Deploying Kimi K3 on AWS

Open weight models have significantly evolved, now capable of executing complex tasks such as multi-step workflows, advanced reasoning, and intricate coding. With the increased capabilities of these models, their sizes have also grown, necessitating specialized infrastructure, high-performance GPU compute, and finely tuned serving frameworks.

On July 27, 2026, Moonshot AI introduced Kimi K3, a groundbreaking 2.8 trillion parameter Mixture of Experts (MoE) model. This release marks a milestone as the first open-weight system to venture into the 3 trillion parameter territory. Kimi K3 not only showcases superior intelligence but also allows organizations to self-host this powerful model using their infrastructure.

Understanding Kimi K3's Architecture

Kimi K3's architecture features innovative elements such as Kimi Delta Attention (KDA), Gated Multi Head Latent Attention (MLA), and a Stable LatentMoE framework. The model effectively distributes its parameters across 896 specialized experts, engaging only 16 experts per token. This strategic activation results in around 104 billion parameters being active during any forward pass, leading to a remarkable 2.5x scaling efficiency improvement over its predecessor, Kimi K2.

Key Features of Kimi K3

Kimi K3 performs exceptionally well in various tasks, including long-horizon coding, agentic workflows, and complex reasoning challenges. Its capabilities include:

Native tool calling
Structured output generation
Always-on thinking mode for multi-step problem-solving

Setting Up Kimi K3

The open weights for Kimi K3 are accessible on Hugging Face under the model identifier moonshotai/Kimi-K3. These weights are formatted in MXFP4 (Microscaling Floating Point 4-bit), striking a balance between quality and memory efficiency, making them suitable for large-scale inference deployments.

Serving Kimi K3 Efficiently

Given the model's architecture and size, serving Kimi K3 necessitates a vLLM day-0 inference container. At present, the vllm commits for Kimi K3 are found in vllm/vllm-openai:kimi-k3, with expectations to merge into the main vllm container in future updates. vLLM is the recommended serving engine due to its native support for MoE architectures, tensor parallelism, and the MXFP4 quantization format.

GPU Requirements and Deployment Strategies

Deploying a model of Kimi K3's scale requires substantial GPU resources. Specifically, Kimi K3 mandates a p6-b300 instance (ml.48xlarge) that provides 8 NVIDIA B300 Blackwell Ultra GPUs. These GPUs come with high-bandwidth interconnects, essential for efficient tensor-parallel inference across the expert pool.

AWS provides two main methods for securing this capacity. The first method is through Amazon SageMaker HyperPod, which simplifies the deployment of Kimi K3. The Inference Operator is automatically installed during the cluster setup, eliminating the complexities involved in container orchestration, model loading, and endpoint management.

Prerequisites for Deployment

Before deploying Kimi K3, it is crucial to complete the following prerequisites to ensure your infrastructure is ready:

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