AMD Instinct MI300A vs NVIDIA H800 SXM5 Comparison

Head-to-Head Benchmarks

The database contains no recorded benchmark scores for either the AMD Instinct MI300A or the NVIDIA H800 SXM5. Both entries show an average benchmark score of 0, and the head-to-head benchmark list is empty. Consequently, there are zero wins recorded for each accelerator, and neither part achieves a percentile ranking above 50 when compared against all GPUs in the database.

This absence of measured data means that direct performance comparisons cannot be made using synthetic workloads, compute kernels, or AI inference tests. The recorded information instead relies entirely on their listed specifications, which provide a structural basis for understanding their relative capabilities. The lack of benchmark results is notable, because both accelerators are designed for high-performance computing and data center workloads, where measurable throughput is typically the deciding factor. Without those numbers, the analysis shifts to architectural traits and theoretical peak rates.

What can be stated from the database is that the MI300A reaches a higher FP32 peak of 61.29 TFLOPS, while the H800 SXM5 reaches 59.30 TFLOPS in FP32. That is a lead of roughly 1.99 TFLOPS, a modest margin of about 3.4 percent. The MI300A also delivers a texture rate of 1,915.2 GTexel/s, compared to 926.6 GTexel/s for the H800, which is a substantial difference of about 988.6 GTexel/s. In contrast, the H800 SXM5 has a pixel rate of 42.12 GPixel/s, whereas the MI300A lists 0 MPixel/s, indicating no ROP output capability. For FP16 workloads, the H800 SXM5 explicitly lists 237.2 TFLOPS with a 4:1 ratio, while the MI300A does not provide an FP16 figure in the database.

These numbers imply that the MI300A is oriented toward raw FP32 compute and texture-heavy operations, while the H800 SXM5 includes fixed-function pixel processing and a high FP16 throughput that the MI300A does not specify. The data does not reveal which part would win a given benchmark, but the specification sheet suggests different optimization targets.

Architecture Differences

The two accelerators diverge sharply in their underlying design. The AMD Instinct MI300A uses the Aqua Vanjaram chip with the CDNA 3.0 architecture, built on a 5 nm process at TSMC. It integrates 153,000 million transistors on a die size of 1017 mm², resulting in a transistor density of 150.4 million per mm². The NVIDIA H800 SXM5 uses the GH100 chip with the Hopper architecture, also on a 5 nm TSMC process, but with 80,000 million transistors on an 814 mm² die, giving a density of 98.3 million per mm². The MI300A thus packs nearly twice the transistor count and a higher density, which suggests a more complex integration, likely including multiple compute dies and memory stacks.

Clock behavior differs as well. The MI300A has a base clock of 1000 MHz and a boost clock of 2100 MHz, while the H800 SXM5 runs at 1095 MHz base and 1755 MHz boost. The H800 has a higher base clock by 95 MHz, but the MI300A boost clock exceeds the H800 by 345 MHz. Memory clocks also differ: the MI300A runs at 1300 MHz with 5.2 Gbps effective, and the H800 at 1313 MHz with 5.3 Gbps effective. The difference is small, but the memory configuration is not.

The MI300A features 128 GB of HBM3 memory on an 8192-bit bus, yielding a bandwidth of 5.32 TB/s. The H800 SXM5 has 80 GB of HBM3 on a 5120-bit bus, delivering 3.36 TB/s. The MI300A leads in capacity by 48 GB, in bus width by 3072 bits, and in bandwidth by 1.96 TB/s. That is a decisive memory advantage, which matters for large models and data-intensive workloads.

Stream processor counts also tell a story. The MI300A has 14,592 shading units, 912 texture mapping units, and 0 ROPs. The H800 SXM5 has 16,896 shading units, 528 TMUs, 24 ROPs, and 528 tensor cores. The H800 has 2,304 more shading units, but the MI300A has 384 more TMUs. The H800 includes tensor cores, while the MI300A does not list any. The MI300A lists no RT cores for either part, but the H800 has a defined ROP count, and the MI300A has zero.

