AMD Instinct MI350P vs NVIDIA H100 CNX Comparison

FAQ

Q: What are the core process differences between the AMD Instinct MI350P and the NVIDIA H100 CNX?

A: The AMD Instinct MI350P uses a 3 nm process from TSMC, while the NVIDIA H100 CNX uses a 5 nm process from TSMC. The MI350P features the CDNA 4.0 architecture, whereas the H100 CNX is built on the Hopper architecture.

Q: How do the memory capacities and bandwidth compare?

A: The MI350P has 144 GB of HBM3e memory on an 8192-bit bus, delivering 8.19 TB/s. The H100 CNX has 80 GB of HBM2e on a 5120-bit bus, delivering 2.04 TB/s. The MI350P holds a clear advantage in both capacity and bandwidth.

Q: Which card has higher FP32 compute performance?

A: The H100 CNX delivers 53.84 TFLOPS FP32, while the MI350P provides 36.04 TFLOPS FP32. The H100 CNX leads in this metric by a significant margin.

Q: Which card has higher FP16 performance?

A: The H100 CNX reaches 215.4 TFLOPS FP16 (4:1 ratio), while the MI350P reaches 36.04 TFLOPS FP16 (1:1 ratio). The H100 CNX is substantially ahead in FP16 throughput.

Q: What are the power consumption profiles?

A: The MI350P has a TDP of 600 W and requires a 1000 W suggested PSU. The H100 CNX has a TDP of 350 W and a 750 W suggested PSU. The H100 CNX consumes considerably less power.

Q: What are the physical dimensions of both cards?

A: Both are dual-slot cards with a length of 267 mm (10.5 inches) and a height of 111 mm (4.4 inches). The MI350P has a width of 40 mm (1.6 inches); the H100 CNX width is not recorded in the database.

Head-to-Head Benchmarks

The recorded data includes no direct head-to-head benchmark scores, so the comparison relies on the specification fields captured in the database. The most decisive wins break along compute and memory lines.

The H100 CNX dominates in raw FP32 throughput. Its 53.84 TFLOPS is 17.80 TFLOPS higher than the MI350P's 36.04 TFLOPS, making it roughly 49% ahead in this metric. For workloads that depend on single-precision math, this is the defining difference.

The FP16 comparison is even more lopsided. The H100 CNX delivers 215.4 TFLOPS FP16, which is 179.36 TFLOPS above the MI350P's 36.04 TFLOPS. That is roughly a 5.98x advantage, and it stems from the H100 CNX's 4:1 FP16 ratio compared to the MI350P's 1:1 ratio. For mixed-precision training and inference tasks, the H100 CNX has a massive edge.

The MI350P turns the tables on memory. Its 8.19 TB/s bandwidth is 6.15 TB/s higher than the H100 CNX's 2.04 TB/s, a roughly 4.01x advantage. The MI350P also carries 144 GB of HBM3e versus 80 GB of HBM2e, providing 64 GB more capacity. Memory-bound workloads that fit within the MI350P's larger pool will see a substantial benefit.

Texture rate also favors the MI350P. Its 1,126.4 GTexel/s is 285.1 GTexel/s above the H100 CNX's 841.3 GTexel/s, a roughly 33.9% advantage. The MI350P's 512 TMUs outnumber the H100 CNX's 456 TMUs, supporting the higher texture throughput.

Pixel rate is the reverse. The H100 CNX records 44.28 GPixel/s with 24 ROPs, while the MI350P records 0 MPixel/s with 0 ROPs. The MI350P has no pixel pipeline at all, which is expected for an accelerator with no display outputs.

Clock speeds also differ. The MI350P boosts to 2200 MHz, while the H100 CNX boosts to 1845 MHz. The MI350P runs 355 MHz higher at boost, which helps close the gap in some throughput metrics despite its lower shader count.

Transistor counts are close. The H100 CNX has 80,000 million transistors on an 814 mm² die, while the MI350P has 73,000 million on a 1190 mm² die. The H100 CNX packs its transistors denser at 98.3M per mm² versus 61.3M per mm² for the MI350P.

Architecture Differences

The MI350P uses CDNA 4.0, AMD's latest accelerator architecture, built for dense compute with no graphics pipeline. The H100 CNX uses NVIDIA's Hopper architecture, which is also compute-focused but includes a small ROP configuration and tensor cores.

Process technology is a core differentiator. The MI350P is fabricated on TSMC's 3 nm node, while the H100 CNX uses TSMC's 5 nm node. This gives the MI350P a transistor density of 61.3M per mm², but its die is much larger at 1190 mm². The H100 CNX has a smaller die at 814 mm² but a higher density of 98.3M per mm², reflecting a more compact design.

The MI350P uses HBM3e memory, while the H100 CNX uses HBM2e. This memory generation gap explains much of the bandwidth difference: 8.19 TB/s versus 2.04 TB/s. The MI350P also uses a wider 8192-bit bus compared to the H100 CNX's 5120-bit bus.

Tensor cores exist only on the H100 CNX, with 456 tensor cores recorded. The MI350P has no tensor core field in the database. This directly affects the FP16 4:1 ratio on the H100 CNX, which is absent on the MI350P.

The MI350P has 8192 shading units and 512 TMUs, while the H100 CNX has 14592 shading units and 456 TMUs. The H100 CNX has nearly double the shader count, but the MI350P has more texture units. ROPs are 0 on the MI350P and 24 on the H100 CNX, reinforcing that the MI350P is purely a compute part.

APIs differ as well. The MI350P lists N/A for DirectX, OpenGL, and Vulkan. The H100 CNX has null entries for those APIs. Neither card supports graphics APIs in the recorded data.

Specification Differences

The two cards differ across nearly every recorded specification field.

