Kimi-K2.5 has about 1059B parameters. See exactly how much GPU memory it needs at FP16, INT8, and INT4, and the cheapest GPU to run it, with live hourly pricing from 5+ data center partners.
To run Kimi-K2.5 for inference at FP16, you need roughly 2308 GB of VRAM. That exceeds a single 8-GPU node, so it needs a multi-node cluster. Talk to our team for a custom configuration.
0.0
GB VRAM REQUIRED
FP16INFERENCEBATCH 1CTX 4k
Estimated peak VRAM including weights, activations, and KV cache. Add 10% headroom for production traffic.
This model needs multi-node training
Required VRAM exceeds 8× our largest single-node GPU. Talk to our team about a custom multi-node cluster.
Estimated peak VRAM at context length 4,096 and batch size 1, including weights, activations, and KV cache. Quantizing to INT8 (Q8) or INT4 (Q4) cuts memory roughly in half and in quarter.
Precision
Inference
LoRA fine-tune
Full fine-tune
FP16
2308 GB
3462 GB
9231 GB
INT8
1154 GB
1731 GB
4615 GB
INT4
577 GB
865 GB
2308 GB
Cheapest GPU to run Kimi-K2.5 by precision
FP16
VRAM required2308GB
Full precision. Best quality, highest memory.
Needs a multi-node cluster. Talk to our team for a custom configuration.
Inference only holds the model weights plus a KV cache, so it is the cheapest setup. LoRA fine-tuning adds a small adapter and roughly 50% more memory. Full fine-tuning holds gradients and optimizer state on top of the weights, which is about 4x the inference footprint, so it often needs multiple GPUs even when inference fits on one. A model this size needs multiple GPUs even for inference, and full fine-tuning multiplies that again. Browse the GPU catalog to size a node. Check the live GPU pricing for current rates.
Similar models
Compare GPU requirements for models in the same class.
Kimi-K2.5 has about 1059B parameters. At FP16 it needs roughly 2308 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 577 GB. Leave about 10% headroom for production traffic.
For FP16 inference, the cheapest fit on Spheron is a multi-node GPU cluster. If you quantize to INT4, 8x A100 80GB at about $6.56/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.
LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Kimi-K2.5. The VRAM matrix above shows the exact estimate for each setup.
We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.
Quantized to INT4, Kimi-K2.5 needs about 577 GB of VRAM, so it is too large for a single 24 GB RTX 4090 and needs a bigger card or multiple GPUs. At FP16 it needs roughly 2308 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.