Kimi-K2.6 GPU Requirements: VRAM & Cheapest GPU
Kimi-K2.6 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-K2.6 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.
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.
VRAM required to run Kimi-K2.6
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-K2.6 by precision
Full precision. Best quality, highest memory.
8-bit quantized. ~2x smaller, minimal quality loss.
4-bit quantized. ~4x smaller, runs on smaller GPUs.
Inference vs fine-tuning Kimi-K2.6
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.
Deployment guideDeploy Kimi K2.6 step by stepHands-on production setup, GPU configs, and benchmarks for Kimi-K2.6.Read guideSimilar models
Compare GPU requirements for models in the same class.