H100 vs MI300X: NVIDIA vs AMD AI Accelerator Comparison

Introduction

For most of the 2022–2024 period, NVIDIA's H100 had the AI accelerator market largely to itself at the highest performance tier. AMD's MI300X changed that calculus. Launched in late 2023, the MI300X brought 192 GB of HBM3 memory—more than double the H100's 80 GB—and positioned itself as the preferred accelerator for large language model inference where fitting model weights into GPU memory is the primary constraint.

By 2025 and into 2026, the H100 vs MI300X comparison has become one of the most practically significant hardware decisions in AI infrastructure. Cloud providers, enterprises, and AI labs are actively evaluating both platforms, and the choice ripples down into server board design, interconnect architecture, cooling infrastructure, and total cost of ownership.

This article compares the two platforms in depth—from die architecture and memory subsystem through interconnect design, power delivery, and PCB-level implications—giving hardware engineers and infrastructure decision-makers the information they need to evaluate both options objectively.

1. Table of Contents
2. Introduction
3. Platform Overview
4. - NVIDIA H100: Hopper Architecture
5. - AMD MI300X: CDNA 3 Architecture
6. Full Specification Comparison: H100 vs MI300X
7. Compute Performance
8. - Training Throughput
9. - Inference Throughput
10. Memory Architecture: HBM3 vs HBM3 (192 GB)
11. GPU-to-GPU Interconnect: NVLink 4.0 vs Infinity Fabric
12. Host Interface: PCIe Gen5 on Both Platforms
13. Power and Thermal Envelope
14. PCB Design Differences: H100 vs MI300X Systems
15. - Form Factor and Board Interface
16. - Layer Count
17. - Laminate Materials
18. - Power Delivery
19. - Thermal Management
20. - Interconnect Routing
21. Software Ecosystem
22. Workload Fit: Which Platform for Which Use Case?
23. Infrastructure Comparison: DGX H100 vs MI300X Server
24. FAQ

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Platform Overview

NVIDIA H100: Hopper Architecture

The H100 is built on NVIDIA's Hopper architecture, introduced in 2022. The GH100 die is manufactured on TSMC's 4N process with 80 billion transistors. Key architectural innovations include the Transformer Engine with native FP8 support, fourth-generation NVTensor Cores, and NVLink 4.0 providing 900 GB/s of bidirectional GPU-to-GPU bandwidth.

The H100 ships in two primary form factors:

• SXM5: High-bandwidth mezzanine form factor for DGX and HGX server configurations; 700 W TDP; 80 GB HBM3 at 3.35 TB/s
• PCIe: Standard add-in card for broader server compatibility; 350 W TDP; 80 GB HBM2e at 2.0 TB/s (lower memory bandwidth than SXM5)

The H200 variant upgrades the memory to 141 GB of HBM3e at 4.8 TB/s while retaining the same GH100 die and SXM5 socket, making it a drop-in upgrade for H100 infrastructure.

AMD MI300X: CDNA 3 Architecture

The MI300X is AMD's flagship AI training and inference accelerator, built on the CDNA 3 architecture. It is an ambitious multi-die design: three GPU dies (XCDs—Accelerator Complex Dies) and four HBM3 memory stacks are integrated using AMD's 3D chiplet packaging technology, with the XCDs stacked vertically on a shared interposer alongside the HBM stacks.

The MI300X ships exclusively in OAM (Open Accelerator Module) form factor, making it compatible with OAM-compliant Universal Base Boards (UBBs). It does not ship in an SXM-compatible or standard PCIe add-in card form factor—OAM-based infrastructure is required. For a detailed explanation of the OAM standard, see What Is an OAM Module? Open Accelerator Module Standard for AI Hardware.

