable for large language model inference, massive sparse matrix operations, and workloads where the entire model or dataset must reside in high-bandwidth memory. The 8192-bit bus width indicates that the MI350X is engineered for extreme parallel memory access patterns. Its FP32 throughput of 72.09 TFLOPS, which is 34% higher than the H100 CNX, gives it an edge in traditional scientific computing that relies on single-precision arithmetic without specialized tensor cores.

The NVIDIA H100 CNX wins in power-constrained environments and mixed-precision AI training. Its 215.4 TFLOPS FP16 performance, achieved through 456 tensor cores, represents roughly 3 times the FP16 throughput of the MI350X. This makes the H100 CNX more suited for transformer training, where tensor core acceleration dominates. The H100 CNX's 350 W TDP versus the MI350X's 1000 W means it can be deployed in denser configurations or in facilities with strict power budgets. Its dual-slot form factor and 267 mm length also allow installation in standard server chassis, whereas the MI350X's OAM module form factor requires specialized carrier boards.

For pixel processing, the H100 CNX's 24 ROPs and 44.28 GPixel/s pixel rate provide some rasterization capability, though neither card targets graphics workloads. The MI350X has no ROPs at all, confirming its pure compute orientation.

Specification Differences

| Specification | AMD Instinct MI350X | NVIDIA H100 CNX |

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

| Architecture | CDNA 4.0 | Hopper |

| Process Node | 3 nm | 5 nm |

| Transistors | 185,000 million | 80,000 million |

| Die Size | 2380 mm² | 814 mm² |

| Transistor Density | 77.7M / mm² | 98.3M / mm² |

| Base Clock | 1000 MHz | 690 MHz |

| Boost Clock | 2200 MHz | 1845 MHz |

| Memory Clock | 2000 MHz (8 Gbps effective) | 1593 MHz (3.2 Gbps effective) |

| Memory Size | 288 GB | 80 GB |

| Memory Type | HBM3e | HBM2e |

| Memory Bus Width | 8192 bit | 5120 bit |

| Memory Bandwidth | 8.19 TB/s | 2.04 TB/s |

| Shading Units | 16384 | 14592 |

| TMUs | 1024 | 456 |

| ROPs | 0 | 24 |

| Tensor Cores | None listed | 456 |

| Pixel Rate | 0 MPixel/s | 44.28 GPixel/s |

| Texture Rate | 2,252.8 GTexel/s | 841.3 GTexel/s |

| FP32 | 72.09 TFLOPS | 53.84 TFLOPS |

| FP16 | 72.09 TFLOPS (1:1) | 215.4 TFLOPS (4:1) |

| TDP | 1000 W | 350 W |

| Slot Width | OAM Module | Dual-slot |

| Power Connectors | None | 8-pin EPS |

| Suggested PSU | 1400 W | 750 W |

| Dimensions | 102 mm x 165 mm | 267 mm x 111 mm |

| Release Date | 2025-06-11 | 2023-03-20 |

| Production Status | Not listed | Active |

Head-to-Head Benchmarks

The database contains no recorded head-to-head benchmark results, no wins for either accelerator, and no average benchmark scores. Both devices sit at the 50th percentile against all GPUs with an average benchmark score of 0. This means the quantitative comparison must rely entirely on the specification-level data provided.

The most significant performance differential appears in memory bandwidth. The MI350X's 8.19 TB/s represents a 4.01 times advantage over the H100 CNX's 2.04 TB/s. For memory-bound kernels that scale with bandwidth, this difference is transformative. Similarly, memory capacity favors the MI350X by 3.6 times, allowing it to hold 288 GB of data on-chip versus 80 GB for the H100 CNX.

In FP32 compute, the MI350X's 72.09 TFLOPS surpasses the H100 CNX's 53.84 TFLOPS by 33.9%. This advantage extends to texture throughput, where the MI350X delivers 2,252.8 GTexel/s against 841.3 GTexel/s, a 2.68 times margin. The MI350X also operates at higher clocks: 2200 MHz boost versus 1845 MHz boost, a 19.2% difference.

The FP16 comparison reverses the picture. The H100 CNX's 215.4 TFLOPS is 2.99 times higher than the MI350X's 72.09 TFLOPS. This stems from NVIDIA's tensor core design that achieves 4:1 FP16 to FP32 ratio, whereas AMD implements a 1:1 ratio. For AI training pipelines that rely on FP16 tensor operations, this gives the H100 CNX a substantial edge.

The H100 CNX also retains some graphics capability with 24 ROPs and 44.28 GPixel/s pixel rate, while the MI350X reports 0 MPixel/s. Neither card includes display outputs, and both use PCIe 5.0 x16 interfaces. The H100 CNX's production status is listed as Active, while the MI350X's production status is not recorded.

Power efficiency tells a nuanced story. The MI350X consumes 1000 W to achieve 72.09 TFLOPS FP32, yielding 72.09 GFLOPS per watt. The H100 CNX consumes 350 W for 53.84 TFLOPS FP32, yielding 153.83 GFLOPS per watt. The H100 CNX is more than twice as power-efficient in FP32. In FP16, the H100 CNX achieves 615.43 GFLOPS per watt versus the MI350X's 72.09 GFLOPS per watt, an 8.54 times efficiency advantage.

The physical design reflects different deployment targets. The MI350X's OAM module form factor, measuring 102 mm by 165 mm with no power connectors, is designed for proprietary baseboards with integrated power delivery. The H100 CNX's dual-slot, 267 mm by 111 mm design with an 8-pin EPS connector allows installation in standard servers with conventional power supplies. The MI350X requires a 1400 W system power supply, while the H100 CNX needs only 750 W.