Kimi K2.5’s official checkpoint is 595.178GB—not a 500GB download and not a 32B-model memory problem. That makes 600GB a mathematical file boundary, 640GB a tight experiment, and 768GB the safer default planning tier for a full-model GPU deployment.
The decisive capacity numbers
| Aggregate memory | Margin after official files | What it means |
|---|---|---|
| 576GB | 19.178GB short | The listed checkpoint files do not fit |
| 600GB | 4.822GB left | Paper fit only; virtually no general runtime allowance |
| 640GB (8×80GB) | 44.822GB left | Tight and sensitive to placement, engine, context, and workload |
| 720GB (4×180GB) | 124.822GB left | More credible, but still requires per-device validation |
| 768GB (8×96GB) | 172.822GB left | Safer planning target for useful runtime headroom |
These are file-only, aggregate calculations. They do not prove that every component will shard evenly or that a particular serving engine can use the remaining memory.
Why the obvious shortcuts fail
The publisher labels Kimi K2.5 as a one-trillion-parameter mixture-of-experts model with 32B activated parameters. Activation affects compute, but the full expert set still needs storage somewhere. The official checkpoint also declares groupwise 4-bit compressed weights while excluding many attention, shared-expert, vision, projector, and language-head modules. Multiplying one trillion parameters by four bits therefore understates the actual repository.
The stronger evidence is the repository itself: 64 listed safetensors files total 595,177,988,208 bytes. Its index metadata reports 595,148,192,736 bytes, a small 29,795,472-byte accounting difference that should be disclosed rather than silently rounded away.
Multimodal and context memory still count
Kimi K2.5 includes a 400M-parameter vision encoder, and its configuration permits up to 262,144 positions. Neither fact guarantees that a deployment can serve long text, images, or video inside the file-only margin. KV cache, framework allocations, buffers, multimodal preprocessing, batch size, and concurrency all increase live memory.
Before buying hardware
- Confirm the exact model revision and all listed checkpoint files.
- Verify that the intended engine supports Kimi K2.5 and its mixed compressed-tensors format.
- Check the memory exposed on each device and the actual interconnect topology.
- Start at short context and batch size 1, then measure peak per-device memory.
- Record any CPU or NVMe offload and its latency/bandwidth effect.
- Test text-only and multimodal workloads separately.
Full calculation and deployment notes: read the evergreen Kimi-K2.5 VRAM requirements guide.
Continue the investigation
- Plan multi-GPU memory and topology
- Compare NVIDIA data-center tiers
- Browse cited local-AI benchmarks
- Create a reproducible benchmark record
- Follow new evidence by RSS
Primary sources
- Official Kimi K2.5 model card
- Official repository API with blob sizes
- Official model configuration
- Official safetensors index
- Official deployment guidance
This is a capacity analysis, not a speed benchmark. Repository state checked October 25, 2026.