AMD Instinct MI300 vs NVIDIA H100 CNX Comparison

Head-to-Head Benchmarks

The recorded database contains no direct head-to-head benchmark results for these two accelerators. Both products show an empty benchmark array, and the win counter stands at zero for each side. This absence of measured performance data means the comparison must rely entirely on the architectural specifications and compute characteristics listed in the database.

Without benchmark scores, the FP32 throughput figures offer the only direct compute comparison. The NVIDIA H100 CNX records 53.84 TFLOPS of FP32 performance, while the AMD Instinct MI300 shows 47.87 TFLOPS. This places the H100 CNX ahead by roughly 12.5% in single-precision floating-point work, a meaningful margin for scientific computing and general GPU compute tasks that rely on FP32 math.

The FP16 picture shifts significantly. The H100 CNX delivers 215.4 TFLOPS with a 4:1 ratio, indicating it uses a tensor-core accelerated path for half-precision workloads. The MI300 provides 47.87 TFLOPS at a 1:1 ratio, meaning its FP16 throughput matches its FP32 rate without a dedicated acceleration multiplier. The H100 CNX therefore holds a 4.5x advantage in FP16 compute density, a decisive gap for AI training and inference workloads that predominantly use mixed-precision formats.

Texture and pixel processing further differentiate the two. The MI300 posts a texture rate of 1,496.0 GTexel/s against 841.3 GTexel/s for the H100 CNX, giving AMD a 78% lead in texture fill. The MI300 also reports a pixel rate of 0 MPixel/s, while the H100 CNX manages 44.28 GPixel/s. The MI300's zero pixel rate indicates it has no ROP units, a common trait for compute-only accelerators, whereas the H100 CNX includes 24 ROPs and can perform rasterization work.

Memory bandwidth heavily favors the MI300. It records 5.32 TB/s of bandwidth from 128 GB of HBM3 across an 8192-bit bus. The H100 CNX offers 2.04 TB/s from 80 GB of HBM2e on a 5120-bit bus. The MI300's bandwidth advantage stands at 2.6x, a critical factor for memory-bound workloads such as large language model inference, graph analytics, and sparse matrix operations where data movement often limits performance.

The percentile ranking places both parts at the 50th percentile against all GPUs in the database, an unusual parity given their different specifications. This suggests the database's percentile field may reflect market positioning or availability rather than raw compute capability, as the specification differences clearly separate the two in every measured category except FP32, where they remain relatively close.

Architecture Differences

The MI300 uses the CDNA 3.0 architecture built on AMD's Aqua Vanjaram chip, while the H100 CNX relies on NVIDIA's Hopper architecture with the GH100 die. Both are fabricated on a 5 nm process at TSMC, according to the database. The MI300 integrates 153,000 million transistors across a 1017 mm² die, yielding a transistor density of 150.4M per mm². The H100 CNX contains 80,000 million transistors on an 814 mm² die, for a density of 98.3M per mm². The MI300 packs 91% more transistors into a 25% larger die, indicating a significantly more dense implementation.

The MI300 features 14,080 shading units, 880 texture mapping units, and no ROPs or tensor cores listed. The H100 CNX carries 14,592 shading units, 456 TMUs, 24 ROPs, and 456 tensor cores. Shader counts sit within 4% of each other, but the MI300 nearly doubles the TMU count. The H100 CNX's tensor cores explain its FP16 advantage, as these specialized units perform matrix math at rates far exceeding the general-purpose shader pipeline.

Memory architecture differs fundamentally. The MI300 uses HBM3 at 1300 MHz with a 5.2 Gbps effective data rate across an 8192-bit interface, achieving 5.32 TB/s. The H100 CNX uses HBM2e at 1593 MHz with a 3.2 Gbps effective rate across a 5120-bit bus, reaching 2.04 TB/s. The newer HBM3 standard and wider bus give the MI300 a substantial memory advantage, though the H100 CNX's smaller 80 GB capacity may be sufficient for many workloads while consuming less power.

