AMD Instinct MI350X vs NVIDIA H800 SXM5 Comparison

The Verdict

The database records two fundamentally different server accelerators. The AMD Instinct MI350X and the NVIDIA H800 SXM5 occupy distinct positions in the accelerator landscape, and the recorded specifications indicate that each device serves a different primary workload profile.

For organizations prioritizing massive memory capacity and raw FP32 throughput, the AMD Instinct MI350X shows a clear advantage. Its 288 GB of HBM3e memory with 8.19 TB/s of bandwidth dwarfs the H800's 80 GB HBM3 configuration, which delivers 3.36 TB/s. The MI350X also posts higher FP32 compute at 72.09 TFLOPS compared to 59.30 TFLOPS for the H800. This combination points toward workloads where large model residency and single-precision math dominate.

For workloads that rely on mixed-precision tensor operations, the NVIDIA H800 SXM5 holds the advantage. Its FP16 throughput of 237.2 TFLOPS (4:1 ratio) exceeds the MI350X's 72.09 TFLOPS (1:1 ratio) by a substantial margin. The H800 also includes 528 dedicated tensor cores, a feature absent from the MI350X's recorded specification, making it the more suitable choice for transformer-style training and inference loops that leverage tensor core acceleration.

The data does not declare a single winner. The MI350X wins on memory capacity, memory bandwidth, texture rate, and FP32 compute. The H800 wins on FP16 throughput, transistor density, pixel rate, and includes tensor cores. The choice depends entirely on whether the workload is memory-bound single-precision math or tensor-core-accelerated mixed-precision computation.

Where Each One Wins

The AMD Instinct MI350X demonstrates dominance in several measurable categories. Memory capacity is the most striking gap: 288 GB versus 80 GB, a 3.6 times difference. Memory bandwidth follows the same pattern, with 8.19 TB/s versus 3.36 TB/s, or roughly 2.4 times the H800's bandwidth. These figures suggest the MI350X can hold larger models or datasets in on-device memory, reducing the need for frequent host-device transfers.

Texture rate also favors the MI350X at 2,252.8 GTexel/s, compared to 926.6 GTexel/s for the H800. The MI350X ships with 1,024 texture mapping units versus 528 on the H800, which explains the texture rate gap. FP32 compute favors the MI350X as well: 72.09 TFLOPS versus 59.30 TFLOPS, a 21.6% advantage.

The NVIDIA H800 SXM5 wins in mixed-precision tensor performance. Its FP16 output of 237.2 TFLOPS is more than three times the MI350X's 72.09 TFLOPS. This is the largest single performance gap between the two devices in any compute category. The H800 also has a higher transistor density at 98.3M transistors per mm² versus 77.7M for the MI350X, though the MI350X uses a smaller 3 nm process node compared to 5 nm for the H800.

Pixel rate is another H800 advantage: 42.12 GPixel/s versus 0 MPixel/s for the MI350X. The MI350X records zero ROPs, which explains the zero pixel rate. This is not necessarily a disadvantage in compute-focused accelerator workloads, but the specification difference is recorded and notable.

The H800 also draws less power. Its TDP is 700 W with a suggested PSU of 1100 W, while the MI350X has a 1000 W TDP and requires a 1400 W suggested PSU. The H800 uses an 8-pin EPS power connector, while the MI350X lists no power connectors on its OAM module form factor.

Architecture Differences

The MI350X uses the CDNA 4.0 architecture on a 3 nm process node fabricated by TSMC. It integrates 185,000 million transistors on a 2380 mm² die, resulting in a transistor density of 77.7M per mm². The chip is designated MI350 256CU, indicating a large compute unit count. The H800 uses the Hopper architecture on a 5 nm TSMC process, with 80,000 million transistors on a 814 mm² die, yielding a higher density of 98.3M per mm². The H800's chip is designated GH100.

Memory technology differs substantially. The MI350X uses HBM3e with an 8192-bit bus, while the H800 uses HBM3 with a 5120-bit bus. The MI350X memory clock runs at 2000 MHz with 8 Gbps effective speed, while the H800 memory runs at 1313 MHz with 5.3 Gbps effective. These differences produce the bandwidth gap already noted: 8.19 TB/s versus 3.36 TB/s.

Shader configuration differs in both count and organization. The MI350X has 16,384 shading units and 1,024 TMUs with zero ROPs. The H800 has 16,896 shading units, 528 TMUs, and 24 ROPs. The H800 also includes 528 tensor cores; the MI350X records no tensor core count. The MI350X's FP16 throughput matches its FP32 throughput exactly (72.09 TFLOPS for both), indicating a 1:1 ratio. The H800's FP16 is 4:1 relative to its FP32, producing 237.2 TFLOPS.

Clock speeds favor the H800 in base frequency and the MI350X in boost frequency. The H800 has a 1095 MHz base clock and 1755 MHz boost, while the MI350X has a 1000 MHz base and 2200 MHz boost. The higher boost clock on the MI350X contributes to its FP32 advantage despite having slightly fewer shading units.

Form factors differ as well. The MI350X is an OAM module measuring 102 mm by 165 mm, while the H800 is an SXM module with no recorded dimensions. Both use PCIe 5.0 x16 bus interfaces and have no display outputs. Neither device exposes DirectX, OpenGL, or Vulkan APIs; the MI350X lists these as N/A, while the H800 leaves them null.

Power delivery differs in connector type. The MI350X uses no power connectors because the OAM module receives power through its socket. The H800 uses an 8-pin EPS connector. Suggested PSU ratings are 1400 W for the MI350X and 1100 W for the H800.

FAQ

Q: Which accelerator has more memory?