• OAM form factor: 700 W TDP; 192 GB HBM3 at 5.3 TB/s
• Die configuration: 3 × XCD (GPU dies) + 4 × HBM3 stacks on a shared interposer
• Process node: TSMC N5 (XCDs) + TSMC N6 (interposer/base die)

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Full Specification Comparison: H100 vs MI300X

Specification	NVIDIA H100 SXM5	AMD MI300X OAM
Architecture	Hopper (GH100)	CDNA 3 (MI300X)
Process node	TSMC 4N	TSMC N5 (XCD) + N6 (base)
Die configuration	1 × GH100 monolithic	3 × XCD + base interposer (3D chiplet)
Total transistors	80 billion	153 billion (combined)
FP16 / BF16 TFLOPS (dense)	989	1,307
FP8 TFLOPS (dense)	~2,000 (sparse)	Not natively supported (FP8 via software emulation)
FP64 TFLOPS	34	163.4
INT8 TOPS	~4,000 (sparse)	2,614
Memory type	HBM3	HBM3
Memory capacity	80 GB	192 GB
Memory bandwidth	3.35 TB/s	5.3 TB/s
GPU-to-GPU interconnect	NVLink 4.0 (900 GB/s bidir.)	AMD Infinity Fabric (448 GB/s bidir. per link)
Host interface	PCIe Gen5 ×16 (~128 GB/s)	PCIe Gen5 ×16 (~128 GB/s)
TDP	700 W	750 W
Form factor	SXM5 (NVIDIA proprietary)	OAM (Open Compute Project standard)
Compatible baseboard	NVIDIA HGX H100 baseboard	OAM-compliant Universal Base Board (UBB)
Max GPUs per server node	8 (DGX H100)	8 (OAM UBB standard configuration)

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Compute Performance

Training Throughput

Raw compute specifications tell only part of the training performance story. The H100's Transformer Engine—which dynamically switches between FP8 and FP16/BF16 precision within a single forward or backward pass—delivers disproportionate performance on transformer model training relative to its peak FLOPS numbers.

The MI300X has higher peak BF16 TFLOPS (1,307 vs 989) and significantly higher FP64 performance (163 vs 34 TFLOPS), but its lack of native FP8 support means it cannot fully exploit the precision reduction techniques that make H100 particularly efficient on large language model training. In practice, training throughput benchmarks on transformer models (LLaMA, GPT-4 class architectures) show H100 competitive with or ahead of MI300X on a per-GPU basis despite the lower peak BF16 FLOPS figure.

AMD has addressed FP8 support in the MI350 generation (the MI300X successor), narrowing this gap for future workloads.

Inference Throughput

Inference is where the MI300X most clearly differentiates itself from the H100. The critical constraint for large language model inference is memory capacity: the model weights for a 70B-parameter model in BF16 precision require approximately 140 GB of GPU memory. On H100 (80 GB), a 70B model requires two GPUs. On MI300X (192 GB), the same model fits on a single GPU.

This single-GPU fit dramatically reduces inference cost and latency:

• No inter-GPU communication overhead for KV cache and attention computation
• Lower infrastructure cost (one GPU instead of two for the same model)
• Higher tokens-per-second throughput per dollar at large batch sizes

For models above 192 GB (e.g., GPT-4 class, 400B+ parameter models), multi-GPU configurations are required on both platforms, and H100's higher NVLink bandwidth (900 GB/s vs MI300X Infinity Fabric's 448 GB/s per inter-GPU link) becomes an advantage in maintaining high GPU utilization during tensor-parallel inference.

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Memory Architecture: HBM3 vs HBM3 (192 GB)

Both H100 and MI300X use HBM3 memory, but the capacity and bandwidth differ substantially:

Parameter	H100 SXM5	MI300X
Memory type	HBM3	HBM3
Capacity	80 GB	192 GB
Bandwidth	3.35 TB/s	5.3 TB/s
Number of HBM stacks	6	8 (across 4 HBM3 packages)
HBM integration method	CoWoS (HBM adjacent to GH100 die on interposer)	3D chiplet stacking (HBM stacks on base interposer alongside XCDs)
Memory bus width	6 × 1,024 bits = 6,144 bits total	8 × 1,024 bits = 8,192 bits total

The MI300X's 192 GB capacity is its most significant competitive advantage over the base H100. The NVIDIA response to this advantage is the H200 (141 GB HBM3e at 4.8 TB/s)—which closes the gap partially—and the B200 (192 GB HBM3e at 8.0 TB/s), which exceeds MI300X on both capacity and bandwidth simultaneously.

