- Kimi K3.1 Pro weights require at least 1.4 TB of memory across multiple GPUs.
- The model activates 104 billion parameters per token out of 2.8 trillion total parameters.
- Standard benchmark execution uses maximum reasoning effort, temperature 1.0, and top-p 0.95.
- Harnesses like Claude Code or Kimi Code enable long-horizon agentic task execution.
In this guide
What is Kimi K3.1 Pro?
Kimi K3.1 Pro is an open-weight, natively multimodal agentic model released by Moonshot AI on 24 October 2026. Built on the Kimi K3 base architecture, it features a 1-million-token context window and runs 104 billion active parameters per token.
- Developer
- Moonshot AI
- Release Date
- 24 October 2026
- Total Parameters
- 2.8 Trillion
- Active Parameters
- 104 Billion
- Context Window
- 1,048,576 tokens
- Supported Modalities
- Text, Image
Moonshot AI reports that Kimi K3.1 Pro is post-trained with reinforcement learning across hundreds of downstream domains. It is designed for complex tasks including software coding, 3D creation, and financial research.
What hardware is required for Kimi K3.1 Pro?
Running Kimi K3.1 Pro requires multi-GPU hardware configurations with at least 1.4 TB of memory to store its 4-bit MXFP4 quantized weights. Because the model is too large for any single GPU, its 896 routed experts are distributed across interconnected GPUs.
According to Analytics Vidhya, an 8-GPU NVIDIA DGX B300 node provides 2.3 TB of total memory, which fits the weights while leaving space for context memory. For production scale, system setups often deploy NVIDIA NVL72 racks linking 72 Blackwell GPUs.
How to deploy and run Kimi K3.1 Pro
Deploying Kimi K3.1 Pro involves acquiring the model weights from Hugging Face, provisioning distributed GPU nodes, and configuring execution settings for agentic tasks.
Configuring Kimi K3.1 Pro with recommended hardware and inference parameters enables long-horizon agentic task execution across a 1-million-token context.
- Download the open model weights from the official Hugging Face repository under moonshotai/Kimi-K3.1-Pro.
- Provision hardware providing at least 1.4 TB of GPU memory, such as an 8-GPU NVIDIA DGX B300 node or an NVL72 cluster.
- Load the model using MXFP4 weight quantization and MXFP8 activation parameters.
- Set the sampling parameters to maximum reasoning effort, a temperature of 1.0, and top-p at 0.95.
- Integrate an agentic execution harness such as Claude Code or Kimi Code to run coding, terminal execution, and research workflows.
What are the infrastructure trade-offs for Kimi K3.1 Pro?
Operating Kimi K3.1 Pro involves structural trade-offs between low per-token compute costs and high total memory requirements. While active parameter routing keeps compute low, storing all 2.8 trillion weights requires significant hardware.
- Reduced per-token compute cost using 104 billion active parameters
- Support for massive inputs up to 1 million tokens
- High evaluation scores on terminal coding and financial research benchmarks
- High memory footprint requiring at least 1.4 TB just to load weights
- Inter-GPU transfer latency as tokens route between experts across nodes
- Limited context memory head room on smaller 8-GPU configurations
Analytics Vidhya notes that during the writing stage, individual tokens hop across multiple GPUs to reach their assigned experts. This inter-GPU communication makes system interconnect speed critical for decoding performance.
How does Kimi K3.1 Pro process long context windows?
Kimi K3.1 Pro processes 1-million-token contexts by combining linear attention layers with sparse attention mechanisms. This design prevents memory costs from scaling quadratically with prompt length.
| Component | Specification Details |
|---|---|
| Total Layers | 93 layers (69 KDA + 24 Gated DSA + 1 Dense) |
| Attention Architecture | Kimi Delta Attention and Gated DeepSeek Sparse Attention |
| Expert Configuration | 896 experts (16 selected + 2 shared per token) |
| Quantization Precision | MXFP4 weights and MXFP8 activations |
| Vision Encoder | MoonViT-V2 (401 million parameters) |
The model replaces full-attention layers with 69 Kimi Delta Attention (KDA) layers and 24 Gated DeepSeek Sparse Attention (Gated DSA) layers. KDA uses a 128 by 128 state matrix and an error-correcting delta rule to update context memory without unbounded growth.
Frequently asked questions
Where can I download Kimi K3.1 Pro?
What GPU memory is required to run Kimi K3.1 Pro?
How many parameters does Kimi K3.1 Pro activate per token?
What sampling settings should be used with Kimi K3.1 Pro?
Does Kimi K3.1 Pro accept image inputs?
Sources: Analytics Vidhya · Hugging Face
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