AMD Instinct MI355X vs NVIDIA H100 CNX Comparison

Where Each One Wins

The benchmark database separates these two accelerators by design intent rather than by raw win counts. The AMD Instinct MI355X is built around a massive memory and compute configuration aimed at capacity-bound workloads, while the NVIDIA H100 CNX targets a different balance of throughput and power efficiency. Neither part records a benchmark win in the head-to-head data, so the distinction is drawn from the architecture and specification sheets.

The MI355X wins on memory capacity, bandwidth, and raw FP32 throughput. Its 288 GB of HBM3e memory dwarfs the H100 CNX’s 80 GB of HBM2e, and the 8.19 TB/s bandwidth is four times the H100 CNX’s 2.04 TB/s. For workloads that load large models or datasets into memory, the MI355X holds a decisive advantage. The FP32 rate of 78.64 TFLOPS also exceeds the H100 CNX’s 53.84 TFLOPS, giving the AMD part a lead in single-precision compute.

The H100 CNX wins on power efficiency and physical integration. Its 350 W TDP is one-quarter of the MI355X’s 1400 W, and it ships as a dual-slot PCIe card with an 8-pin EPS connector, whereas the MI355X is an OAM module with no power connectors and a suggested 1800 W PSU. The H100 CNX also carries a higher transistor density, 98.3M per mm² versus 77.7M per mm², indicating a more compact logic layout. For systems with power or chassis constraints, the H100 CNX is the more adaptable option.

The FP16 comparison splits the pair sharply. The H100 CNX delivers 215.4 TFLOPS FP16 with a 4:1 ratio, while the MI355X provides 78.64 TFLOPS FP16 at a 1:1 ratio. The NVIDIA part is engineered for accelerated FP16 workloads, likely tensor operations, while the AMD part treats FP16 as a symmetric pairing with FP32.

Architecture Differences

The two accelerators use different process nodes, memory types, and compute organizations. The MI355X is fabbed on TSMC’s 3 nm process, while the H100 CNX uses 5 nm. The MI355X packs 185,000 million transistors onto a 2380 mm² die, resulting in a transistor density of 77.7M per mm². The H100 CNX has 80,000 million transistors on an 814 mm² die, with a density of 98.3M per mm². The AMD chip is substantially larger and denser in absolute transistor count, but the NVIDIA chip achieves a higher density per area.

The MI355X uses the CDNA 4.0 architecture with an MI350 256CU chip. It has 16,384 shading units, 1,024 TMUs, and 0 ROPs. The pixel rate is listed as 0 MPixel/s, and the texture rate is 2,457.6 GTexel/s. The H100 CNX uses the Hopper architecture with a GH100 chip. It has 14,592 shading units, 456 TMUs, 24 ROPs, and 456 tensor cores. Its pixel rate is 44.28 GPixel/s, and its texture rate is 841.3 GTexel/s. The MI355X has more shading units and TMUs, but the H100 CNX has dedicated tensor cores and a nonzero ROP count.

Memory architecture differs fundamentally. The MI355X uses 288 GB of HBM3e on an 8192-bit bus, with a memory clock of 2000 MHz (8 Gbps effective) and 8.19 TB/s bandwidth. The H100 CNX uses 80 GB of HBM2e on a 5120-bit bus, with a memory clock of 1593 MHz (3.2 Gbps effective) and 2.04 TB/s bandwidth. The AMD part has a wider bus and faster memory, but the NVIDIA part uses an older memory type.

Clock speeds also differ. The MI355X has a base clock of 1000 MHz and a boost of 2400 MHz. The H100 CNX has a base of 690 MHz and a boost of 1845 MHz. The AMD part operates at higher frequencies. Power delivery reflects this: the MI355X has a TDP of 1400 W and a suggested PSU of 1800 W, while the H100 CNX has a TDP of 350 W and a suggested PSU of 750 W.

Physical formats are not interchangeable. The MI355X is an OAM module measuring 102 mm by 165 mm, with no display outputs. The H100 CNX is a dual-slot PCIe card measuring 267 mm by 111 mm, also with no display outputs. Both use PCIe 5.0 x16. The MI355X has no API support listed, while the H100 CNX has null entries for DirectX, OpenGL, and Vulkan.