From a PCB perspective, HBM is integrated on the accelerator package (CoWoS for H100; 3D chiplet interposer for MI300X) and does not appear as routable signals on the baseboard PCB. Both platforms route HBM signals entirely within the package substrate.

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GPU-to-GPU Interconnect: NVLink 4.0 vs Infinity Fabric

Parameter	NVIDIA NVLink 4.0 (H100)	AMD Infinity Fabric (MI300X)
Total bandwidth per GPU	900 GB/s bidirectional	448 GB/s bidirectional (per inter-GPU link)
Number of GPU-to-GPU links	18 NVLink links (via NVSwitch)	7 Infinity Fabric links (direct peer-to-peer)
Switch fabric	NVSwitch 3.0 (dedicated switch chip on baseboard)	No dedicated switch chip; direct GPU-to-GPU links via UBB routing
Topology (8-GPU node)	Fully non-blocking via NVSwitch; any GPU to any GPU at full bandwidth	Direct peer-to-peer; some GPU pairs communicate via intermediate hop
Coherent memory access	Yes (NVLink supports cache-coherent GPU memory access)	Yes (Infinity Fabric is coherent)
In-fabric collective operations	Yes (NVSwitch 3.0 supports in-fabric all-reduce)	No dedicated in-fabric reduction
Scale-out (multi-node)	InfiniBand / Ethernet (separate NIC)	InfiniBand / Ethernet (separate NIC)

NVLink 4.0's 900 GB/s bidirectional bandwidth—twice the MI300X Infinity Fabric's 448 GB/s—is a meaningful advantage for workloads that require heavy all-to-all communication between GPUs, such as tensor-parallel training of very large models where gradient exchange volume is high. For inference of models that fit in a single GPU's memory, the interconnect bandwidth difference is irrelevant.

The absence of a dedicated NVSwitch equivalent in MI300X systems means the UBB must route Infinity Fabric connections as direct point-to-point links between module slots—a simpler routing challenge than the NVSwitch-based topology, but one that limits the theoretical all-to-all bandwidth in an 8-GPU configuration. For a detailed comparison of NVLink and NVSwitch architecture, see What Is NVLink? and What Is NVSwitch?

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Host Interface: PCIe Gen5 on Both Platforms

Both H100 SXM5 and MI300X OAM use PCIe Gen5 ×16 as the host CPU interface, providing approximately 128 GB/s of bidirectional bandwidth. This is one area of true parity between the platforms—both can saturate a PCIe Gen5 link equally, and both face the same PCB signal integrity requirements for PCIe Gen5 routing on the baseboard or UBB.

PCIe Gen5 at 32 GT/s per lane requires:

• Channel insertion loss < 28 dB at 16 GHz (Nyquist)
• Backdrilling of through-hole vias to remove stubs
• Low-loss laminate on PCIe signal routing layers (Megtron 6E or equivalent)
• Differential impedance 85 Ω ± 5% (PCIe specification)

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Power and Thermal Envelope

Parameter	H100 SXM5	MI300X OAM
TDP per GPU	700 W	750 W
Total power (8-GPU node)	~5,600 W (GPU) + ~1,080 W (NVSwitch) = ~6,680 W	~6,000 W (GPU only; no separate switch chips)
Power bus	12 V (SXM5 / HGX baseboard)	48 V preferred (OAM Gen2 UBB)
Cooling requirement	Air or direct liquid cooling	Direct liquid cooling strongly preferred at 750 W
Thermal interface	Heatsink/cold plate on SXM5 module surface	Cold plate on OAM module thermal contact area (< 0.1 mm flatness)

The MI300X's slightly higher 750 W TDP vs H100's 700 W is not a significant practical difference in thermal design. Both platforms operate comfortably within liquid cooling limits; the more relevant comparison is that MI300X systems omit the NVSwitch chips that add ~1,080 W to H100 baseboards, so total rack power for an 8-GPU MI300X node is comparable to or slightly lower than an equivalent H100 node.

