AMD Instinct MI300A vs NVIDIA H800 PCIe 80 GB Comparison

AMD Instinct MI300A vs NVIDIA H800 PCIe 80 GB: A Database Comparison

The AMD Instinct MI300A and NVIDIA H800 PCIe 80 GB are both high-end server accelerators, but they target different workloads. The database shows AMD’s part as a large OAM module with a 750 W power envelope, while NVIDIA’s card is a dual-slot PCIe board at 350 W. Their raw specifications diverge sharply in memory capacity, bandwidth, and feature set, which leads to distinct strengths in compute-heavy and memory-bound tasks.

Where Each One Wins

The MI300A holds the advantage in raw FP32 compute. It delivers 61.29 TFLOPS, which is roughly 20% higher than the H800’s 51.22 TFLOPS. For workloads that rely on single-precision math, such as certain scientific simulations or data analytics, the MI300A shows a clear edge.

The H800 counters with its tensor core array. It includes 456 tensor cores, while the MI300A lists no tensor core count. The H800 also provides 204.9 TFLOPS of FP16 throughput at a 4:1 ratio, a figure not matched by the MI300A in the recorded data. This makes the H800 the stronger option for mixed-precision deep learning training and inference, where tensor operations dominate.

Memory capacity is another split. The MI300A packs 128 GB of HBM3, compared to the H800’s 80 GB of HBM2e. For very large models or datasets that must fit in on-chip memory, the MI300A wins outright. The H800, with its smaller footprint and lower power draw, wins in deployment flexibility, as it fits into standard PCIe slots and uses a single 16-pin power connector.

Architecture Differences

The MI300A uses AMD’s CDNA 3.0 architecture, built on a 5 nm process at TSMC. It integrates 153,000 million transistors on a 1017 mm² die, yielding a transistor density of 150.4M per mm². The chip is named Aqua Vanjaram. The H800 uses NVIDIA’s Hopper architecture, also on a 5 nm TSMC node, but with 80,000 million transistors on an 814 mm² die, giving a density of 98.3M per mm². Its chip is GH100.

The MI300A has no ROPs, a pixel rate of 0 MPixel/s, and no display outputs. It is an OAM module with no power connectors, relying on the system board for power delivery. The H800 is a dual-slot card with 24 ROPs, a 42.12 GPixel/s pixel rate, and a 1x 16-pin power connector. Both lack display outputs, confirming their server-only nature.

Memory architecture differs significantly. The MI300A uses HBM3 with an 8192-bit bus and 5.32 TB/s bandwidth. The H800 uses HBM2e with a 5120-bit bus and 2.04 TB/s bandwidth. The MI300A’s bus width is 60% larger, and its bandwidth is 2.6 times higher. The MI300A also has 912 TMUs versus the H800’s 456 TMUs, and its texture rate is 1,915.2 GTexel/s versus 800.3 GTexel/s.

Clock speeds tell a mixed story. The MI300A has a lower base clock of 1000 MHz but a higher boost of 2100 MHz. The H800 runs at 1095 MHz base and 1755 MHz boost. Memory clocks are 1300 MHz (5.2 Gbps effective) for the MI300A and 1593 MHz (3.2 Gbps effective) for the H800. The H800’s higher memory clock does not overcome its narrower bus.

Head-to-Head Benchmarks

The database contains no direct head-to-head benchmark entries or wins for either part. Both items show zero benchmark scores and zero wins in the head-to-head section. However, the specification data allows for a clear comparison of theoretical limits.

FP32 compute: the MI300A’s 61.29 TFLOPS exceeds the H800’s 51.22 TFLOPS by 10.07 TFLOPS, or about 19.7%. This is the largest single-number advantage for AMD in the compute domain. For any FP32-bound task, the MI300A should complete more operations per second.

FP16 compute: the H800’s 204.9 TFLOPS (4:1) is the only FP16 figure recorded. It represents a 4x ratio over its FP32 number, which is typical of tensor-optimized hardware. The MI300A has no listed FP16 value, so the H800 is the only part with a quantified half-precision capability. This gives NVIDIA a decisive edge in tensor-heavy workloads.

Memory bandwidth: the MI300A’s 5.32 TB/s is 2.6 times the H800’s 2.04 TB/s. The difference is 3.28 TB/s. For memory-bound kernels, such as large matrix multiplications or data shuffling, the MI300A can feed its compute units far faster. The H800’s 2.04 TB/s is still substantial but becomes a bottleneck relative to its tensor throughput.

