Short answer: AMD Instinct MI325X offers 256GB HBM3E on one CDNA 3/gfx942 accelerator, while NVIDIA B200 offers 180GB HBM3E on one Blackwell/GB100 accelerator. MI325X crosses the listed Qwen3.5-397B 4-bit file-size boundary on one device, but with only 20.292GB of GPTQ margin or 10.988GB for Q4 plus projector. B200 does not cross either boundary on one GPU, but it uses the CUDA/Blackwell software path and publishes up to 8TB/s memory bandwidth versus MI325X’s 6TB/s maximum peak theoretical. Capacity and peak specifications do not establish which complete system is faster for your workload.
This is a qualified-system comparison, not a desktop-card comparison. MI325X is a passive OAM module and B200 is an SXM accelerator used in HGX/DGX-class platforms. Compare complete server SKUs, topology, host resources, cooling, rack power, firmware, service and software support—not loose-module prices.
MI325X vs B200 specifications
| Specification | AMD Instinct MI325X | NVIDIA B200 |
|---|---|---|
| Architecture / target | CDNA 3 / gfx942 | Blackwell / GB100; compute capability 10.0 |
| Memory per accelerator | 256GB HBM3E with full-chip ECC | 180GB HBM3E |
| Published memory bandwidth | 6TB/s maximum peak theoretical | Up to 8TB/s |
| Host / module form | PCIe 5.0 x16 host interface; passive OAM module | SXM accelerator in qualified HGX/DGX-class platform |
| Scale-up fabric | Infinity Fabric in qualified multi-accelerator systems | Fifth-generation NVLink/NVSwitch; up to 1.8TB/s GPU-to-GPU bandwidth |
| Maximum accelerator power | 1000W TBP | Up to 1000W configurable power |
| Primary software path | ROCm with exact gfx942 support | CUDA with exact Blackwell/compute-10.0 support |
Qwen3.5-122B: where 256GB changes the decision
| Complete artifact | Files | MI325X 256GB | B200 180GB |
|---|---|---|---|
| Official GPTQ-Int4 | 78.864GB | 177.136GB file margin | 101.136GB file margin |
| Community Q4_K_M plus F16 projector | 77.446GB | 178.554GB file margin | 102.554GB file margin |
| Official FP8 | 127.163GB | 128.837GB file margin | 52.837GB file margin |
| Community Q8_0 plus F16 projector | 130.781GB | 125.219GB file margin | 49.219GB file margin |
| Official BF16 | 250.173GB | 5.827GB file margin; unsafe boundary | Does not fit; two-device arithmetic minimum |
Both accelerators provide substantial file margin for the listed 4-bit, FP8 and Q8 artifacts. The meaningful capacity change is BF16: MI325X crosses the raw file-size boundary, but 5.827GB is not credible runtime headroom after ROCm context, cache, recurrent and attention state, workspaces, temporary buffers and concurrency. Treat it as a measurement boundary, not a one-device recommendation.
Qwen3.5-397B: one-device 4-bit boundary versus multi-device planning
| Complete artifact | Files | MI325X 256GB | B200 180GB |
|---|---|---|---|
| Official GPTQ-Int4 | 235.708GB | 20.292GB file margin on one accelerator | Two-device arithmetic minimum: 360GB aggregate |
| Community Q4_K_M plus F16 projector | 245.012GB | 10.988GB file margin; very tight | Two-device arithmetic minimum: 360GB aggregate |
| Official FP8 | 406.152GB | Two-device arithmetic minimum: 512GB aggregate | Three-device arithmetic minimum: 540GB aggregate |
| Community Q8_0 plus F16 projector | 422.426GB | Two-device arithmetic minimum: 512GB aggregate | Three-device arithmetic minimum: 540GB aggregate |
| Official BF16 | 806.796GB | Four-device arithmetic minimum: 1,024GB aggregate | Five-device arithmetic minimum: 900GB aggregate |
Those device counts are division, not validated deployments. Infinity Fabric and NVLink/NVSwitch do not turn separate accelerator memories into one transparent device. The runtime must support tensor, pipeline, expert or other placement, and each accelerator needs local headroom. A smaller nominal aggregate can still be a worse usable layout if the artifact cannot be placed evenly.
ROCm gfx942 versus CUDA compute capability 10.0
MI325X and B200 require different exact software paths. For MI325X, confirm the ROCm release, gfx942 kernels, framework/container, attention and quantization implementations, communication libraries and compiled extensions. For B200, confirm the driver, CUDA toolkit, compute-10.0 build, framework/container, kernels, NVLink/NVSwitch communication path and any MIG configuration. A runtime recognizing the accelerator is not proof that the intended model, precision and distributed path are correct or fast.
Choose by the constraint you can verify
- Favor the MI325X capacity tier when a qualified ROCm/gfx942 platform is available and the single-device 235–245GB file boundary materially reduces placement complexity—but measure the tight margins.
- Favor the B200 software/platform path when the required CUDA/Blackwell stack, vendor support or existing NVIDIA infrastructure dominates, while accepting that the listed 397B artifacts are multi-GPU from the start.
- Reject a specification-only winner. Hold model revision, artifact, precision, context, input modality, batch/concurrency and quality target constant; report per-device peak memory, placement, throughput, time to first token, throttling and wall power.
Detailed guides and next actions
- AMD Instinct MI325X 256GB guide
- NVIDIA B200 180GB guide
- AMD MI300X/MI325X/MI350X/MI355X tier hub
- NVIDIA H100/H200/B200/B300 tier hub
- AMD MI300X versus NVIDIA H200
- NVIDIA B300 versus AMD MI355X
- CUDA versus ROCm compatibility guide
- Qwen3.5 122B and 397B hardware paths
- Build a reproducible benchmark record
- Follow the market with a private watchlist or RSS
Primary sources
- AMD Instinct MI325X product specifications
- AMD ROCm MI300-series architecture reference
- NVIDIA Enterprise Reference Architecture: HGX B200 components
- NVIDIA CUDA compute-capability documentation
Capacity calculations use decimal GB and nominal device capacity. Specifications and software support can change; verify the exact qualified system and current vendor documentation before purchase.