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PCB Design Differences: H100 vs MI300X Systems

Form Factor and Board Interface

The most fundamental PCB difference between H100 and MI300X systems is the module-to-board interface:

• H100: SXM5 socket on NVIDIA HGX baseboard; the SXM5 is a high-density land grid array (LGA) style socket with a rigid mezzanine connector; the baseboard design is NVIDIA-proprietary or NVIDIA-licensed
• MI300X: OAM edge connector on a Universal Base Board; the OAM module plugs in via an edge-card connector; UBB design is open and can be created by any ODM compliant with the OAM specification

This difference means that MI300X-based infrastructure development is accessible to a wider range of ODMs and cloud providers without requiring NVIDIA licensing, but it also means the OAM edge connector interface introduces design considerations (connector launch impedance, power pin current capacity, mechanical mating tolerance) that the SXM5 socket approach handles differently.

Layer Count

Board Type	H100 HGX Baseboard	MI300X UBB (OAM)
Typical layer count	20–24	16–22
NVSwitch routing layers	Yes (4 × NVSwitch 3.0 on baseboard)	No (no dedicated switch chips)
Inter-GPU link routing layers	NVLink 4.0 differential pairs (high density)	Infinity Fabric point-to-point links (lower density)
PCIe routing layers	PCIe Gen5 ×16 per GPU slot	PCIe Gen5 ×16 per OAM slot
Power plane layers	Multiple (12 V distribution + NVSwitch power)	Multiple (48 V distribution; higher current density per plane)

H100 baseboards require more layers primarily because of NVSwitch integration: four NVSwitch 3.0 chips on the baseboard each connect to all 8 GPUs via NVLink 4.0, generating a very high density of high-speed differential pairs that require dedicated routing layers. MI300X UBBs avoid this complexity—the Infinity Fabric links are point-to-point between module slots and require fewer routing layers—but must manage the 48 V high-current power distribution that H100 12 V designs do not.

Laminate Materials

Layer Function	H100 HGX Baseboard	MI300X UBB
Inter-GPU interconnect layers	Megtron 6E / Tachyon 100G (NVLink 4.0 at 100 Gb/s per lane)	Megtron 6 / Tachyon 100G (Infinity Fabric; lower per-lane speed)
PCIe Gen5 layers	Megtron 6E or equivalent	Megtron 6E or equivalent
Power and ground planes	Megtron 6 or standard laminate	Megtron 6 or standard laminate; 3–4 oz copper for 48 V bus
Copper foil (interconnect layers)	VLP (Very-Low-Profile)	LP or VLP depending on Infinity Fabric speed

Power Delivery

Power delivery architecture differs significantly between the two platforms:

H100 HGX Baseboard (12 V bus):

• 12 V delivered to baseboard from PSU; on-board VRMs convert to GPU VCORE (~0.9 V), NVSwitch VCORE, and auxiliary rails
• At 700 W per GPU × 8 GPUs = 5,600 W GPU power; 12 V bus current approximately 467 A for GPUs alone
• NVSwitch power adds ~1,080 W; total 12 V bus current approximately 557 A
• High current density requires multiple parallel power paths, heavy copper planes, and careful bus bar design
• PDN target impedance at GPU package: < 0.15 mΩ from DC to 100 MHz

MI300X UBB (48 V bus, OAM Gen2):

• 48 V delivered to UBB from PSU; on-module VRMs (within the MI300X OAM module) convert to accelerator core voltages
• At 750 W per module × 8 modules = 6,000 W total; 48 V bus current approximately 125 A—a 4× reduction in board-level current vs 12 V
• Lower current simplifies power plane sizing and reduces copper thickness requirements; 2–3 oz copper on 48 V planes is adequate vs 3–4 oz for 12 V high-current designs
• OAM edge connector power pins must still carry 750 W / 48 V ≈ 15.6 A per module; connector contact rating and resistance must be verified

Thermal Management

Both platforms operate at 700–750 W per accelerator, making thermal management equally critical at the board level:

• H100 SXM5: Cold plate or heatsink mounts directly to the SXM5 module surface; the HGX baseboard must accommodate cold plate mounting hardware and routing of liquid cooling lines between modules; thermal vias under SXM5 socket area transfer heat from socket pads to internal copper planes
• MI300X OAM: Cold plate contacts the OAM module thermal interface surface; the UBB does not directly carry the module's thermal load, but must manage heat from on-board components (connectors, management ICs, passive components) and maintain T_g ≥ 170°C in the high-ambient-temperature environment created by 8 × 750 W modules in close proximity

