AMD Instinct MI350P vs NVIDIA H20 NVL16 Comparison

Head-to-Head Benchmarks

The recorded data for the AMD Instinct MI350P and the NVIDIA H20 NVL16 contains no completed benchmark runs, leaving both head-to-head results and individual scores empty. The database shows zero wins for either part in direct comparison, and the average benchmark score for each is recorded as 0. This absence of measured outcomes means the customary numeric comparison of performance, whether in compute throughput, memory bandwidth, or application-specific tasks, cannot be drawn from the current dataset. The percentile rank for both GPUs sits at 50, indicating no differentiation based on recorded testing.

What the data does provide is theoretical peak capability, which serves as a substitute for direct measurement until benchmark submissions populate. The AMD Instinct MI350P carries a boost clock of 2200 MHz, while the NVIDIA H20 NVL16 boosts to 1980 MHz. In raw FP32 throughput, NVIDIA posts 39.54 TFLOPS against AMD's 36.04 TFLOPS, a gap of roughly 3.5 TFLOPS in favor of the H20. For FP16, the NVIDIA part reaches 79.07 TFLOPS using a 2:1 ratio, whereas the AMD accelerator delivers 36.04 TFLOPS at a 1:1 ratio, meaning the H20 offers more than double the half-precision throughput. Memory bandwidth tells the opposite story: the MI350P reaches 8.19 TB/s from its HBM3e stack, while the H20 NVL16 provides 4.03 TB/s over HBM3. That is a 4.16 TB/s advantage for AMD, which represents a substantial margin for data movement.

Texture rate also splits the pair. AMD records 1,126.4 GTexel/s, nearly double NVIDIA's 617.8 GTexel/s. Pixel rate, however, is effectively absent on the MI350P at 0 MPixel/s, while the H20 NVL16 manages 47.52 GPixel/s. These theoretical figures indicate that neither part uniformly dominates; each leads in different domains, which is typical for accelerators aimed at distinct workloads.

Where Each One Wins

Based on the recorded specifications, the NVIDIA H20 NVL16 wins in compute-heavy operations that rely on FP16 tensor work. Its 79.07 TFLOPS half-precision output, combined with 312 tensor cores, positions it for machine learning training and inference tasks that use mixed-precision arithmetic. The FP32 count of 39.54 TFLOPS also edges out AMD's 36.04 TFLOPS, though the margin is narrow. The H20 also holds a clear advantage in pixel processing, with 47.52 GPixel/s versus 0 MPixel/s, making it the only part in this comparison with any rasterization capability, even if minimal.

The AMD Instinct MI350P wins in memory-centric workloads. The 8.19 TB/s bandwidth is double the H20's 4.03 TB/s, which directly benefits large language model inference, graph analytics, and other memory-bound operations. The MI350P also leads in texture throughput at 1,126.4 GTexel/s, a 508.6 GTexel/s advantage, which can aid in certain compute shader and image processing tasks. Its 144 GB of memory versus 96 GB provides additional capacity for datasets that exceed the NVIDIA part's limit.

For raw FP16 without tensor cores, the MI350P's 36.04 TFLOPS at 1:1 ratio is lower than the H20's 79.07 TFLOPS at 2:1, so NVIDIA wins that specific metric decisively. However, the AMD part's memory bandwidth and capacity make it the stronger candidate for workloads where data residency and transfer speed dominate over raw FLOP counts. The data indicates a trade-off: NVIDIA for compute density, AMD for memory throughput.

Architecture Differences

The two accelerators come from different architectural generations. AMD uses CDNA 4.0, while NVIDIA is built on Hopper. Both are fabricated by TSMC, but the node sizes differ: AMD uses a 3 nm process, NVIDIA uses 5 nm. Transistor counts are close, with NVIDIA at 80,000 million and AMD at 73,000 million. Die size, however, favors AMD in physical terms at 1190 mm² versus 814 mm², even though it packs fewer transistors. This yields a transistor density of 61.3M per mm² for AMD and 98.3M per mm² for NVIDIA, indicating the Hopper design uses its silicon more efficiently in terms of transistor packing.

The MI350P integrates 8192 shading units and 512 texture mapping units, with no ROPs recorded. The H20 NVL16 has 9984 shading units, 312 TMUs, and 24 ROPs. NVIDIA also includes 312 tensor cores, a feature AMD does not list for the MI350P. The presence of tensor cores on the H20 explains its FP16 advantage, since those units are designed for matrix operations. AMD's CDNA architecture, by contrast, appears to rely on general-purpose shader units for both FP32 and FP16, which aligns with the 1:1 ratio recorded for the MI350P.

Memory architecture differs fundamentally. AMD pairs 144 GB of HBM3e on a 8192-bit bus with an effective 8 Gbps per pin, yielding 8.19 TB/s. NVIDIA pairs 96 GB of HBM3 on a 6144-bit bus with 5.3 Gbps effective, yielding 4.03 TB/s. The wider bus and newer memory type give AMD a clear structural advantage in bandwidth, while NVIDIA compensates with a higher clocked memory controller relative to its bus width. Clock speeds also diverge: AMD runs at 1000 MHz base and 2200 MHz boost, NVIDIA at 1830 MHz base and 1980 MHz boost. The higher base clock on NVIDIA suggests better sustained performance at lower load levels.

