KIMI K3 OPEN WEIGHTS — JULY 27, 2026
● Release date: By July 27, 2026 (Sunday)
● Model: 2.8T parameters, 896 experts, 16 active per token (Stable LatentMoE)
● Quantisation: MXFP4 training-aware — Q4 and Q8 expected in community releases
● Minimum deployment: 64 accelerators recommended by Moonshot AI
● Context window: 1M tokens (Allegretto+ equivalent)
● Vision: Native multimodal
● Why self-host: Eliminates China NI Law data residency risk on Moonshot-hosted API
● License: TBC — Moonshot has not confirmed license terms as of July 25
Why Self-Hosting K3 Changes the Calculation
The Moonshot-hosted Kimi K3 API carries a real data residency risk: Moonshot AI is a Chinese company, and China's National Intelligence Law requires Chinese companies to cooperate with government intelligence requests. For regulated industries — finance, healthcare, legal, defence — this means the Moonshot API is off the table regardless of K3's benchmark performance. Self-hosting K3 on AWS, Azure, or GCP Western regions removes Moonshot from the data chain entirely. Your inference runs on US or EU infrastructure, under your cloud provider's data agreements, with no Chinese company in the loop. The model weights themselves are open — what you do with them on your own infrastructure is governed by your cloud provider and applicable local law, not Moonshot.
Moonshot AI recommends at least 64 accelerators for deployment of K3. At 2.8 trillion total parameters with 16 active per token at inference time, K3 behaves like a ~50B dense model computationally — but stores 2.8T parameters across all experts in memory. That storage requirement is what drives the GPU count.
GPU Requirements by Quantisation Level
| Quantisation |
Model size |
VRAM needed |
GPU option |
Quality vs full precision |
| FP16 (full) |
~5.6TB |
5,600GB+ |
100+ H100 80GB |
Reference quality |
| Q8 |
~2.8TB |
2,800GB+ |
~35+ H100 80GB |
Near-lossless |
| Q4 (MXFP4) |
~1.4TB |
1,400GB+ |
~18 H100 80GB |
Sweet spot — trained MXFP4-aware |
| Q2 (aggressive) |
~700GB |
700GB+ |
~9 H100 80GB |
Meaningful quality loss expected |
VRAM estimates approximate — actual requirements depend on KV cache size, batch size, and context length in use. Q4 MXFP4 is likely the practical deployment target: K3 was trained with MXFP4 quantisation awareness, meaning Q4 quality degradation should be minimal relative to models not trained this way.
Cloud Cost Estimates — Q4 Deployment
| Provider |
Instance / Config |
GPU count |
On-demand $/hr |
Spot/reserved est. |
| AWS |
p5.48xlarge (8×H100 80GB) ×3 |
24 H100 |
~$98/hr |
~$40-55/hr reserved |
| Azure |
ND H100 v5 ×3 nodes |
24 H100 |
~$95/hr |
~$38-50/hr reserved |
| GCP |
a3-megagpu-8g ×3 |
24 H100 |
~$100/hr |
~$42-55/hr committed |
Cost for Q4 at 18-24 H100 80GB. Actual throughput depends on batch size and sequence length. At $50/hr reserved and 100 tokens/second throughput, self-hosted K3 Q4 generates tokens at roughly $0.14/1K tokens — comparable to DeepSeek V4 Flash API pricing but with full data sovereignty. For high-volume workloads (10M+ tokens/day), self-hosting pays back within weeks vs Moonshot API at $3/M.
Serving Stack — What to Run It On
vLLM (recommended): The standard serving framework for large MoE models. Supports tensor parallelism across multiple GPUs, continuous batching, and OpenAI-compatible API endpoint. K3's MoE architecture is well-supported in vLLM 0.5+. Start here.
SGLang: Alternative to vLLM with strong MoE support and slightly better throughput on some workloads. Worth benchmarking against vLLM for your specific use case once weights are available.
llama.cpp (Q4 only): For teams without H100 access, llama.cpp Q4 on consumer A100 clusters is a possibility for dev/test workloads. Not recommended for production inference at scale — throughput will be severely limited.
Wait for managed hosting: Within days of weights dropping, Together AI, Fireworks AI, Groq, and Replicate will likely offer managed K3 inference at competitive per-token rates on Western infrastructure. This gives you US data residency without managing your own cluster. Check these providers on July 27-28.
What to Watch For on July 27
License terms: Moonshot has not confirmed the license as of July 25. DeepSeek V4 used MIT for weights. Llama uses Meta's custom license. A permissive license (MIT, Apache 2.0) means commercial self-hosting is clear. A restrictive license (non-commercial only, or with usage caps) changes the calculation. Confirm license before deploying in production.
Quantisation quality: K3 was trained with MXFP4 quantisation awareness, which means Q4 degradation should be lower than models without this. Independent benchmark comparisons between full-precision hosted API and Q4 self-hosted will appear within 24 hours of weights dropping — watch HuggingFace, Reddit r/LocalLLaMA, and the K3 GitHub release thread.
Managed inference providers: Together AI and Fireworks typically list major open-weight models within 48 hours of weight release. A managed K3 endpoint on Western infrastructure at competitive pricing may be available by Monday July 28 — check before spinning up your own cluster.
Sources: Moonshot AI technical blog · Towards AI Newsletter TAI #214 · Tom's Hardware K3 coverage · BenchLM · Related: Kimi K3 full review and benchmarks → · Kimi K3 vs Claude Opus 4.8 → · Kimi K3 vs GPT-5.6 Sol →