Interconnect Routing

The interconnect routing challenge differs fundamentally between the two platforms:

H100 baseboard: Must route NVLink 4.0 differential pairs (100 Gb/s per lane) between 8 GPU packages and 4 NVSwitch packages. This creates a very high density of controlled-impedance differential pairs across the board, requiring dedicated signal routing layers with ultra-low-loss laminate, VLP copper foil, tight intra-pair skew (< 5 ps), and backdrilling of all through-hole vias. Total differential pair count on the NVLink routing layers can exceed 2,000 traces. See A100 vs H100: PCB Stack Differences for detailed NVLink routing rules.

MI300X UBB: Must route Infinity Fabric point-to-point links between OAM module edge connectors. Since there is no NVSwitch equivalent, each module connects directly to several other modules via traces on the UBB. The per-link bandwidth of Infinity Fabric is lower than NVLink 4.0, meaning per-lane signaling rates are somewhat lower, relaxing but not eliminating signal integrity requirements. Impedance control (100 Ω ± 5%), intra-pair skew management, and backdrilling on PCIe Gen5 vias are all still required.

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Software Ecosystem

Dimension	NVIDIA H100	AMD MI300X
Primary compute framework	CUDA	ROCm (HIP)
ML framework support	PyTorch, TensorFlow, JAX: native CUDA support; broadest ecosystem	PyTorch, TensorFlow: ROCm support mature but still behind CUDA ecosystem
Inference runtimes	TensorRT, vLLM (CUDA), Triton Inference Server	vLLM (ROCm), MIGraphX, Triton (ROCm backend)
BLAS / kernel libraries	cuBLAS, cuDNN: highly optimized, years of tuning	rocBLAS, MIOpen: improving rapidly; performance gap narrowing
Custom kernel development	CUDA C++, PTX; extensive tooling	HIP (CUDA-like API); CUDA-to-HIP porting tools available
Model compatibility	Virtually all public models tested on CUDA first	Most major models supported; some require ROCm-specific patches

Software ecosystem maturity remains NVIDIA's most durable competitive advantage. CUDA has been the dominant GPU compute platform for over 15 years, and the volume of optimized kernels, model implementations, and tooling built for CUDA far exceeds what is available for ROCm. AMD has made significant progress closing this gap—ROCm support in PyTorch and vLLM is now production-quality—but organizations with existing CUDA codebases face non-trivial migration effort when moving to MI300X.

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Workload Fit: Which Platform for Which Use Case?

Use Case	Recommended Platform	Primary Reason
LLM inference (70B–180B parameters)	MI300X	192 GB fits large models on a single GPU; lower inference cost per token
LLM inference (1B–30B parameters)	H100 or MI300X (similar)	Both fit small models easily; cost and software ecosystem drive choice
LLM pre-training (100B+ parameters)	H100 (or B200)	NVLink 900 GB/s enables efficient tensor parallelism; Transformer Engine FP8 advantage
Fine-tuning (7B–70B)	H100 or MI300X	Memory capacity advantage of MI300X helps at 70B; H100 software ecosystem advantage at all sizes
HPC / scientific computing (FP64)	MI300X	163 TFLOPS FP64 vs H100's 34 TFLOPS; MI300X dominates FP64 workloads
Existing CUDA codebase	H100	Zero migration effort; full CUDA ecosystem compatibility
New ROCm / open-source stack	MI300X	OAM form factor, open infrastructure, growing ROCm ecosystem
Multi-vendor infrastructure	MI300X	OAM standard allows mixing accelerator vendors on common UBB hardware

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Infrastructure Comparison: DGX H100 vs MI300X Server

