Short answer: eight H100 80GB GPUs provide 640GB of aggregate memory, which exceeds Kimi K2.5’s official 595.178GB checkpoint files by 44.822GB. So the weights fit by aggregate arithmetic. That does not guarantee a working deployment: the average remainder is only 5.603GB per GPU before runtime allocations, and the model may not partition evenly.
Verdict: 8×H100 80GB is a tight configuration to validate, not a comfortable recommendation. If you cannot test the exact software and topology before committing, plan a larger-capacity path.
The exact calculation
| Item | Capacity |
|---|---|
| Official Kimi K2.5 safetensors files | 595.178GB |
| Eight H100 80GB GPUs | 640GB aggregate |
| Aggregate file-only margin | 44.822GB |
| Average margin per GPU if perfectly balanced | 5.603GB |
The official repository lists 64 safetensors files totaling 595,177,988,208 bytes. Its index metadata reports 595,148,192,736 bytes. This guide uses the actual listed blob total for the file-capacity boundary.
Why aggregate fit can still fail
- Uneven placement: attention, shared experts, vision components, projector, and language head may not divide like routed experts.
- Runtime allocations: framework state, CUDA context, kernels, communication buffers, temporary tensors, and allocator fragmentation consume memory.
- KV cache: context length, batch size, concurrency, precision, and engine design determine live cache demand.
- Multimodal processing: images and video introduce preprocessing and live-memory demands beyond static weights.
- Software support: the runtime must understand Kimi K2.5’s architecture and mixed compressed-tensors checkpoint.
H100 identity matters
“H100 80GB” can describe PCIe or SXM products in different servers and topologies. Do not quote eight cards without recording the exact form factor, memory exposed per device, NVLink/NVSwitch or PCIe topology, MIG state, power mode, cooling, driver, CUDA version, framework, serving engine, and model revision.
H100 NVL is a distinct 94GB-per-GPU product. Seven nominal 94GB devices total 658GB and leave 62.822GB of aggregate file margin, but a seven-way topology is not automatically supported or preferable. Keep 80GB and 94GB calculations separate.
What larger paths change
| Path | Aggregate memory | File-only margin | What improves |
|---|---|---|---|
| 8×H100 80GB | 640GB | 44.822GB | Weights fit by sum; runtime margin remains tight |
| 5×H200 141GB | 705GB | 109.822GB | More aggregate headroom with five devices |
| 4×B200 180GB | 720GB | 124.822GB | More headroom and fewer devices; Blackwell software is a separate gate |
| 8×96GB | 768GB | 172.822GB | Safer capacity tier; exact platform still matters |
| 3×MI325X 256GB | 768GB | 172.822GB | Fewer devices; ROCm and placement remain separate gates |
Larger file margins do not prove higher throughput, lower latency, better correctness, lower energy use, or better value.
Minimum validation procedure
- Pin the exact Kimi K2.5 revision and verify all 64 checkpoint files.
- Confirm the engine supports the architecture and mixed compressed-tensors format.
- Record exact H100 form factor, topology, MIG state, exposed memory, parallelism, placement, and offload.
- Start with short text context, batch size 1, and concurrency 1.
- Record peak memory on every H100 while loading and generating.
- Increase context and concurrency gradually; test image/video inputs separately.
- Measure correctness, failures, time to first token, prompt/generation throughput, throttling, and wall power.
Decision rule
Use 8×H100 80GB when the hardware is already available or the exact configuration can be proven before purchase. If the deployment must support long context, multimodal input, concurrency, or operational safety margin without aggressive tuning, evaluate 705GB–768GB and larger paths. If CPU or storage offload is acceptable, disclose it and measure the latency/bandwidth tradeoff rather than describing the run as fully GPU-resident.
Continue planning
- Read the full Kimi K2.5 VRAM analysis
- Compare ten hardware capacity paths
- Check H100 PCIe, SXM and NVL distinctions
- Understand multi-GPU placement limits
- Browse cited benchmark evidence
- Stage a reproducible Kimi K2.5 result
Primary sources
- Official Kimi K2.5 model card
- Official repository API with blob sizes
- Official Kimi K2.5 configuration
- Official Kimi K2.5 deployment guidance
- NVIDIA H100 specifications
- NVIDIA MIG user guide
Capacity analysis only; no H100 Kimi K2.5 performance result is claimed. Repository and specifications checked October 25, 2026.