AMD MI300X vs NVIDIA H200 for Local AI

Short answer: AMD Instinct MI300X offers 192GB HBM3 per accelerator, while NVIDIA H200 offers 141GB HBM3e. The extra 51GB lets MI300X hold some artifacts with materially more device headroom, but capacity alone does not select a winner. Choose from the exact model, ROCm-versus-CUDA support, server platform, multi-accelerator topology, measured throughput, power and complete deployment cost.

MI300X vs H200 specifications

Decision factor	AMD Instinct MI300X	NVIDIA H200
Architecture	AMD CDNA 3 (gfx942)	NVIDIA Hopper (GH100; compute capability 9.0)
Memory per accelerator	192GB HBM3 with full-chip ECC	141GB HBM3e
Published bandwidth	5.3TB/s maximum peak theoretical	4.8TB/s
Primary software lane	ROCm	CUDA
Form factor	Passive OAM module	SXM, or H200 NVL dual-slot air-cooled PCIe for qualified systems
Published power	750W maximum TBP	SXM configurable up to 700W; NVL configurable up to 600W
Eight-accelerator memory	1.5TB aggregate HBM3 on the MI300X platform	1.1TB aggregate HBM3e on HGX H200

Qwen3.5-122B fit: where 192GB changes the answer

Complete artifact	Files	H200 141GB	MI300X 192GB
Official GPTQ-Int4	78.864GB	Fits by files with substantial capacity margin	Fits by files with larger capacity margin
Community Q4_K_M + F16 projector	77.446GB	Fits by files; verify projector and runtime placement	Fits by files; verify projector and runtime placement
Official FP8	127.163GB	Fits by files, but only 13.837GB nominal decimal margin remains	Fits by files with 64.837GB nominal decimal margin
Community Q8_0 + F16 projector	130.781GB	Fits by files, but only 10.219GB nominal decimal margin remains	Fits by files with 61.219GB nominal decimal margin
Official BF16	250.173GB	Does not fit on one accelerator	Does not fit on one accelerator

Qwen3.5-397B requires multiple accelerators

Official artifact	Files	Minimum H200 nominal aggregate	Minimum MI300X nominal aggregate
GPTQ-Int4	235.708GB	2×141GB = 282GB	2×192GB = 384GB
FP8	406.152GB	3×141GB = 423GB, a tight aggregate file boundary	3×192GB = 576GB
BF16	806.796GB	6×141GB = 846GB, a tight aggregate file boundary	5×192GB = 960GB

ROCm versus CUDA is a workload test, not a slogan

MI300X requires an exact ROCm-supported operating system, container, framework, gfx942 kernels, quantization path and model architecture. H200 requires the matching NVIDIA driver, CUDA toolkit, Hopper kernels, framework and extensions. A framework name appearing on both platforms does not prove identical operator coverage, numerical behavior or speed.

• Verify Qwen3.5 MoE routing, hybrid attention, MTP, multimodal projector and chosen precision on the exact runtime release.
• Record container digest, driver, toolkit, framework, model revision and quantization.
• Run a fixed prompt-processing and generation workload before comparing results.
• Treat vendor peak FLOPS and bandwidth as specifications, not observed token rates.

Multi-accelerator topology decides whether aggregate memory is useful

AMD’s eight-accelerator MI300X platform provides 1.5TB aggregate HBM3 with fully meshed Infinity Fabric connectivity. NVIDIA’s HGX H200 reference platform provides about 1.1TB aggregate HBM3e across eight GPUs with NVLink/NVSwitch. Those totals do not appear as one device. The runtime must implement tensor, pipeline, expert or layer placement, and communication can dominate a poor split.

For every result, report per-device placement, local peak memory, topology, interconnect traffic, CPU/system-memory offload, context, batch size and concurrency. The benchmark builder includes fields for those details.

Power and acquisition comparisons need the complete system

MI300X’s 750W figure, H200 SXM’s configurable-up-to-700W figure and H200 NVL’s configurable-up-to-600W figure describe accelerator limits, not wall power for an equivalent inference job. A complete server also powers CPUs, host memory, storage, networking, fans or liquid cooling, and power-conversion losses. Compare measured wall energy for the same workload and include the qualified platform, warranty and support—not a bare-module listing.

A defensible selection sequence

1. Name the exact model repository, revision, files, precision and projector.
2. Confirm the production runtime on the exact ROCm or CUDA software stack.
3. Choose one- or multi-accelerator placement and calculate local—not only aggregate—headroom.
4. Verify the complete qualified server, topology, cooling, power, firmware and management path.
5. Benchmark identical context, input modality, output length, batch size and concurrency.
6. Measure peak device/system memory, time to first token, prompt processing, generation, errors, throttling and wall power.
7. Compare complete acquisition or hosting cost and operational support.

Continue with the MI300X guide, H200 guide, Qwen3.5-122B guide, Qwen3.5-397B guide, 96GB-to-1TB hardware paths, multi-GPU memory guide, workload test or private watchlist/RSS page.

Primary sources

• AMD Instinct MI300X specifications
• AMD MI300X datasheet
• AMD MI300X platform acceptance guide
• NVIDIA H200 product page
• NVIDIA HGX H200 reference architecture
• HighVRAM methodology

Reviewed October 25, 2026. Specifications are manufacturer-published. HighVRAM has not independently benchmarked every platform, runtime or workload. Arithmetic file fit does not establish compatibility, speed, quality, reliability or value.

Next-generation capacity comparison: the MI325X versus B200 guide moves this cross-vendor decision to 256GB AMD and 180GB NVIDIA tiers with exact Qwen3.5 file boundaries.

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Would 256GB on one AMD accelerator change the placement? Continue with the MI325X 256GB guide, including one-device 397B 4-bit boundaries and the 2.048TB platform.

AMD’s newer 288GB option is covered in the MI350X vs MI355X local-AI guide, including its different cooling and power requirements.

Choosing among AMD generations? Compare MI300X, MI325X, MI350X and MI355X before selecting the NVIDIA comparison point.