Head-to-Head Benchmarks

The recorded head-to-head benchmark data is empty, meaning no direct benchmark scores exist in the database for this pairing. However, the specification sheet provides measurable differences that serve as a proxy for performance.

The most significant gap is memory bandwidth. The MI355X offers 8.19 TB/s versus the H100 CNX’s 2.04 TB/s, a 4.01x advantage. This is a direct consequence of the 8192-bit bus versus 5120-bit, and the faster HBM3e memory. For memory-bound operations, the MI355X should complete data transfers in roughly one-quarter the time.

FP32 throughput favors the MI355X by a factor of 1.46. The AMD part delivers 78.64 TFLOPS, while the NVIDIA part delivers 53.84 TFLOPS. This gap is smaller than the memory difference but still substantial for single-precision workloads.

FP16 throughput reverses the order. The H100 CNX delivers 215.4 TFLOPS, which is 2.74x the MI355X’s 78.64 TFLOPS. The NVIDIA part’s 4:1 FP16 ratio indicates a specialized path for half-precision compute, whereas the AMD part’s 1:1 ratio shows a balanced approach.

Texture rate also favors the MI355X. The AMD part achieves 2,457.6 GTexel/s versus 841.3 GTexel/s for the NVIDIA part, a 2.92x lead. The H100 CNX has a nonzero pixel rate of 44.28 GPixel/s, while the MI355X is listed at 0 MPixel/s, indicating the AMD part is not designed for rasterization output.

Transistor density favors the NVIDIA part. The H100 CNX reaches 98.3M transistors per mm² versus 77.7M for the MI355X, a 1.27x density advantage. This suggests the NVIDIA design packs logic more tightly, though the AMD part uses more total transistors.

Power efficiency is not directly benchmarked, but the TDP figures imply a stark contrast. The H100 CNX delivers 53.84 TFLOPS FP32 at 350 W, while the MI355X delivers 78.64 TFLOPS at 1400 W. Per watt, the H100 CNX provides roughly 0.154 TFLOPS/W versus 0.056 TFLOPS/W for the AMD part, a 2.75x efficiency lead for NVIDIA in FP32.

FAQ

Q: Which accelerator has more memory?

A: The AMD Instinct MI355X has 288 GB of HBM3e, which is 3.6x the 80 GB of HBM2e found on the NVIDIA H100 CNX.

Q: Which accelerator has higher FP32 performance?

A: The MI355X delivers 78.64 TFLOPS FP32, which is 1.46x the H100 CNX’s 53.84 TFLOPS.

Q: Which accelerator has higher FP16 performance?

A: The H100 CNX delivers 215.4 TFLOPS FP16, which is 2.74x the MI355X’s 78.64 TFLOPS.

Q: What is the power consumption difference?

A: The MI355X has a TDP of 1400 W, while the H100 CNX has a TDP of 350 W. The NVIDIA part uses one-quarter the power.

Q: What memory types do these use?

A: The MI355X uses HBM3e, while the H100 CNX uses HBM2e. The MI355X also has a wider 8192-bit bus versus 5120-bit.

Q: Do these cards have display outputs?

A: No. Both the MI355X and the H100 CNX are listed with no display outputs.

The Verdict

The data points to a clear split. The AMD Instinct MI355X is the choice for workloads that need maximum memory capacity and bandwidth. Its 288 GB HBM3e pool and 8.19 TB/s bandwidth support very large models or datasets, and its 78.64 TFLOPS FP32 rate handles single-precision compute with headroom. The texture rate of 2,457.6 GTexel/s also exceeds the NVIDIA part, though the zero pixel rate suggests it is not suited for graphics output.

The NVIDIA H100 CNX is the choice for power-constrained environments and FP16-heavy workloads. Its 350 W TDP fits into dual-slot PCIe systems, and its 215.4 TFLOPS FP16 rate is more than double the AMD part. The 456 tensor cores provide a dedicated path for accelerated math, and the 44.28 GPixel/s pixel rate indicates some rasterization capability, though no display outputs are present.

Neither part wins outright in the database’s head-to-head benchmarks, because those benchmarks are not recorded. The specification comparison indicates the MI355X leads in memory and FP32, while the H100 CNX leads in FP16 and power efficiency. Systems with abundant power and space should favor the MI355X for memory-bound AI training. Systems with limited power budgets or FP16 tensor workloads should favor the H100 CNX. The choice depends entirely on which resource is the constraint.