# Kimi K3 Hardware Requirements

> Source: <https://openmodelmap.com/kimi-k3/hardware>
> Published: 2026-07-27 15:12:59+00:00

Everything you need to know about running the world's largest open-source model — from VRAM calculations to GPU recommendations and cost estimates.

Kimi K3 uses a **Mixture-of-Experts (MoE)** architecture with 896 experts. During inference, only 16 experts are active per token — but **all 896 experts must be loaded into VRAM simultaneously**. This is different from dense models where only the active layers need to be in memory.

The math: 2.8 trillion parameters × quantization bytes × overhead factor.

With expert parallelism (each GPU holds a subset of experts), the per-GPU requirement drops proportionally. 8 GPUs = each holds 112 experts = ~193 GB per GPU at INT4.

| Quantization | Total VRAM | GPU Setup | Est. Cost | Notes |
|---|---|---|---|---|
| FP16 | 5,600 GB | 32× H100 80GB | $800K-1M | Full precision. Only for training or maximum quality inference. Not practical for most deployments. |
| INT8 | 2,800 GB | 16× H100 80GB | $400K-500K | Good balance of quality and speed. Recommended for high-stakes applications where accuracy matters. |
| INT4 / Q4_K_M | 1,540 GB | 8× H100 80GB | $200K-250K | Recommended for most production deployments. Minimal quality loss (<1%), significant VRAM savings. |
| Q3_K_M | 1,260 GB | 7× H100 80GB or 16× A100 80GB | $175K-220K | Slightly more quality loss than Q4 but further VRAM savings. Good for cost-sensitive deployments. |
| Q2_K | 980 GB | 6× H100 80GB or 13× A100 80GB | $150K-190K | Maximum compression. Noticeable quality degradation. Use only when VRAM is severely constrained. |

Prices are approximate and subject to availability. Spot/preemptible instances offer significant discounts.

Recommended models work best on RTX 5090 (32GB VRAM). The JD.com link below may earn OMM a small commission at no extra cost to you.

⚠️ Above links point to RTX 5090 32GB. Hardware recommendations are for reference only. Verify specs before purchase. OMM is not responsible for purchase decisions.

The absolute minimum is 6× H100 80GB with Q2_K extreme quantization (~480 GB total VRAM). For practical INT4 deployment, you need 8× H100 80GB (~640 GB). A single consumer GPU like RTX 4090 (24GB) cannot run even the smallest quantized version — the model is simply too large.

No. A single RTX 4090 has 24GB VRAM, while Kimi K3 at the most aggressive Q2 quantization still needs ~980 GB total. Even with 4× RTX 4090 (96GB total), you would need expert offloading to system RAM, reducing inference speed to impractically slow (<0.1 tokens/second). For consumer hardware, use the Kimi API instead.

H100 80GB cards currently cost $25,000-30,000 each. An 8× H100 server runs $200,000-250,000. Cloud rental: ~$20-30/hour for 8× H100 on Lambda Labs or RunPod. For most users, the Kimi API (pay-per-token, ~$0.50-1.00/M tokens) is dramatically more cost-effective unless running inference 24/7.

Even with extreme quantization, Kimi K3's 2.8T parameters (all experts must be loaded) means the smallest possible model is still ~350 GB at Q1 (1-bit quantization). Consumer GPUs simply don't have enough VRAM. However, community efforts may produce expert-pruned versions (e.g., keeping only the top 100 experts) that could fit on multi-GPU consumer rigs.

⚠️ 免责声明 / Disclaimer

本文内容仅供参考，不构成任何购买建议。GPU 价格和云服务费用为估算值，实际价格因地区、供需关系和经销商而异。硬件需求基于 MoE 架构理论分析，实际 VRAM 使用取决于上下文长度、批次大小和框架优化。购买 GPU 或云服务前请根据自身需求评估。所列云服务商（Lambda Labs、RunPod、Vast.ai）与 OpenModelMap 无商业合作关系。OpenModelMap 不对因使用本文信息而产生的任何直接或间接损失承担责任。

This content is for informational purposes only and does not constitute purchasing advice. GPU prices and cloud costs are estimates — actual prices vary by region, availability, and vendor. Hardware requirements are based on MoE architecture analysis. Cloud providers listed are not affiliated with OpenModelMap. Always evaluate based on your own needs before purchasing.