AMD Instinct MI300 vs NVIDIA H800 SXM5 Comparison

AMD Instinct MI300 and NVIDIA H800 SXM5 are both 5 nm accelerators built by TSMC, but they target different corners of the server accelerator market. The MI300 uses the CDNA 3.0 architecture with the Aqua Vanjaram chip, while the H800 SXM5 relies on the Hopper architecture with the GH100 die. Both cards have no display outputs, use PCIe 5.0 x16 interfaces, and are designed strictly for compute workloads. The recorded data shows clear splits in compute throughput, memory capacity, and feature sets, which translate into distinct usage profiles.

Head-to-Head Benchmarks

The most dramatic difference between the two accelerators appears in memory capacity and bandwidth. The AMD Instinct MI300 carries 128 GB of HBM3 memory across an 8192-bit bus, yielding 5.32 TB/s of memory bandwidth. The NVIDIA H800 SXM5, by contrast, has 80 GB of HBM3 on a narrower 5120-bit bus, producing 3.36 TB/s. That means the MI300 delivers 48 GB more memory and roughly 58% higher bandwidth (5.32 TB/s versus 3.36 TB/s). For workloads that scale with memory footprint, such as large language model inference or graph analytics, the MI300 can hold substantially larger datasets on-device.

However, raw compute throughput tells a different story. The H800 SXM5 has a higher base clock at 1095 MHz versus 1000 MHz on the MI300, and a higher boost clock at 1755 MHz versus 1700 MHz. The H800 also packs more shading units, 16896 versus 14080 on the MI300. The result is that NVIDIA's card reaches 59.30 TFLOPS FP32, while AMD's card delivers 47.87 TFLOPS FP32. That puts the H800 roughly 24% ahead in single-precision floating-point performance.

The gap widens dramatically in half-precision compute. The H800 SXM5 achieves 237.2 TFLOPS FP16 using a 4:1 ratio, meaning it packs four times the FP16 throughput of its FP32 rate. The MI300 lists its FP16 at 47.87 TFLOPS with a 1:1 ratio, identical to its FP32 figure. So the H800 delivers roughly 5 times the FP16 throughput of the MI300 (237.2 TFLOPS versus 47.87 TFLOPS). This is a decisive advantage for training neural networks that rely on mixed-precision arithmetic, which has become standard practice in deep learning.

Texture and pixel throughput also favor different cards. The MI300 has 880 texture mapping units and a texture rate of 1,496.0 GTexel/s, versus 528 TMUs and 926.6 GTexel/s on the H800. That gives AMD a 61% lead in texture fill rate. Pixel rate, however, only appears on the NVIDIA side: the H800 lists 42.12 GPixel/s with 24 ROPs, while the MI300 lists 0 MPixel/s and no ROPs. This suggests the MI300 is not designed for rasterization at all, while the H800 retains some minimal pixel-processing capability, although both are far from graphics-oriented products.

The MI300 also has the H800 beat in transistor count and die size. AMD's chip contains 153,000 million transistors on a 1017 mm² die, giving a transistor density of 150.4 million per mm². NVIDIA's GH100 has 80,000 million transistors on an 814 mm² die, or 98.3 million per mm². That means the MI300 packs nearly twice the transistor count and 25% more silicon area. This aligns with its larger memory subsystem and wider bus.

The Verdict

The data supports a clear split. The NVIDIA H800 SXM5 wins decisively in compute throughput, particularly for FP16 workloads, and also leads in FP32. It reaches 59.30 TFLOPS FP32 versus 47.87 TFLOPS on the MI300, and 237.2 TFLOPS FP16 versus 47.87 TFLOPS. The H800 also has a higher base and boost clock, more shading units, and includes tensor cores, which the MI300 does not list. For training large neural networks or running mixed-precision inference at scale, the H800 SXM5 is the stronger choice based on these measurements.

The AMD Instinct MI300 wins on memory capacity and bandwidth. With 128 GB versus 80 GB, and 5.32 TB/s versus 3.36 TB/s, the MI300 can accommodate larger models and datasets without spilling to host memory. It also has a higher texture rate, 1,496.0 GTexel/s versus 926.6 GTexel/s, which may benefit certain convolution-heavy workloads. The MI300 also draws less power, 600 W versus 700 W, and has a lower suggested PSU requirement, 1000 W versus 1100 W. For inference scenarios where the entire model must reside in GPU memory, or for memory-bound analytics, the MI300 has a measurable advantage.

There is no single winner across all metrics. The verdict from the recorded data is that the H800 SXM5 is a compute-centric accelerator optimized for mixed-precision training, while the MI300 is a memory-centric accelerator built for large-scale inference and memory-bound workloads. The choice depends entirely on whether the bottleneck is compute throughput or memory capacity.