Power and packaging also diverge. The MI300A has a TDP of 750 W and uses an OAM Module slot, with no power connectors specified and a suggested PSU of 1150 W. The H800 SXM5 has a 700 W TDP, uses an SXM Module slot, requires an 8-pin EPS connector, and suggests a 1100 W PSU. Both use PCIe 5.0 x16 for the bus interface and have no display outputs. The MI300A does not list a production status, while the H800 is marked Active. The MI300A released on 2023-12-05, and the H800 on 2023-03-20, so the H800 appeared earlier by roughly eight and a half months.

The MI300A lists no APIs for DirectX, OpenGL, or Vulkan, while the H800 also lists null for those fields. Neither part is designed for graphics output. The MI300A has no specified successor or predecessor beyond Radeon Instinct, while the H800 has a predecessor of Server Ada and a successor of Server Blackwell.

FAQ

Q: Which accelerator has a higher FP32 peak performance?

A: The AMD Instinct MI300A reaches 61.29 TFLOPS in FP32, while the NVIDIA H800 SXM5 reaches 59.30 TFLOPS. The MI300A leads by 1.99 TFLOPS.

Q: What is the memory capacity difference between the two?

A: The MI300A has 128 GB of HBM3 memory, and the H800 SXM5 has 80 GB of HBM3. The MI300A provides 48 GB more capacity.

Q: Does the H800 SXM5 have tensor cores?

A: Yes, the H800 SXM5 lists 528 tensor cores. The MI300A does not list a tensor core count in the database.

Q: Which part has a higher memory bandwidth?

A: The MI300A delivers 5.32 TB/s of bandwidth, compared to 3.36 TB/s for the H800 SXM5. That is a 1.96 TB/s advantage for the MI300A.

Q: What are the TDP values for each accelerator?

A: The MI300A has a TDP of 750 W, and the H800 SXM5 has a TDP of 700 W. The MI300A consumes 50 W more power.

Q: How do the transistor counts compare?

A: The MI300A integrates 153,000 million transistors, while the H800 SXM5 integrates 80,000 million transistors. The MI300A has 73,000 million more transistors.

The Verdict

The data indicates that each accelerator is built for a distinct role, even though no benchmark scores exist to confirm real-world performance. The AMD Instinct MI300A delivers a higher FP32 peak, a larger memory pool, a wider bus, and more bandwidth, along with a higher texture rate and a higher boost clock. These traits point toward workloads that require massive data movement and high parallel FP32 throughput, such as large-scale scientific simulation or memory-bound compute kernels. The 128 GB HBM3 capacity and 5.32 TB/s bandwidth make it suitable for models or datasets that exceed the H800’s 80 GB capacity.

The NVIDIA H800 SXM5 counters with more shading units, a higher base clock, tensor cores, a defined ROP count, and a higher FP16 throughput of 237.2 TFLOPS. The tensor cores are a differentiator because the MI300A lists none, and the FP16 figure suggests strong performance for mixed-precision AI training and inference. The H800 also has a lower TDP of 700 W, which may matter for dense server deployments where power limits are tight.

Who should pick which depends on the workload profile. For FP32 compute and memory capacity, the MI300A has a clear specification advantage. For FP16 tensor-based AI workloads and a more conventional server module with an 8-pin EPS connector, the H800 SXM5 has the listed features. The MI300A uses an OAM Module slot with no power connectors, which may require a different system design. The H800’s SXM Module form factor and Active production status make it a known quantity in current server platforms.

Neither part has a launch MSRP in the database, so cost is not a factor in this analysis. The percentile ranking for both is 50, meaning they sit at the median of all GPUs in the database, but that ranking is based on zero benchmark scores, so it reflects a position of no data rather than measured performance.