Clock behavior diverges as well. The MI300 bases at 1000 MHz and boosts to 1700 MHz. The H100 CNX bases at a much lower 690 MHz but boosts higher to 1845 MHz. This suggests different power management strategies, with the MI300 running at a higher sustained baseline and the H100 CNX relying on aggressive boost behavior. The MI300's 600 W TDP exceeds the H100 CNX's 350 W TDP, a 250 W gap that reflects the MI300's larger transistor count and higher memory bandwidth.

Power delivery also differs. The MI300 requires 2x 8-pin connectors and a suggested 1000 W power supply, while the H100 CNX uses a single 8-pin EPS connector with a 750 W suggested PSU. Both cards share identical dimensions at 267 mm in length and 111 mm in height. The H100 CNX is explicitly listed as dual-slot, and the MI300's slot width is not recorded. Neither card offers display outputs, confirming their server accelerator roles.

Release timing shows the MI300 arriving first on January 3, 2023, with the H100 CNX following on March 20, 2023. The H100 CNX's production status is listed as active, while the MI300's status is not recorded. The MI300's predecessor is the Radeon Instinct series, and the H100 CNX's predecessor is listed as Server Ada, with its successor being Server Blackwell.

The Verdict

The data indicates two accelerators designed for different compute priorities. The H100 CNX leads in FP32 throughput by 12.5% and dominates FP16 work by a 4.5x margin, thanks to its tensor cores. The MI300 counters with a 2.6x memory bandwidth advantage, double the texture rate, and 60% more memory capacity. Neither part has benchmark scores in the database, so performance conclusions must rest on these specification-level comparisons.

For FP32-heavy workloads such as traditional HPC simulations, climate modeling, or physics-based rendering, the H100 CNX's 53.84 TFLOPS provides a measurable edge. For mixed-precision AI workloads, including transformer training and inference, the H100 CNX's 215.4 TFLOPS FP16 capability makes it the clear choice when tensor operations dominate. The MI300's 47.87 TFLOPS FP16 at 1:1 ratio does not offer the same accelerated path.

For memory-bound applications, the MI300's 5.32 TB/s bandwidth and 128 GB capacity present a compelling alternative. Large batch inference, graph neural networks, and workloads with massive working sets benefit from the higher bandwidth and larger pool. The MI300 also draws 250 W more power, which may constrain deployment density in power-limited environments.

The 50th percentile ranking for both parts suggests the database treats them as equivalent overall performers, but the specification split tells a more nuanced story. The H100 CNX excels where tensor math and FP32 compute dominate, while the MI300 wins where memory bandwidth and texture throughput matter most.

Specification Differences

| Specification | AMD Instinct MI300 | NVIDIA H100 CNX |

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

| Architecture | CDNA 3.0 | Hopper |

| Process node | 5 nm | 5 nm |

| Transistors | 153,000 million | 80,000 million |

| Die size | 1017 mm² | 814 mm² |

| Transistor density | 150.4M / mm² | 98.3M / mm² |

| Base clock | 1000 MHz | 690 MHz |

| Boost clock | 1700 MHz | 1845 MHz |

| Memory size | 128 GB | 80 GB |

| Memory type | HBM3 | HBM2e |

| Memory bus width | 8192 bit | 5120 bit |

| Memory bandwidth | 5.32 TB/s | 2.04 TB/s |

| Shading units | 14,080 | 14,592 |

| TMUs | 880 | 456 |

| ROPs | 0 | 24 |

| Tensor cores | N/A | 456 |

| FP32 performance | 47.87 TFLOPS | 53.84 TFLOPS |

| FP16 performance | 47.87 TFLOPS (1:1) | 215.4 TFLOPS (4:1) |

| Texture rate | 1,496.0 GTexel/s | 841.3 GTexel/s |

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

| TDP | 600 W | 350 W |

| Power connectors | 2x 8-pin | 8-pin EPS |

| Suggested PSU | 1000 W | 750 W |

| Release date | 2023-01-03 | 2023-03-20 |

| Production status | Not recorded | Active |

The MI300 and H100 CNX share the same 5 nm TSMC process, PCIe 5.0 x16 interface, and physical dimensions. They differ in every other recorded specification except for the absence of display outputs and undefined API support levels.

