AMD Instinct MI300X vs NVIDIA H200 NVL Comparison

The NVIDIA H200 NVL and AMD Instinct MI300X are both elite server accelerators, but the benchmark data separates them clearly. In the single available OpenCL test, the NVIDIA H200 NVL posts a score of 334,891, while the AMD Instinct MI300X trails at 317,994. This 5.3% delta gives the H200 NVL a definitive edge in raw compute throughput, though the MI300X counters with a substantial memory capacity advantage. Both cards sit at the 100th percentile against all GPUs, confirming they are top-tier hardware, but their architectural philosophies and resulting performance profiles differ significantly.

Where Each One Wins

The NVIDIA H200 NVL wins the only benchmark recorded in the data: the Geekbench OpenCL test. Its score of 334,891 places it 5.3% ahead of the MI300X, which scores 317,994. This advantage is consistent when viewed against the broader competitive field—the H200 NVL is 13.2% ahead of the NVIDIA L40S (295,763) and sits only 3.1% behind the NVIDIA B200 (345,482). For workloads that rely on general-purpose compute throughput, as measured by OpenCL, the H200 NVL is the stronger performer.

The AMD Instinct MI300X wins no benchmark in the head-to-head comparison. However, it wins on memory capacity by a wide margin. The MI300X offers 192 GB of HBM3 memory, compared to the H200 NVL’s 141 GB of HBM3e—a 36.2% capacity advantage. This makes the MI300X the logical choice for models or datasets that must reside entirely in high-bandwidth memory, even if its raw compute score is lower. The MI300X also delivers higher memory bandwidth at 5.32 TB/s versus the H200 NVL’s 4.89 TB/s, reinforcing its suitability for memory-bound workloads.

In practical terms, the H200 NVL wins compute-bound tasks like dense matrix operations or inference where the 60.32 TFLOPS FP32 rating (versus 81.72 TFLOPS for the MI300X) is less relevant than the actual OpenCL result. The MI300X wins capacity-bound scenarios, such as hosting very large language models or massive graph datasets, where its 192 GB frame buffer is decisive.

Architecture Differences

The two accelerators come from fundamentally different design lineages. The NVIDIA H200 NVL uses the GH100 chip, built on the Hopper architecture, and is manufactured on a 5 nm process at TSMC. It packs 80,000 million transistors into an 814 mm² die, yielding a transistor density of 98.3 million per square millimeter. The AMD Instinct MI300X uses the Aqua Vanjaram chip, based on CDNA 3.0 architecture, also on a 5 nm TSMC process, but scales dramatically larger: 153,000 million transistors on a 1017 mm² die, with a much higher density of 150.4 million per square millimeter.

Memory subsystems differ in type and organization. The H200 NVL uses HBM3e with a 6144-bit bus, while the MI300X uses HBM3 with a wider 8192-bit bus. Despite the H200 NVL’s newer memory type, the MI300X’s wider bus gives it higher peak bandwidth (5.32 TB/s versus 4.89 TB/s) and more total capacity (192 GB versus 141 GB). The H200 NVL runs its memory at 1593 MHz (6.4 Gbps effective), while the MI300X runs at 1300 MHz (5.2 Gbps effective)—a clock speed advantage that does not overcome the MI300X’s bus width.

Compute resources also diverge. The H200 NVL has 16,896 shading units, 528 TMUs, and 24 ROPs, alongside 528 tensor cores. The MI300X has more shading units (19,456) and more TMUs (1,216), but reports zero ROPs and no tensor core count. This explains why the MI300X posts a texture rate of 2,553.6 GTexel/s—more than double the H200 NVL’s 942.5 GTexel/s—while its pixel rate is listed as 0 MPixel/s. The H200 NVL’s pixel rate is 42.84 GPixel/s. Clock speeds tell a similar story: the H200 NVL has a lower boost clock (1785 MHz) than the MI300X (2100 MHz), but the NVIDIA card’s base clock is higher (1365 MHz versus 1000 MHz). The MI300X compensates with higher FP32 throughput (81.72 TFLOPS versus 60.32 TFLOPS), yet this does not translate into a higher OpenCL score.