Memory capacity: 128 GB versus 80 GB is a 48 GB gap. The MI300A holds 60% more data on-chip. This directly affects the size of models that can be resident without CPU-GPU transfers. The H800’s 80 GB is large by conventional standards but smaller than the MI300A’s offering.

Texture rate: the MI300A’s 1,915.2 GTexel/s is 2.4 times the H800’s 800.3 GTexel/s. While texture rate is less relevant for compute accelerators, it indicates the MI300A’s wider shading and texture pipeline. The H800’s pixel rate of 42.12 GPixel/s is nonzero, while the MI300A has 0 MPixel/s, but neither part targets rasterization.

Specification Differences

The following fields differ between the two parts:

• Chip: Aqua Vanjaram (AMD) versus GH100 (NVIDIA)
• 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 1593 MHz (3.2 Gbps effective)
• Memory Size: 128 GB versus 80 GB
• Memory Type: HBM3 versus HBM2e
• Memory Bus Width: 8192 bit versus 5120 bit
• Memory Bandwidth: 5.32 TB/s versus 2.04 TB/s
• TMUs: 912 versus 456
• ROPs: 0 versus 24
• Tensor Cores: none listed versus 456
• Pixel Rate: 0 MPixel/s versus 42.12 GPixel/s
• Texture Rate: 1,915.2 GTexel/s versus 800.3 GTexel/s
• FP32 Compute: 61.29 TFLOPS versus 51.22 TFLOPS
• FP16 Compute: none listed versus 204.9 TFLOPS (4:1)
• TDP: 750 W versus 350 W
• Slot Width: OAM Module versus Dual-slot
• Power Connectors: None versus 1x 16-pin
• Suggested PSU: 1150 W versus 750 W
• Dimensions: not listed versus 268 mm (10.6 inches) length, 111 mm (4.4 inches) height
• Release Date: 2023-12-05 versus 2023-03-20
• Predecessor: Radeon Instinct versus Server Ada
• Successor: none listed versus Server Blackwell
• Production Status: not listed versus Active

Identical fields include manufacturer process node (5 nm, TSMC), shading units (14592), bus interface (PCIe 5.0 x16), and display outputs (none). Both have a 50th percentile versus all GPUs and zero average benchmark score.

FAQ

Q: Which part has higher single-precision compute?

A: The AMD MI300A delivers 61.29 TFLOPS FP32, compared to the NVIDIA H800’s 51.22 TFLOPS. The MI300A leads by roughly 20% in this metric.

Q: Does the NVIDIA H800 support tensor operations?

A: Yes, it has 456 tensor cores and lists 204.9 TFLOPS FP16 at a 4:1 ratio. The AMD MI300A does not list tensor cores or an FP16 figure in the database.

Q: How do memory capacities compare?

A: The MI300A has 128 GB of HBM3, while the H800 has 80 GB of HBM2e. The AMD part holds 48 GB more data on-chip.

Q: What is the power difference?

A: The MI300A has a TDP of 750 W and suggests a 1150 W PSU. The H800 has a TDP of 350 W and suggests a 750 W PSU. The H800 consumes less than half the power of the MI300A.

Q: Which card has higher memory bandwidth?

A: The MI300A provides 5.32 TB/s, while the H800 provides 2.04 TB/s. The AMD part offers 2.6 times the bandwidth.

Q: Are both parts the same physical size?

A: No. The MI300A is an OAM module with no listed dimensions, while the H800 is a dual-slot PCIe card measuring 268 mm in length and 111 mm in height.

The Verdict

The data points to a clear division of roles. The AMD Instinct MI300A is built for raw FP32 throughput and massive on-chip memory. Its 61.29 TFLOPS, 128 GB capacity, and 5.32 TB/s bandwidth make it the choice for workloads that need wide data paths and large resident datasets. The lack of tensor cores and FP16 figures means it does not compete in the mixed-precision deep learning space that NVIDIA dominates.

The NVIDIA H800 PCIe 80 GB is the better fit for AI training and inference. Its 456 tensor cores and 204.9 TFLOPS FP16 give it a specialized advantage that the MI300A cannot match with the recorded specifications. Its 350 W TDP and dual-slot form factor also make it easier to integrate into existing servers, compared to the MI300A’s OAM module and 750 W envelope.

For a system builder choosing between these two, the decision hinges on the workload. If the task is FP32-heavy scientific computing or requires more than 80 GB of on-chip memory, the MI300A is the stronger part. If the task is deep learning with tensor operations, or if power and slot space are constrained, the H800 is the practical choice. Benchmark results confirm no direct head-to-head tests exist in the database, so the comparison rests on architectural specifications. The MI300A wins on compute density and memory, the H800 wins on tensor capability and efficiency.