AMD Instinct MI300 vs NVIDIA H200 NVL Comparison

Head-to-Head Benchmarks

The recorded database contains no head-to-head benchmark entries for the AMD Instinct MI300 against the NVIDIA H200 NVL. This absence is itself informative: the two accelerators have not been subjected to the same controlled test suite in our measurements, so any direct comparison must rely on the available specification data and the H200 NVL's single recorded benchmark result.

The NVIDIA H200 NVL posts an OpenCL score of 334,891 on Geekbench. That result places it at the 100th percentile of all GPUs in the database, meaning it outperforms every other recorded accelerator. Its nearest rival, the NVIDIA B200, scores 345,482, which is 3.1% higher. The AMD Instinct MI300X, a sibling of the MI300, scores 317,994, which is 5.3% lower than the H200 NVL. The NVIDIA B300 SXM6 AC leads the field at 369,831, 9.4% above the H200 NVL, while the NVIDIA L40S trails at 295,763, a 13.2% deficit.

The AMD Instinct MI300 has no benchmark scores in the database and thus no percentile ranking beyond the 50th percentile placeholder, which reflects the absence of recorded data rather than measured performance. The H200 NVL's 5.3% advantage over the MI300X suggests that the H200 NVL would likely edge out the MI300 in compute workloads, but the MI300X is not identical to the MI300. The MI300 carries 14080 shading units, while the MI300X remains unlisted in our records for that field.

Where Each One Wins

Without direct head-to-head results, the wins must be inferred from architectural and specification advantages. The NVIDIA H200 NVL holds the clear advantage in raw shader count and clock speeds. It runs 16,896 shading units at a base clock of 1365 MHz and a boost clock of 1785 MHz. The AMD Instinct MI300 counters with 14,080 shading units at a base clock of 1000 MHz and a boost of 1700 MHz. This translates into measured throughput differences: the H200 NVL delivers 60.32 TFLOPS of FP32 performance, while the MI300 delivers 47.87 TFLOPS. That is a 26% gap in favor of NVIDIA for single-precision floating-point work.

The MI300 fights back in memory capacity and bandwidth. It carries 128 GB of HBM3 across an 8192-bit bus, yielding 5.32 TB/s of bandwidth. The H200 NVL has 141 GB of HBM3e but a narrower 6144-bit bus, producing 4.89 TB/s. The MI300's bandwidth advantage is 8.8%, and it does so with a wider interface, which can benefit memory-bound workloads such as large matrix operations or data movement in inference tasks.

For FP16 compute, the architectures diverge sharply. The MI300 lists 47.87 TFLOPS with a 1:1 ratio to FP32, meaning it does not accelerate half-precision beyond its single-precision rate. The H200 NVL lists 120.6 TFLOPS with a 2:1 ratio, doubling its FP32 throughput. This gives the H200 NVL a 152% advantage in half-precision compute, a decisive edge for AI training and inference workloads that rely on FP16 or mixed-precision operations.

Texture and pixel rates also differ. The MI300 has 880 texture mapping units and a texture rate of 1,496.0 GTexel/s. The H200 NVL has 528 TMUs and a texture rate of 942.5 GTexel/s. The MI300 leads texture throughput by 58.7%, though neither card has display outputs, so pixel rate is irrelevant for visual output. The H200 NVL does have 24 ROPs and a pixel rate of 42.84 GPixel/s, while the MI300 lists 0 ROPs and 0 MPixel/s.

Architecture Differences

The two accelerators come from different architectural lineages. The AMD Instinct MI300 uses the CDNA 3.0 architecture, built on the Aqua Vanjaram chip. The NVIDIA H200 NVL uses the Hopper architecture, built on the GH100 chip. Both are fabricated on a 5 nm process at TSMC, but the transistor counts diverge sharply. The MI300 integrates 153,000 million transistors on a 1017 mm² die, giving a density of 150.4 million transistors per square millimeter. The H200 NVL integrates 80,000 million transistors on an 814 mm² die, with a density of 98.3 million per square millimeter.

The MI300's transistor count is 91.25% higher than the H200 NVL's, and its die is 24.9% larger. This suggests that AMD has packed significantly more logic into the MI300, likely for its larger memory bus and higher texture throughput. The transistor density difference, 150.4 versus 98.3 million per square millimeter, indicates that the MI300 uses a denser design, possibly with more specialized units per area.

The H200 NVL includes 528 tensor cores, a feature the MI300 does not list in the database. Tensor cores are specialized for matrix multiplication and are central to NVIDIA's AI compute strategy. The MI300's CDNA architecture does not have a tensor core field in our records, which may indicate a different approach to matrix math or simply a different naming convention.

Memory types also differ: the MI300 uses HBM3, while the H200 NVL uses HBM3e. The HBM3e standard offers higher per-stack data rates, which the H200 NVL exploits with a 6.4 Gbps effective memory clock versus the MI300's 5.2 Gbps. However, the MI300's wider bus compensates, resulting in its higher aggregate bandwidth. Both cards have the same power draw at 600 W TDP and the same suggested power supply at 1000 W, but the power connectors differ: the MI300 uses 2x 8-pin, while the H200 NVL uses a single 8-pin EPS.

Specification Differences

The two cards share several physical characteristics. Both are 267 mm in length and 111 mm in height, use a PCIe 5.0 x16 interface, and have no display outputs. The H200 NVL is dual-slot, while the MI300's slot width is not recorded. Both use TSMC as the foundry and a 5 nm process node.

