Arrakis / Models / Kimi-K3

Kimi-K3 GPU requirements

Kimi-K3 has 2.8T parameters. To serve it, plan for about 6,672 GB of GPU memory in 16-bit, 3,336 GB in 8-bit or 1,668 GB in 4-bit, including about 20% for the KV cache and activations.

It is a mixture of experts: 896 experts, 16 of them active for each token. All of them stay in GPU memory, so plan memory for all 2.8T parameters.

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What it takes to serve it

GPU memory to run it for inference, with the smallest GPU setup that holds it and the lowest price on Arrakis right now.

Precision	Memory and the smallest setup
16-bit	6,672 GB: more than one 8-GPU machine
8-bit	3,336 GB: more than one 8-GPU machine
4-bit	1,668 GB: 8 × B300, from $36.80 per hour for all 8

What it takes to fine-tune it

QLoRA trains adapters on a 4-bit frozen model, LoRA on a 16-bit one, and full fine-tuning updates every weight. Figures are before activations, which grow with batch size and sequence length.

Method	Memory and the smallest setup
QLoRA	1,946 GB: 8 × B300, from $36.80 per hour for all 8
LoRA	6,672 GB: more than one 8-GPU machine
Full fine-tuning	44,479 GB: more than one 8-GPU machine

About the model

From its card and files on Hugging Face. Memory figures here follow the GPU memory guide.

Parameters: 2.8T
Experts: 896
Experts for each token: 16
Context window (tokens): 1,048,576
Published in: FP8
License: other
Released: 2026-06-13
Weights: moonshotai/Kimi-K3

Deploy it on Arrakis

Open Models in the console, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine. Or run it yourself on any machine with enough GPU memory:

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Questions

Live prices for every GPU are on the GPU prices page.

How much GPU memory does Kimi-K3 need?

To serve it, about 6,672 GB in 16-bit, 3,336 GB in 8-bit, 1,668 GB in 4-bit, including about 20% for the KV cache and activations. To fine-tune it, about 1,946 GB with QLoRA, 6,672 GB with LoRA and 44,479 GB for full fine-tuning with Adam, before activations.

Can I run Kimi-K3 on one GPU?

No. Even in 4-bit it needs about 1,668 GB: 8 B300 GPUs.

What does it cost to run Kimi-K3?

In 16-bit it needs more than one 8-GPU machine; in 4-bit, 8 B300 GPUs. Each setup on this page shows the lowest price per GPU-hour on Arrakis right now, and you pay by the hour from a prepaid balance.

How do I deploy Kimi-K3 on Arrakis?

Open Models in the Arrakis console, paste moonshotai/Kimi-K3, choose vLLM, SGLang or llama.cpp, and rent a GPU it fits on. You get an OpenAI-compatible endpoint with its own API key, and root SSH to the machine.

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