Power and packaging also differ. AMD consumes 600 W TDP with a dual-slot design and a single 16-pin power connector, requiring a 1000 W suggested PSU. NVIDIA consumes 400 W TDP in an SXM module form factor with an 800 W suggested PSU and no separate power connector listed. The SXM module is designed for server blade integration, while the dual-slot card can fit standard PCIe slots, though both use a PCIe 5.0 x16 interface. Release dates place NVIDIA earlier, with production status marked Active, while AMD's status is not recorded; AMD lists a release date in May 2026, NVIDIA in September 2025.

Specification Differences

The table below highlights only the fields where the two parts differ:

| Field | AMD Instinct MI350P | NVIDIA H20 NVL16 |

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

| Architecture | CDNA 4.0 | Hopper |

| Process node | 3 nm | 5 nm |

| Transistors | 73,000 million | 80,000 million |

| Die size | 1190 mm² | 814 mm² |

| Transistor density | 61.3M / mm² | 98.3M / mm² |

| Base clock | 1000 MHz | 1830 MHz |

| Boost clock | 2200 MHz | 1980 MHz |

| Memory clock | 2000 MHz, 8 Gbps effective | 1313 MHz, 5.3 Gbps effective |

| Memory size | 144 GB | 96 GB |

| Memory type | HBM3e | HBM3 |

| Memory bus width | 8192 bit | 6144 bit |

| Memory bandwidth | 8.19 TB/s | 4.03 TB/s |

| Shading units | 8192 | 9984 |

| TMUs | 512 | 312 |

| ROPs | 0 | 24 |

| Tensor cores | Not listed | 312 |

| Pixel rate | 0 MPixel/s | 47.52 GPixel/s |

| Texture rate | 1,126.4 GTexel/s | 617.8 GTexel/s |

| FP32 | 36.04 TFLOPS | 39.54 TFLOPS |

| FP16 | 36.04 TFLOPS (1:1) | 79.07 TFLOPS (2:1) |

| TDP | 600 W | 400 W |

| Slot width | Dual-slot | SXM Module |

| Power connectors | 1x 16-pin | Not listed |

| Suggested PSU | 1000 W | 800 W |

| Dimensions | 267 mm length, 111 mm height, 40 mm width | Not listed |

| Production status | Not recorded | Active |

| Release date | May 6, 2026 | September 1, 2025 |

| Predecessor | Radeon Instinct | Server Ada |

| Successor | Not recorded | Server Blackwell |

Both use PCIe 5.0 x16 and have no display outputs. Neither lists RT cores. Neither provides a launch MSRP in the database. The NVIDIA part explicitly lists a successor in Server Blackwell, while AMD's successor field is empty.

FAQ

Q: Which accelerator has higher FP32 throughput?

A: The NVIDIA H20 NVL16 leads with 39.54 TFLOPS FP32, compared to the AMD Instinct MI350P's 36.04 TFLOPS.

Q: How much more memory bandwidth does the AMD part offer?

A: The MI350P provides 8.19 TB/s, which is 4.16 TB/s more than the H20 NVL16's 4.03 TB/s.

Q: Does either GPU support tensor operations?

A: Only the NVIDIA H20 NVL16 lists 312 tensor cores. The AMD MI350P does not list tensor cores in the database.

Q: What is the difference in power consumption?

A: The MI350P has a 600 W TDP, while the H20 NVL16 has a 400 W TDP. Suggested PSU ratings are 1000 W and 800 W, respectively.

Q: Which has a larger memory capacity?

A: The AMD MI350P holds 144 GB of HBM3e, while the NVIDIA H20 NVL16 holds 96 GB of HBM3.

Q: Are there any display outputs on either card?

A: No. Both the MI350P and H20 NVL16 record "No outputs" for display connections.

Q: What process nodes are used?

A: AMD uses a 3 nm TSMC process, while NVIDIA uses a 5 nm TSMC process. Transistor densities are 61.3M per mm² and 98.3M per mm², respectively.

The Verdict

The database currently records no measured benchmark scores for either the AMD Instinct MI350P or the NVIDIA H20 NVL16, so any verdict must rest on architectural specifications alone. For workloads that prioritize raw compute throughput, particularly FP16 operations common in AI training, the NVIDIA H20 NVL16 is the stronger choice based on its 79.07 TFLOPS FP16 and 39.54 TFLOPS FP32, both exceeding the AMD part. The inclusion of 312 tensor cores gives NVIDIA a dedicated path for matrix math that AMD does not list.

For workloads that depend on memory bandwidth and capacity, the AMD Instinct MI350P is the clear leader. Its 8.19 TB/s bandwidth and 144 GB capacity surpass the H20's 4.03 TB/s and 96 GB, making it better suited for large-scale inference, data analytics, and any operation where data movement is the bottleneck. The MI350P also uses a newer 3 nm process, which suggests improved transistor efficiency per area, though its lower transistor density indicates a less packed design.

The H20 NVL16 has a lower TDP at 400 W versus 600 W, and it is available as an active production part with a release date in September 2025. The MI350P is not marked as active and has a later release date of May 2026. For buyers needing immediate availability and lower power draw, the NVIDIA part holds the edge. For those prioritizing memory throughput and capacity, the AMD part delivers more on paper.

Neither accelerator provides display outputs, ruling out any graphics use case. The choice reduces to compute versus memory. NVIDIA wins compute, AMD wins memory. Without benchmark results, the database cannot assign a performance winner, but the specification split is unambiguous.