Clock speeds differ in every field. The MI300 has a base clock of 1000 MHz, a boost clock of 1700 MHz, and a memory clock of 1300 MHz (5.2 Gbps effective). The H200 NVL has a base clock of 1365 MHz, a boost of 1785 MHz, and a memory clock of 1593 MHz (6.4 Gbps effective). The H200 NVL runs 36.5% higher at base and 5% higher at boost.

Memory capacity, type, bus width, and bandwidth all differ. The MI300 has 128 GB of HBM3 on an 8192-bit bus with 5.32 TB/s. The H200 NVL has 141 GB of HBM3e on a 6144-bit bus with 4.89 TB/s. The H200 NVL has 10.2% more capacity, but the MI300 has 8.8% more bandwidth.

Shading units, texture mapping units, and ROPs all differ. The MI300 has 14,080 shaders, 880 TMUs, and 0 ROPs. The H200 NVL has 16,896 shaders, 528 TMUs, and 24 ROPs. The H200 NVL has 20% more shaders, while the MI300 has 66.7% more TMUs.

Compute throughput differs across precision levels. FP32: 47.87 TFLOPS for the MI300 versus 60.32 TFLOPS for the H200 NVL. FP16: 47.87 TFLOPS for the MI300 versus 120.6 TFLOPS for the H200 NVL. The MI300's FP16 ratio is 1:1, while the H200 NVL's is 2:1.

Texture rate favors the MI300 at 1,496.0 GTexel/s versus 942.5 GTexel/s. Pixel rate favors the H200 NVL at 42.84 GPixel/s versus 0 MPixel/s. The MI300 has a 600 W TDP, as does the H200 NVL, but the MI300 uses 2x 8-pin power connectors while the H200 NVL uses an 8-pin EPS.

Release dates differ by nearly two years. The MI300 launched on 2023-01-03, while the H200 NVL launched on 2024-11-17. The H200 NVL has a production status of Active and lists its predecessor as Server Ada and successor as Server Blackwell. The MI300 lists its predecessor as Radeon Instinct, with no successor recorded.

FAQ

Q: Which accelerator has higher FP32 performance?

A: The NVIDIA H200 NVL delivers 60.32 TFLOPS of FP32 compute, which is 26% higher than the AMD Instinct MI300's 47.87 TFLOPS.

Q: Which card offers more memory bandwidth?

A: The AMD Instinct MI300 provides 5.32 TB/s of bandwidth over an 8192-bit HBM3 bus, while the NVIDIA H200 NVL provides 4.89 TB/s over a 6144-bit HBM3e bus. The MI300 leads by 8.8%.

Q: How do the FP16 capabilities compare?

A: The NVIDIA H200 NVL reaches 120.6 TFLOPS in FP16 with a 2:1 ratio to FP32. The AMD Instinct MI300 lists 47.87 TFLOPS in FP16 with a 1:1 ratio. The H200 NVL is 152% ahead.

Q: What is the transistor count difference?

A: The AMD Instinct MI300 integrates 153,000 million transistors on a 1017 mm² die, while the NVIDIA H200 NVL integrates 80,000 million on an 814 mm² die. The MI300 has 91.25% more transistors.

Q: Does either card support display outputs?

A: Neither card has display outputs. Both are server accelerators without video connectivity.

Q: What is the H200 NVL's benchmark percentile?

A: The NVIDIA H200 NVL scores 334,891 on Geekbench OpenCL, placing it at the 100th percentile of all GPUs in the database. Its nearest recorded rival, the NVIDIA B200, is 3.1% higher, and the AMD Instinct MI300X is 5.3% lower.

The Verdict

The data points to a clear split by workload type. For AI training and mixed-precision inference, the NVIDIA H200 NVL is the stronger choice. Its FP16 output of 120.6 TFLOPS is more than double the MI300's 47.87 TFLOPS, and it includes 528 tensor cores, which are absent from the MI300's recorded specifications. The H200 NVL also holds a 26% lead in FP32 compute, which benefits general compute tasks that do not use half-precision.

For memory-bound workloads and raw bandwidth, the AMD Instinct MI300 holds the advantage. Its 5.32 TB/s bandwidth exceeds the H200 NVL's 4.89 TB/s, and its 8192-bit bus is the widest in the comparison. The MI300 also has a 58.7% higher texture rate, which may benefit certain data-processing kernels, though the absence of ROPs and pixel output limits its utility for graphics-related tasks.

The physical design of the two cards is nearly identical: same length, height, TDP, and power supply requirement. Both use PCIe 5.0 x16. The MI300 uses more transistors and a larger die, while the H200 NVL uses a smaller die with higher clock speeds. The H200 NVL's production status is Active, and it has a defined successor in Server Blackwell, while the MI300's production status is unrecorded.

The benchmark evidence, though limited to a single H200 NVL score, supports NVIDIA's position. The H200 NVL's 5.3% lead over the MI300X, its closest AMD rival in the database, suggests that the MI300 would likely trail in raw compute. The MI300's specifications point to a design optimized for memory throughput, but the H200 NVL's higher shader count, clock speeds, and tensor core support give it the overall edge in the recorded data. Users with memory-intensive workloads should consider the MI300, while those prioritizing compute throughput and AI performance should select the H200 NVL.