Parameter	NVIDIA DGX H100	8 × MI300X OAM Server (ODM)
GPUs per node	8 × H100 SXM5	8 × MI300X OAM
Total GPU memory	640 GB (8 × 80 GB)	1,536 GB (8 × 192 GB)
Total GPU memory bandwidth	26.8 TB/s (8 × 3.35 TB/s)	42.4 TB/s (8 × 5.3 TB/s)
GPU-to-GPU interconnect BW	900 GB/s per GPU (NVLink 4.0 via NVSwitch)	448 GB/s per GPU (Infinity Fabric)
Total accelerator TDP	~6,680 W (GPU + NVSwitch)	~6,000 W (GPU only)
Baseboard form factor	NVIDIA HGX H100 (proprietary)	OAM UBB (ODM-designed, OCP standard)
Host CPU	2 × AMD EPYC (DGX H100)	ODM-defined; typically 2 × AMD EPYC or Intel Xeon
Network interface	8 × 400G InfiniBand (ConnectX-7)	ODM-defined; typically 8 × 400G InfiniBand
Vendor lock-in	High (NVIDIA ecosystem)	Low (OAM standard; multi-vendor capable)

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FAQ

Is MI300X faster than H100 for AI training?
It depends on the workload. MI300X has higher peak BF16 TFLOPS (1,307 vs 989) and significantly higher memory capacity (192 GB vs 80 GB), which benefits memory-bound training of large models. H100's Transformer Engine with native FP8 delivers higher effective throughput on transformer model training. In published benchmarks on LLM training (LLaMA 2, GPT-NeoX), H100 and MI300X are broadly competitive, with H100 ahead on compute-bound workloads and MI300X competitive or ahead on memory-bound configurations.

Which GPU is better for LLM inference: H100 or MI300X?
For inference of models in the 70B–180B parameter range, MI300X is generally the preferred choice in 2025–2026. Its 192 GB of HBM3 allows these models to run on a single GPU without tensor parallelism across multiple GPUs, reducing inference latency and cost. For smaller models (< 30B parameters) that fit easily in H100's 80 GB, the choice depends more on software ecosystem maturity and price.

Can MI300X run CUDA code?
Not natively. MI300X uses AMD's ROCm software stack. AMD provides HIP (Heterogeneous-computing Interface for Portability), a CUDA-like API, and HIPIFY tools to automatically port CUDA code to HIP. Many popular frameworks (PyTorch, TensorFlow, vLLM) now have production-quality ROCm backends, but complex custom CUDA kernels require manual porting effort.

What form factor does MI300X use?
The MI300X ships in OAM (Open Accelerator Module) form factor, which is an Open Compute Project standard. It plugs into OAM-compliant Universal Base Boards (UBBs) designed by ODMs. It is not compatible with NVIDIA SXM sockets or standard PCIe add-in card slots. For more on OAM, see What Is an OAM Module?

Does MI300X have an equivalent to NVSwitch?
No. MI300X uses direct point-to-point Infinity Fabric links between GPU modules, routed on the UBB, without a dedicated switch chip equivalent to NVIDIA's NVSwitch. This simplifies the UBB design (no NVSwitch BGA placement or NVLink high-density routing) but limits all-to-all bandwidth in configurations where multiple modules need to communicate simultaneously.

How does the MI300X compare to the H200?
The H200 upgrades the H100's memory to 141 GB of HBM3e at 4.8 TB/s while retaining the same SXM5 form factor and 700 W TDP. The H200 closes the memory capacity gap with MI300X (141 GB vs 192 GB) but does not eliminate it. The MI300X retains more memory capacity (192 GB vs 141 GB) and higher raw memory bandwidth (5.3 TB/s vs 4.8 TB/s) than the H200. The B200 (192 GB HBM3e at 8.0 TB/s) matches MI300X capacity and exceeds it on bandwidth and compute.

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Related Articles:

• What Is an AI Server? Architecture, Components & PCB Requirements
• What Is an OAM Module? Open Accelerator Module Standard for AI Hardware
• What Is NVLink? How NVIDIA's High-Speed GPU Interconnect Shapes PCB Routing
• What Is NVSwitch? The Silicon Behind NVIDIA's GPU Cluster Scale-Out
• A100 vs H100: GPU Generational Leap & PCB Stack Differences Explained
• NVIDIA Blackwell Architecture Explained: B200, GB200 & PCB Design Impact