Four NVIDIA B200 accelerators and three AMD MI325X accelerators both cross Kimi K2.5’s official 595.178GB file boundary—but they do so with different capacity, topology, software, and platform tradeoffs. Four B200 180GB devices total 720GB and leave 124.822GB before runtime allocations. Three MI325X 256GB devices total 768GB and leave 172.822GB.
The capacity comparison
| Path | Aggregate memory | File-only margin | Main validation gate |
|---|---|---|---|
| 8×80GB | 640GB | 44.822GB | Tight placement and runtime headroom |
| 5×H200 141GB | 705GB | 109.822GB | Five-way topology and per-device placement |
| 4×B200 180GB | 720GB | 124.822GB | Blackwell driver, framework, and kernel readiness |
| 3×MI325X 256GB | 768GB | 172.822GB | ROCm/gfx942, server topology, and three-way placement |
| 8×96GB | 768GB | 172.822GB | Exact product, PCIe/server topology, cooling, and peer path |
| 3×MI350X/MI355X 288GB | 864GB | 268.822GB | ROCm/gfx950 plus air- or liquid-cooled platform qualification |
The figures are aggregate capacity arithmetic. They do not prove that the model can be divided evenly or that any path is faster, cheaper, or more reliable.
Why B200’s 720GB is not automatically the answer
The B200 path has substantially more margin than 8×80GB while using four devices. Its decision gate is the complete Blackwell stack: driver and CUDA versions, framework support, compressed-tensors handling, attention and multimodal kernels, communication libraries, and the exact HGX/DGX or qualified-server topology.
Why MI325X’s larger margin is not a performance ranking
Three MI325X devices provide 48GB more aggregate memory than four B200 devices and reduce the arithmetic device count. That does not establish tokens per second, latency, energy use, correctness, or value. The exact ROCm release, gfx942 support, model kernels, communication path, server qualification, placement strategy, and workload must be tested.
What to measure before buying
- Pin the Kimi K2.5 repository revision and serving engine build.
- Record every accelerator, exposed memory, topology, parallelism, placement, and offload.
- Start with short text context, batch 1, and concurrency 1.
- Record peak memory on each device—not only the sum.
- Repeat at the intended text context and image/video workload.
- Measure correctness, time to first token, prompt and generation throughput, throttling, failures, and wall power.
- Compare complete delivered-system pricing and support.
Full platform analysis: read the Kimi K2.5 hardware guide.
Continue the decision
- Check the official checkpoint arithmetic
- Compare MI325X and B200 platform requirements
- Understand multi-GPU placement limits
- Browse cited benchmark evidence
- Stage a reproducible result
- Follow new evidence by RSS
Primary sources
- Official Kimi K2.5 model card
- Official repository file metadata
- NVIDIA HGX component reference
- AMD Instinct MI325X specifications
- AMD ROCm MI350-series architecture reference
This is a capacity comparison, not a measured hardware benchmark. Sources and repository state checked October 25, 2026.