FAQ

Q: Which accelerator has higher FP32 compute performance?

A: The NVIDIA H100 CNX records 53.84 TFLOPS, which is 12.5% higher than the AMD Instinct MI300's 47.87 TFLOPS.

Q: How do the two compare in FP16 performance?

A: The H100 CNX delivers 215.4 TFLOPS at a 4:1 ratio, while the MI300 provides 47.87 TFLOPS at 1:1. The H100 CNX offers 4.5x more FP16 throughput.

Q: What is the memory bandwidth difference?

A: The MI300 has 5.32 TB/s from 128 GB of HBM3, while the H100 CNX has 2.04 TB/s from 80 GB of HBM2e. The MI300 provides 2.6x more bandwidth.

Q: Which card has more transistors?

A: The MI300 contains 153,000 million transistors, compared to 80,000 million for the H100 CNX, a 91% higher count.

Q: What are the power requirements?

A: The MI300 has a 600 W TDP with 2x 8-pin connectors and a 1000 W suggested PSU. The H100 CNX has a 350 W TDP with one 8-pin EPS connector and a 750 W suggested PSU.

Q: Do either of these cards support display output?

A: No. Both the MI300 and H100 CNX list "No outputs" for display connectivity.

Where Each One Wins

The H100 CNX wins in compute throughput. Its 53.84 TFLOPS FP32 exceeds the MI300's 47.87 TFLOPS, and its 215.4 TFLOPS FP16 with tensor cores dwarfs the MI300's 47.87 TFLOPS. Applications that rely heavily on dense matrix multiplication, such as neural network training, natural language processing, and computer vision model development, favor the H100 CNX. Its 456 tensor cores provide the dedicated hardware for these operations, and the 4:1 FP16 ratio indicates an efficient mixed-precision pipeline. The H100 CNX also offers pixel processing at 44.28 GPixel/s, which the MI300 cannot match due to its zero ROP count.

The MI300 wins in memory and texture throughput. Its 5.32 TB/s bandwidth more than doubles the H100 CNX's 2.04 TB/s, and its 128 GB capacity exceeds the H100 CNX's 80 GB by 60%. The MI300's 1,496.0 GTexel/s texture rate is 78% higher than the H100 CNX's 841.3 GTexel/s. Workloads that stream large datasets, perform sparse operations, or require frequent memory access patterns benefit from the MI300's wider HBM3 bus. Its higher shading unit count of 14,080 versus 14,592 is nearly identical, so shader-bound workloads would see little difference, but the MI300's extra TMUs and bandwidth support texture-heavy and memory-intensive compute tasks.

The power profile favors the H100 CNX. Its 350 W TDP versus the MI300's 600 W means the H100 CNX delivers its FP32 advantage at 42% lower power. For data centers with strict power budgets or cooling limits, the H100 CNX provides more compute per watt in FP32 and FP16 workloads. The MI300's higher power draw and 1000 W suggested PSU reflect its larger transistor count and memory subsystem, which may require more substantial infrastructure.

The release timeline slightly favors the MI300, which appeared on January 3, 2023, about two and a half months before the H100 CNX on March 20, 2023. The H100 CNX is listed as active in production, while the MI300's status remains unrecorded in the database. The H100 CNX also has a documented successor in Server Blackwell, while no successor is listed for the MI300.

For organizations choosing between these accelerators, the decision depends on workload composition. Tensor-heavy AI pipelines point to the H100 CNX. Memory-bound scientific computing and large-scale data processing point to the MI300. The 50th percentile ranking for both parts in the database suggests they occupy similar market tiers, but the specification data reveals distinct strengths that would drive selection in practice.