AMD MI300X vs NVIDIA H100 for LLM Serving

Two years after launch, the AMD Instinct MI300X is no longer a curiosity sitting on a marketing slide. It is a serious sovereign-procurement option, with 192 GB of HBM3, 5.3 TB/s of memory bandwidth, and a ROCm software stack that finally serves open-weight LLMs in production through vLLM and SGLang. For an Omani institution sizing a Tower or Rack against the hyperscaler-constrained H100 supply chain, the MI300X is worth pricing seriously. This is the head-to-head: spec sheet, ROCm reality, real workloads, and the buying decision. It pairs with the broader H100 H200 RTX 6000 Mac Studio comparison.

Spec sheet, side by side

The headline numbers come from the vendor datasheets. AMD's Instinct MI300X product page lists 192 GB of HBM3 at 5.3 TB/s of aggregate bandwidth. NVIDIA's H100 SXM page lists 80 GB of HBM3 at 3.35 TB/s. On the compute side, both cards land in the same generation: roughly 1,300 FP16 tensor TFLOPS on MI300X and roughly 989 on H100, and roughly 2,600 FP8 TFLOPS on MI300X versus 1,979 on H100.

• Memory capacity: 192 GB vs 80 GB, a 2.4x ratio. A 70B model in FP16 (140 GB) fits on a single MI300X with headroom. On an H100 it requires two cards and tensor parallelism. A 120B-class model in FP8 fits on a single MI300X.
• Memory bandwidth: 5.3 vs 3.35 TB/s, a 1.58x ratio. Every byte the GPU must read or write to deliver a token moves 58% faster on the MI300X.
• Compute (FP16/FP8 tensor): roughly 1.3x in AMD's favour. The matrix-multiply ceiling is higher on MI300X, though most LLM serving is bandwidth-bound and never approaches it.
• Form factor. MI300X ships in 8-GPU OAM platforms from Dell PowerEdge XE9680, HPE Cray XD, and Supermicro AS-8125GS-TNMR2 chassis. H100 ships in equivalent 8-way HGX baseboards. Rack thermals and power are similar within 5%.

On paper, the MI300X wins on every dimension that matters for sovereign LLM serving. The question is whether the software stack lets you cash in.

ROCm software-stack reality, 2026

ROCm in 2024 was the reason institutions hesitated. ROCm in 2026 is a different conversation. Three things changed.

First, vLLM, the dominant open-source serving engine for sovereign deployments, ships first-class ROCm support. The ROCm AI Developer Hub vLLM tutorial documents the production path, with prebuilt containers, Hugging Face model loading, FP8/INT8 quantisation, and continuous batching. SGLang and TGI add second and third options for teams with different operational preferences.

Second, the kernel libraries that matter (FlashAttention 2 and 3, paged attention, AWQ, GPTQ, FP8 quantisation) are upstream-supported on MI300X. The performance gap that existed in 2024 between hand-tuned CUDA kernels and ROCm equivalents has closed to within 5 to 10 percent on most LLM operators. AMD's MK1 collaboration and the public ROCm 6.x roadmap continue to compress that gap.

Third, fine-tuning is workable, not first-class. Parameter-efficient methods (LoRA, QLoRA) run on ROCm. Full SFT runs on ROCm. The friction is in research codebases that ship CUDA-only operators or that depend on bitsandbytes builds that lag the CUDA equivalent. For a sovereign institution that uses a stable serving stack and trains adapters on it, this is rarely the binding constraint. For a research lab that adopts last-week's GitHub repo every Monday, NVIDIA remains less friction.

The honest summary: ROCm is now production-ready for the LLM serving and adapter-training workloads sovereign institutions actually run. It is still not as smooth as CUDA for arbitrary research code.

Real-world LLM throughput

Specifications and ROCm maturity translate into the numbers a procurement officer can defend. Independent and AMD-authored benchmarks on vLLM and SGLang in 2025 to 2026 paint a consistent picture.

• 27B Arabic model, FP16, 16K context, single user streaming. H100 sustains around 60 tokens per second. MI300X sustains around 80 to 90. Both feel fluent.
• 70B model, FP16, 32K context, single user. A single MI300X (no TP needed) sustains roughly 30 to 35 tokens per second. Two H100s with TP=2 sustain roughly 24 to 30. The single-card simplicity is itself an operational win.
• 120B-class model, FP8. Fits on a single MI300X. Requires two H100s and TP=2 or aggressive quantisation, with the deployment complexity that carries.
• Concurrent users, 27B Arabic model, continuous batching, P50 first-token under 300 ms. One H100 serves roughly 50 to 70 sessions. One MI300X serves roughly 110 to 150, driven by both the 192 GB capacity (more KV cache headroom) and the 5.3 TB/s bandwidth.

For batch-only workloads (overnight summarisation, archival processing) the per-card uplift is smaller because compute, not bandwidth, becomes the binding constraint. For interactive long-context Arabic ministerial workloads, the MI300X advantage compounds with context length.

When to choose AMD

Three buying patterns make AMD the right answer in 2026, and two make NVIDIA the right answer.

Choose MI300X when:

• The workload is interactive LLM serving (vLLM, SGLang, TGI) of open-weight models (Falcon Arabic, Qwen 3.6, Gemma 4, DeepSeek R1).
• You need to host a single 70B-class or 120B-class model without tensor parallelism, for operational simplicity or for budget-bounded single-card deployments.
• H100 supply or lead time is the binding constraint and a 6-to-12-month procurement window is unacceptable.

Choose H100/H200 when:

• The institution depends on CUDA-only research codebases or arbitrary GitHub repos that change weekly.
• You need NVLink-class multi-GPU coherence for very large training runs (rare for sovereign customers, but real for some defence research labs).

Both cards land cleanly in Hosn Tower (single GPU) and Hosn Rack (multi-GPU) configurations. We size MI300X and H100/H200 side by side against the institution's actual concurrency, latency, model footprint, and supply window. Pricing is by quotation. Email [email protected] or message us on WhatsApp at +968 9889 9100 for a one-hour briefing where we walk through the numbers on your workload, not the marketing brochure.