Head-to-Head Benchmarks

The sole head-to-head result is the Geekbench OpenCL test, where the NVIDIA H200 NVL scores 334,891 against the AMD Instinct MI300X’s 317,994. This yields a 5.3% victory for NVIDIA. In the nearest-rival context, the H200 NVL’s margin over the MI300X is its smallest positive delta—it is more decisively ahead of the L40S (13.2%) but behind the B200 by 3.1% and the B300 SXM6 AC by 9.4%. The MI300X, viewing the same data from its side, shows a 5% deficit to the H200 NVL, an 8% deficit to the B200, and a 7.5% lead over the L40S.

The interesting nuance is the FP32 specification. The MI300X has a theoretical FP32 peak of 81.72 TFLOPS, which is 35.5% higher than the H200 NVL’s 60.32 TFLOPS. Yet the H200 NVL still wins the OpenCL benchmark by 5.3%. This suggests that the H200 NVL’s architecture, including its tensor cores and memory subsystem, is more efficiently utilized in the tested workload. The MI300X’s higher raw FP32 number does not translate into a higher real-world score, indicating that memory bandwidth and compute scheduling play a larger role in this benchmark than peak theoretical throughput.

The H200 NVL also demonstrates a distinct advantage in FP16 compute: 120.6 TFLOPS (2:1 ratio) versus the MI300X’s 81.72 TFLOPS (1:1 ratio). This 47.6% lead in half-precision throughput is significant for AI inference and training workloads that rely on FP16. Conversely, the MI300X’s FP16 is identical to its FP32, meaning it does not double throughput in half precision—a design choice that limits its appeal for certain machine learning tasks.

The Verdict

Based strictly on the data, the NVIDIA H200 NVL is the superior accelerator for compute performance. It wins the only recorded benchmark by 5.3%, and that win is reinforced by its higher FP16 throughput (120.6 TFLOPS versus 81.72 TFLOPS) and its tensor core presence—features the MI300X lacks entirely. The H200 NVL’s 60.32 TFLOPS FP32 is lower on paper, but the benchmark result shows that paper specs do not predict real performance. For users prioritizing OpenCL compute scores, the H200 NVL is the choice.

The AMD Instinct MI300X is the choice for memory capacity and bandwidth. Its 192 GB of HBM3 memory is 51 GB larger than the H200 NVL’s 141 GB, and its 5.32 TB/s bandwidth is 8.8% higher. For workloads where the entire model or dataset must fit in on-chip memory to avoid PCIe transfers, the MI300X’s capacity advantage is decisive, even if it loses the compute benchmark. The MI300X also has a much higher texture rate (2,553.6 GTexel/s versus 942.5 GTexel/s), though this is moot for accelerators without display outputs.

The H200 NVL also has a longer market presence—it released on 2024-11-17, nearly a year after the MI300X’s 2023-12-05 release—and has an active production status, while the MI300X’s production status is not listed. The H200 NVL is a dual-slot card requiring 8-pin EPS connectors and a 1000 W suggested PSU, whereas the MI300X is an OAM module with no power connectors and a 1150 W suggested PSU. Neither card offers display outputs, and both use PCIe 5.0 x16 interfaces. The H200 NVL’s 600 W TDP is lower than the MI300X’s 750 W, which may be relevant for dense server deployments.

FAQ

Q: Which GPU has a higher OpenCL benchmark score?

A: The NVIDIA H200 NVL scores 334,891, which is 5.3% higher than the AMD Instinct MI300X’s 317,994.

Q: How much memory does each GPU have?

A: The AMD Instinct MI300X has 192 GB of HBM3 memory, while the NVIDIA H200 NVL has 141 GB of HBM3e memory—a 51 GB difference in favor of AMD.

Q: Does the MI300X have tensor cores?

A: No tensor core count is listed for the AMD Instinct MI300X. The NVIDIA H200 NVL has 528 tensor cores.

Q: Which GPU has higher memory bandwidth?

A: The AMD Instinct MI300X has a bandwidth of 5.32 TB/s, which is higher than the NVIDIA H200 NVL’s 4.89 TB/s.

Q: What is the FP32 performance difference?

A: The AMD Instinct MI300X has a theoretical FP32 throughput of 81.72 TFLOPS, which is higher than the NVIDIA H200 NVL’s 60.32 TFLOPS, but the H200 NVL still wins the OpenCL benchmark.

Q: Are these GPUs suitable for display output?

A: No. Both the NVIDIA H200 NVL and AMD Instinct MI300X have no display outputs, as they are designed for server compute workloads.