Architecture Differences

The two accelerators stem from fundamentally different design philosophies. AMD's MI300 uses the CDNA 3.0 architecture, which is a compute-focused derivative of AMD's GPU designs. Its chip, codenamed Aqua Vanjaram, is built on a 5 nm TSMC process and contains 153,000 million transistors on a 1017 mm² die. The MI300 has 14080 shading units, 880 TMUs, and no ROPs, which matches its 0 MPixel/s pixel rate. It also has no tensor cores listed, and its FP16 throughput is identical to FP32 at 47.87 TFLOPS, indicating a symmetric compute design.

NVIDIA's H800 SXM5 uses the Hopper architecture with the GH100 chip, also on a 5 nm TSMC process. The GH100 has 80,000 million transistors on an 814 mm² die, which is smaller and less dense than the MI300. The H800 has 16896 shading units, 528 TMUs, and 24 ROPs. Critically, it includes 528 tensor cores, which are specialized matrix-multiplication units. The H800's FP16 rate of 237.2 TFLOPS is exactly four times its FP32 rate of 59.30 TFLOPS, which is the signature of tensor-core acceleration. This ratio is absent on the MI300, where FP16 and FP32 are identical.

Memory architecture also differs. The MI300 uses 128 GB of HBM3 with an 8192-bit bus and 5.32 TB/s bandwidth. The H800 uses 80 GB of HBM3 with a 5120-bit bus and 3.36 TB/s bandwidth. The MI300's wider bus is the primary reason for its bandwidth advantage, while the H800's smaller capacity reflects its focus on compute density rather than memory size.

Clocks differ as well. The H800 runs at a base of 1095 MHz and boosts to 1755 MHz, while the MI300 runs at 1000 MHz base and 1700 MHz boost. Despite the MI300's lower clocks, its higher transistor count and larger die suggest that AMD prioritized memory and cache resources over raw clock speed.

Power specifications diverge. The MI300 has a TDP of 600 W and uses two 8-pin power connectors, while the H800 has a TDP of 700 W and uses an 8-pin EPS connector. The H800's suggested PSU is 1100 W versus 1000 W for the MI300. Neither card has display outputs, and both use PCIe 5.0 x16 for host connectivity.

The MI300 was released on January 3, 2023, and its predecessor is Radeon Instinct. The H800 was released on March 20, 2023, with its predecessor listed as Server Ada and its successor as Server Blackwell. The H800 is marked as active in production, while the MI300 does not have a production status listed.

FAQ

Q: Which accelerator has more memory bandwidth?

A: The AMD Instinct MI300, with 5.32 TB/s versus 3.36 TB/s on the NVIDIA H800 SXM5.

Q: Which card is faster in FP16 compute?

A: The NVIDIA H800 SXM5, delivering 237.2 TFLOPS FP16 compared to 47.87 TFLOPS on the MI300.

Q: Do both cards support tensor cores?

A: No. The H800 SXM5 lists 528 tensor cores, while the MI300 does not list any tensor cores.

Q: What is the memory capacity of each card?

A: The MI300 has 128 GB of HBM3, while the H800 SXM5 has 80 GB of HBM3.

Q: Which card has a higher TDP?

A: The H800 SXM5, at 700 W, versus 600 W for the MI300.

Q: Are both cards built on the same process node?

A: Yes, both use a 5 nm TSMC process, but the MI300 has 153,000 million transistors versus 80,000 million on the H800.

Where Each One Wins

The AMD Instinct MI300 wins in memory capacity, memory bandwidth, texture rate, transistor count, and die size. Its 128 GB memory pool is 60% larger than the H800's 80 GB, and its 5.32 TB/s bandwidth is 58% higher. These attributes make the MI300 suitable for workloads where the working set exceeds 80 GB, such as large-scale embedding tables, recommendation models, or multi-tenant inference serving that must avoid host memory transfers. The MI300 also has a higher texture rate, which could benefit convolution operations in certain vision models, although the lack of tensor cores limits its mixed-precision advantages. Additionally, the MI300 draws 100 W less power, which can reduce cooling and power delivery requirements in dense server deployments.

The NVIDIA H800 SXM5 wins in FP32 compute, FP16 compute, clock speeds, shading units, and tensor core availability. Its 59.30 TFLOPS FP32 is 24% higher than the MI300's 47.87 TFLOPS, and its 237.2 TFLOPS FP16 is roughly 5 times higher. The H800 also boosts to 1755 MHz versus 1700 MHz on the MI300. These specifications point directly at deep learning training, where FP16 with tensor cores is the dominant numeric format. The H800 also has 24 ROPs and a pixel rate of 42.12 GPixel/s, giving it some rasterization capability, although neither card is intended for graphics.

The recorded data indicates that the H800 SXM5 is the better accelerator for training large neural networks, especially those that use mixed-precision optimization. The MI300 is the better accelerator for inference and analytics workloads that require maximum memory capacity and bandwidth. The MI300 also carries a lower power envelope, which can be a factor in power-constrained facilities. Both cards are built on the same 5 nm process, but AMD used its larger die to add more memory resources, while NVIDIA used its smaller die to maximize compute throughput with tensor cores. The choice between them hinges on whether a workload is compute-bound or memory-bound, and the benchmark data makes that distinction clear.