A: The AMD Instinct MI350X has 288 GB of HBM3e memory, compared to 80 GB of HBM3 on the NVIDIA H800 SXM5.

Q: Which device delivers higher FP32 performance?

A: The MI350X records 72.09 TFLOPS FP32, which is 21.6% higher than the H800's 59.30 TFLOPS.

Q: Which accelerator is better for FP16 tensor workloads?

A: The H800 SXM5 delivers 237.2 TFLOPS FP16 (4:1 ratio), more than three times the MI350X's 72.09 TFLOPS (1:1 ratio). The H800 also has 528 tensor cores, while the MI350X has no recorded tensor core count.

Q: What is the memory bandwidth difference?

A: The MI350X provides 8.19 TB/s over an 8192-bit bus with HBM3e. The H800 provides 3.36 TB/s over a 5120-bit bus with HBM3.

Q: Which device has a smaller manufacturing process?

A: The MI350X uses a 3 nm process node from TSMC, while the H800 uses a 5 nm process node, also from TSMC.

Q: What are the power requirements?

A: The MI350X has a 1000 W TDP and a suggested PSU of 1400 W. The H800 has a 700 W TDP and a suggested PSU of 1100 W.

Head-to-Head Benchmarks

The database contains no direct head-to-head benchmark entries between these two devices. Both record zero benchmark scores, zero wins in their respective columns, and no nearest rival entries. The analysis therefore relies entirely on the recorded specification data, which provides clear quantitative comparisons across multiple dimensions.

The largest single-category advantage belongs to the H800 in FP16 throughput. At 237.2 TFLOPS, it exceeds the MI350X's 72.09 TFLOPS by 165.11 TFLOPS, or roughly 3.3 times. This gap is significant for any workload that can use tensor cores for FP16 matrix operations, which includes many modern deep learning training and inference kernels.

The MI350X counters with its memory capacity advantage. At 288 GB versus 80 GB, the MI350X holds 208 GB more memory, a 3.6 times difference. For model sizes that exceed 80 GB, the H800 would require model sharding or host memory spillover, while the MI350X can hold the entire model on-device. The bandwidth difference reinforces this: 8.19 TB/s versus 3.36 TB/s means the MI350X can feed its compute units with data roughly 2.4 times faster.

FP32 compute favors the MI350X by 12.79 TFLOPS, from 59.30 to 72.09 TFLOPS. This 21.6% advantage applies to any workload that operates in single precision without tensor core acceleration. The texture rate gap is even larger in relative terms: 2,252.8 GTexel/s versus 926.6 GTexel/s, a 2.4 times difference that stems from the MI350X's 1,024 TMUs versus 528 on the H800.

The H800 wins on pixel rate, though the practical significance is limited. Its 42.12 GPixel/s comes from 24 ROPs, while the MI350X records 0 ROPs and 0 MPixel/s. For compute accelerators with no display outputs, pixel rate rarely appears in workload requirements, but the specification difference is recorded.

Transistor density favors the H800 at 98.3M per mm² versus 77.7M per mm². Despite this, the MI350X packs more total transistors: 185,000 million versus 80,000 million. The MI350X die is nearly three times larger at 2380 mm² versus 814 mm², which explains the higher total transistor count despite lower density.

Clock behavior differs in both base and boost. The H800 starts at 1095 MHz and reaches 1755 MHz, while the MI350X starts at 1000 MHz and reaches 2200 MHz. The MI350X's 445 MHz higher boost clock likely contributes to its FP32 and texture rate advantages, given the relatively similar shading unit counts (16,384 versus 16,896).

Specification Differences

The following fields differ between the two devices in the database:

• Architecture: CDNA 4.0 (MI350X) versus Hopper (H800)
• Chip: MI350 256CU versus GH100
• Process node: 3 nm versus 5 nm
• Transistors: 185,000 million versus 80,000 million
• Die size: 2380 mm² versus 814 mm²
• Transistor density: 77.7M per mm² versus 98.3M per mm²
• Base clock: 1000 MHz versus 1095 MHz
• Boost clock: 2200 MHz versus 1755 MHz
• Memory clock: 2000 MHz 8 Gbps effective versus 1313 MHz 5.3 Gbps effective
• Memory size: 288 GB versus 80 GB
• Memory type: HBM3e versus HBM3
• Memory bus width: 8192 bit versus 5120 bit
• Memory bandwidth: 8.19 TB/s versus 3.36 TB/s
• Shading units: 16,384 versus 16,896
• TMUs: 1,024 versus 528
• ROPs: 0 versus 24
• Tensor cores: not recorded versus 528
• Pixel rate: 0 MPixel/s versus 42.12 GPixel/s
• Texture rate: 2,252.8 GTexel/s versus 926.6 GTexel/s
• FP32: 72.09 TFLOPS versus 59.30 TFLOPS
• FP16: 72.09 TFLOPS (1:1) versus 237.2 TFLOPS (4:1)
• TDP: 1000 W versus 700 W
• Slot width: OAM Module versus SXM Module
• Power connectors: None versus 8-pin EPS
• Suggested PSU: 1400 W versus 1100 W
• Dimensions: 102 mm by 165 mm versus not recorded
• Release date: 2025-06-11 versus 2023-03-20
• Production status: not recorded versus Active
• Predecessor: Radeon Instinct versus Server Ada
• Successor: not recorded versus Server Blackwell
• APIs: DirectX, OpenGL, Vulkan all N/A versus all null

Shared characteristics include PCIe 5.0 x16 bus interface, no display outputs, TSMC as foundry, and no launch MSRP recorded for either device. Both list a 50th percentile versus all GPUs and an average benchmark score of zero, reflecting the absence of recorded benchmark entries in the database.