NVIDIA H100 vs AMD MI300X: AI GPU Comparison
The short answer: AMD’s Instinct MI300X and MI325X win on raw memory capacity and price per gigabyte, while NVIDIA’s H100, H200 and B200 win on ecosystem maturity, interconnect at rack scale and the depth of the CUDA software stack. If your workload is memory-bound and your team can invest in ROCm, AMD often delivers more usable HBM per dollar. If you need the broadest framework support, the largest reference designs and the least porting risk, NVIDIA remains the default. Both are current, buyable parts as of 2026-08, sold through OEM and integrator channels rather than at a fixed price.
This comparison looks at NVIDIA H100/H200/B200 against AMD Instinct MI300X/MI325X on the factors that decide real purchases: HBM capacity, memory bandwidth, software ecosystem, and effective price per gigabyte. The aim is to help you weigh a memory-and-cost advantage against an ecosystem-and-scale advantage before committing budget across the AI GPU and accelerator category. Figures are indicative ranges, not quotes, and HBM supply constraints keep them moving; confirm by RFQ.
Memory: AMD’s headline advantage
Memory capacity is where AMD differentiates most sharply. The MI300X ships with 192GB of HBM3 at 5.3 TB/s, and the MI325X pushes to 256GB of HBM3e at 6.0 TB/s. Against NVIDIA’s 80GB H100 and 141GB H200, that is a large per-GPU capacity lead, which matters directly when a model, its KV cache or a long context will not fit in fewer NVIDIA GPUs. A single MI300X holding a model that would otherwise span two H100s can cut GPU count, networking and power for a given serving job.
NVIDIA closes part of the gap at the top of its stack. The B200 reaches roughly 180–192GB of HBM3e at about 8 TB/s, matching MI300X capacity while leading on bandwidth. So the memory story is not simply “AMD more, NVIDIA less”; it depends on which specific parts you compare.
Ecosystem: NVIDIA’s headline advantage
Hardware only ships value through software. NVIDIA’s CUDA ecosystem has a long lead in framework coverage, optimized libraries, documentation and third-party tooling, and most published models and kernels target it first. AMD’s ROCm has matured considerably and supports the major training and inference frameworks, but teams switching from CUDA should budget time for porting, kernel tuning and validation. For organizations already standardized on NVIDIA, that switching cost is a real line item; for greenfield teams or those with in-house systems expertise, it is more manageable.
Interconnect is part of the same story. NVIDIA’s NVLink and rack-scale NVL72 designs provide very high bisection bandwidth for large training jobs, whereas AMD relies on Infinity Fabric at 896 GB/s per MI300X. For the largest synchronous training runs, NVIDIA’s fabric advantage can outweigh AMD’s per-GPU memory lead.
Specification and price comparison
Figures are representative parts as of 2026-08. Prices are indicative per-GPU market ranges, confirmed by RFQ.
| Spec | AMD MI300X | AMD MI325X | NVIDIA H100 | NVIDIA H200 | NVIDIA B200 |
|---|---|---|---|---|---|
| HBM capacity | 192GB HBM3 | 256GB HBM3e | 80GB HBM3 | 141GB HBM3e | 180–192GB HBM3e |
| Bandwidth | 5.3 TB/s | 6.0 TB/s | 3.35 TB/s | 4.8 TB/s | ~8 TB/s |
| TDP | 750W | ~1000W | 700W | 700W | 1000W |
| Form factor | OAM | OAM | SXM5 | SXM | SXM |
| Interconnect | Infinity Fabric 896 GB/s | Infinity Fabric | NVLink 900 GB/s | NVLink 900 GB/s | NVLink 1.8 TB/s |
| Indicative price | ~$10,000–15,000 | ~$15,000–20,000 | ~$35,000–40,000 | ~$32,000–50,000 | ~$40,000–80,000 |
Price per gigabyte and total cost
On memory economics, AMD is difficult to beat. An MI300X at roughly $10,000–15,000 for 192GB works out to a fraction of the per-gigabyte cost of an H100 or H200. For memory-bound inference where capacity, not compute, sets the GPU count, that gap can lower cluster cost meaningfully. At the node level, an 8-GPU MI300X system runs roughly $240,000–300,000 versus $310,000–370,000 for an 8-GPU HGX H100 node.
Price per gigabyte is not the whole picture, though. Software porting effort, framework maturity, support and resale liquidity all feed total cost of ownership, and NVIDIA’s ecosystem often reduces engineering time even when its hardware costs more up front. The right call depends on workload profile and team capability; our accelerator desk can model both against live availability and current export-control status.
FAQ
Does the MI300X have more memory than the H100? Yes. The MI300X carries 192GB of HBM3 versus 80GB on the H100 SXM5, so a single MI300X can hold far larger models or longer contexts per GPU.
Is AMD’s software ready for production AI? ROCm has matured and supports major frameworks and inference stacks, but NVIDIA’s CUDA ecosystem remains broader and better documented. Budget porting and validation time when switching.
Which is cheaper per GB of memory? AMD, clearly. At roughly $10,000–15,000 for 192GB, the MI300X offers far more HBM per dollar than an H100 or H200, which is its central commercial argument.
How does the MI325X compare to the MI300X? The MI325X raises capacity to 256GB HBM3e and bandwidth to 6.0 TB/s at around 1000W, positioning it against the H200 and B200 on memory-heavy workloads.
Are there export restrictions on AMD accelerators? Yes. The MI325X and certain NVIDIA parts appear in 2026 US export-control changes, so cross-border sales, especially into China, may face licensing constraints. Quotes should factor destination.
Bottom line
Choose AMD Instinct when memory capacity and price per gigabyte drive the decision and your team can invest in ROCm; choose NVIDIA when ecosystem, interconnect and porting risk dominate. Browse the full AI GPUs and accelerators category to compare parts, and request a quote with your workload and destination for an indicative price against live stock.