Deploying Kimi K3 in 4-bit on a single 8xB300 Pod on Runpod

Moonshot AI’s Kimi K3, released in July 2026, is a 2.8-trillion-parameter open-weight multimodal mixture-of-experts model with native vision, 16 active experts per token, and support for up to one million tokens of context. Its checkpoint is already quantization-aware trained, using MXFP4 for routed experts and MXFP8 activations, although BF16 components and quantization metadata bring its 96-shard download size to roughly 1.56 TB. The guide states that an eight-GPU NVIDIA B300 pod with 2.3 TB of memory is the officially viable minimum for single-node serving, leaving enough capacity for a BF16 KV cache and approximately 128K context at modest concurrency, while smaller H100 or B200 configurations cannot load the model. Deployment requires the CUDA 13 Kimi-specific vLLM Docker image rather than a pip installation, substantial persistent storage, Hugging Face transfer caching configured on the volume, and about an hour for downloading weights and initializing the inference engine. A suggested TP8 vLLM configuration enables FlashInfer, prefix caching, tool calling, and K3 reasoning parsing, then exposes an OpenAI-compatible API on port 8000. For exploration, spiky traffic, or full-million-token workloads, Runpod’s managed public endpoint may be more economical and operationally simpler than a dedicated pod, which costs about $59 per hour plus storage and is best suited to evaluation, integration work, low-concurrency internal serving, or sustained high-volume use.

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