allocateKimi K2.5 1T GPU requirements
What it takes to run Kimi K2.5 1T (MoE) locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
No single GPU fits this configuration. Shard across devices, quantize harder, or run it managed.
Or skip the hardware: run Kimi K2.5 1T on Allocate, token-metered.
How it works
Common questions
How much VRAM does Kimi K2.5 1T need?
At 8K context: roughly 2205 GB at FP16, 1102 GB at 8-bit, and 606 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Kimi K2.5 1T?
Not at practical quantizations: even 4-bit needs about 606 GB, beyond any single device here. Sharding across a fleet or managed serving are the options.
Why does Kimi K2.5 1T need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 1.0 trillion parameters must sit in memory. MoE trades memory for throughput.
What license is Kimi K2.5 1T under?
Modified MIT. Permissive: run it, fine-tune it, and own the result.
Is there an alternative to buying GPUs for Kimi K2.5 1T?
Yes: managed inference. Allocate serves Kimi K2.5 1T token-metered inside your own boundary, and if you fine-tune it on your data, the weights belong to you. Idle time costs nothing.
More free tools
Allocate serves Kimi K2.5 1T token-metered inside your own boundary. No hardware to buy, and if you fine-tune it on your data, the weights are yours.