Memory size: 144 GB on the MI350P versus 80 GB on the H100 CNX.

Memory type: HBM3e versus HBM2e.

Memory bus width: 8192 bit versus 5120 bit.

Memory bandwidth: 8.19 TB/s versus 2.04 TB/s.

Shading units: 8192 versus 14592.

Texture mapping units: 512 versus 456.

ROPs: 0 versus 24.

Pixel rate: 0 MPixel/s versus 44.28 GPixel/s.

Texture rate: 1,126.4 GTexel/s versus 841.3 GTexel/s.

FP32 performance: 36.04 TFLOPS versus 53.84 TFLOPS.

FP16 performance: 36.04 TFLOPS (1:1) versus 215.4 TFLOPS (4:1).

Tensor cores: none recorded versus 456.

Base clock: 1000 MHz versus 690 MHz.

Boost clock: 2200 MHz versus 1845 MHz.

Memory clock: 2000 MHz (8 Gbps effective) versus 1593 MHz (3.2 Gbps effective).

TDP: 600 W versus 350 W.

Power connector: 1x 16-pin versus 8-pin EPS.

Suggested PSU: 1000 W versus 750 W.

Transistor count: 73,000 million versus 80,000 million.

Die size: 1190 mm² versus 814 mm².

Transistor density: 61.3M per mm² versus 98.3M per mm².

Process node: 3 nm versus 5 nm.

Width: 40 mm (1.6 inches) on the MI350P, not recorded on the H100 CNX.

Release date: 2026-05-06 for the MI350P versus 2023-03-20 for the H100 CNX.

Production status: not recorded for the MI350P, Active for the H100 CNX.

Predecessor: Radeon Instinct for the MI350P, Server Ada for the H100 CNX.

Successor: none recorded for the MI350P, Server Blackwell for the H100 CNX.

Both cards share PCIe 5.0 x16 interfaces, dual-slot cooling, no display outputs, and identical lengths and heights.

Where Each One Wins

The MI350P wins in memory capacity, memory bandwidth, and texture throughput. Its 144 GB HBM3e pool and 8.19 TB/s bandwidth make it the stronger choice for large model inference and training datasets that exceed the H100 CNX's 80 GB. The 64 GB capacity difference means the MI350P can hold larger batches, bigger embedding tables, or longer context windows without spilling to slower storage. The 4.01x bandwidth advantage also favors workloads that stream data heavily, such as graph analytics, sparse attention, or high-throughput data loading.

The MI350P also wins on texture rate, with 1,126.4 GTexel/s versus 841.3 GTexel/s. For compute workloads that use texture-like gather operations or data sampling, this 33.9% advantage matters. The higher boost clock of 2200 MHz versus 1845 MHz further supports the MI350P's throughput in clock-bound tasks.

The H100 CNX wins in FP32 and FP16 compute. Its 53.84 TFLOPS FP32 is useful for traditional HPC simulations, physics solvers, and any code that relies on single-precision math. The 49% lead over the MI350P makes it the clear pick for those workloads. The FP16 4:1 ratio is the standout feature: 215.4 TFLOPS versus 36.04 TFLOPS. This is a 5.98x advantage that applies to transformer training, large language model fine-tuning, and any mixed-precision deep learning pipeline that can exploit the tensor cores.

The H100 CNX also wins on power efficiency. Its 350 W TDP is 250 W lower than the MI350P's 600 W. For the same FP32 output, the H100 CNX uses far less power. The 750 W suggested PSU versus 1000 W also lowers system power requirements. In dense server racks with strict power ceilings, the H100 CNX allows more accelerators per chassis.

The H100 CNX has a pixel pipeline with 44.28 GPixel/s and 24 ROPs, while the MI350P has none. This gives the H100 CNX a narrow edge in any workload that touches rasterization or framebuffer operations, though both cards lack display outputs.

The Verdict

The data splits the two accelerators into distinct roles.

The NVIDIA H100 CNX is the compute-density leader. Its 53.84 TFLOPS FP32 and 215.4 TFLOPS FP16 make it the stronger part for pure math throughput. The 456 tensor cores and 4:1 FP16 ratio directly support deep learning training and inference. The 350 W TDP makes it easier to deploy at scale. For teams running mixed-precision neural network workloads or single-precision HPC codes, the H100 CNX is the better fit.

The AMD Instinct MI350P is the memory-capacity and memory-bandwidth leader. Its 144 GB HBM3e and 8.19 TB/s bandwidth dwarf the H100 CNX's 80 GB and 2.04 TB/s. The 1:1 FP16 ratio means the MI350P does not accelerate FP16 beyond FP32, so it trades raw compute for memory headroom. For workloads that are memory-bound, such as large-scale inference, graph processing, or data-intensive analytics, the MI350P's larger pool and faster bus deliver practical advantages that raw TFLOPS cannot match.

The production status also matters. The H100 CNX is Active and released in 2023, with an established successor in Server Blackwell. The MI350P releases in 2026 and has no successor recorded. The H100 CNX is a proven, shipping product; the MI350P is a forward-looking part with no production status confirmed.

Neither part has recorded benchmark scores, so the percentile fields sit at 50 for both, and the average benchmark score is 0 for both. The database shows no head-to-head wins for either card. The verdict rests on the specification deltas.

Choose the H100 CNX for FP32 and FP16 compute throughput, tensor-core-accelerated deep learning, and lower power draw. Choose the MI350P for maximum memory capacity, maximum memory bandwidth, and workloads that saturate data movement rather than math throughput. The 5.98x FP16 advantage on the H100 CNX is the single largest performance gap in the data, but the 4.01x bandwidth advantage on the MI350P is equally decisive for memory-bound scenarios.