Specification Differences

The following fields differ between the AMD Instinct MI300A and the NVIDIA H800 SXM5:

• Chip: Aqua Vanjaram versus GH100
• Architecture: CDNA 3.0 versus Hopper
• Generation: Instinct (MIx) versus Server Hopper (Hxx)
• Transistors: 153,000 million versus 80,000 million
• Die Size: 1017 mm² versus 814 mm²
• Transistor Density: 150.4M / mm² versus 98.3M / mm²
• Base Clock: 1000 MHz versus 1095 MHz
• Boost Clock: 2100 MHz versus 1755 MHz
• Memory Clock: 1300 MHz 5.2 Gbps effective versus 1313 MHz 5.3 Gbps effective
• Memory Size: 128 GB versus 80 GB
• Memory Bus Width: 8192 bit versus 5120 bit
• Memory Bandwidth: 5.32 TB/s versus 3.36 TB/s
• Shading Units: 14,592 versus 16,896
• Texture Mapping Units: 912 versus 528
• ROPs: 0 versus 24
• Tensor Cores: null versus 528
• Pixel Rate: 0 MPixel/s versus 42.12 GPixel/s
• Texture Rate: 1,915.2 GTexel/s versus 926.6 GTexel/s
• FP32: 61.29 TFLOPS versus 59.30 TFLOPS
• FP16: null versus 237.2 TFLOPS (4:1)
• TDP: 750 W versus 700 W
• Slot Width: OAM Module versus SXM Module
• Power Connectors: None versus 8-pin EPS
• Suggested PSU: 1150 W versus 1100 W
• Production Status: null versus Active
• Release Date: 2023-12-05 versus 2023-03-20
• Predecessor: Radeon Instinct versus Server Ada
• Successor: null versus Server Blackwell

The two parts share the same process node, foundry, bus interface, memory type, display outputs, and API support (all null or N/A). Neither has a launch MSRP.

Where Each One Wins

The AMD Instinct MI300A wins on raw FP32 compute, memory capacity, memory bandwidth, texture throughput, and boost clock. Its 61.29 TFLOPS FP32 peak and 1,915.2 GTexel/s texture rate indicate strong performance for workloads that rely on single-precision shader and texture operations, even though it has zero ROPs and no pixel output. The 128 GB HBM3 pool and 5.32 TB/s bandwidth give it a substantial advantage for data sets that must reside on the accelerator, reducing the need to move data over PCIe. The 153,000 million transistor count suggests a more complex compute fabric that can handle parallel tasks with high memory pressure.

The NVIDIA H800 SXM5 wins on shading unit count, tensor core availability, FP16 throughput, pixel rate, and base clock. Its 16,896 shading units outnumber the MI300A by 2,304, and its 528 tensor cores provide dedicated hardware for matrix operations, which the MI300A lacks. The 237.2 TFLOPS FP16 figure is a strong indicator for AI training and inference workloads that use mixed-precision arithmetic. The 42.12 GPixel/s pixel rate, while not useful for display output, suggests some fixed-function rasterization capability that the MI300A does not have. The H800 also has a lower TDP at 700 W and a smaller die at 814 mm², which may be easier to cool and integrate in existing server chassis.

The MI300A’s OAM Module form factor and lack of power connectors imply a system designed for high-density compute with dedicated power delivery. The H800’s SXM Module and 8-pin EPS connector align with standard NVIDIA server offerings. The release date difference, with the H800 arriving earlier, may indicate more mature software support in the database, but no benchmark data confirms this.

For memory-bound scientific computing, the MI300A’s larger HBM3 pool and wider bus give it a clear edge. For neural network training with FP16 precision, the H800 SXM5’s tensor cores and explicit FP16 throughput make it the stronger candidate based on the recorded specifications. The data does not support a single winner across all workloads; the choice depends on whether the priority is FP32 throughput and memory capacity or tensor-based FP16 acceleration.