Open-source LLM · Moonshot AI
Kimi K2
1000 B parameters (32 B active per token) · 128K context · Modified MIT · released 2025-07-11 by Moonshot AI.
Kimi K2 needs about 2000 GB at FP16: no single GPU can hold it. Serving requires an 8-GPU HGX baseboard (INT8/INT4) or a full rack (NVL72, TPU pod) at FP16.
Where this model sits
The largest open-weight LLM to date. Only 32B active per token means inference cost tracks a 32B model, but disk + memory footprint is enormous.
What runs it (production serving)
Datacenter GPUs and rack designs sized for Kimi K2. Consumer cards are not practical at this size.
Smallest datacenter GPU that fits (INT4)
Multi-GPU / rack-scale (FP16)
Memory at different context lengths
Weights are fixed. The KV cache grows linearly with context length and can rival the weights at full window. Most guides only quote weights, then hand-wave a 15% overhead, which is only true at short context.
| Context | FP16 total | INT8 total | INT4 total |
|---|---|---|---|
| 4K (short chat) | 2000 + 15.3 = 2015.3 GB | 1000 + 7.6 = 1007.6 GB | 500 + 7.6 = 507.6 GB |
| 32K (typical RAG) | 2000 + 122.0 = 2122.0 GB | 1000 + 61.0 = 1061.0 GB | 500 + 61.0 = 561.0 GB |
| 128K (full window) | 2000 + 488.0 = 2488.0 GB | 1000 + 244.0 = 1244.0 GB | 500 + 244.0 = 744.0 GB |
KV bytes per token = 2 × 61 layers × 128 KV heads × 128 head-dim × precision bytes. INT4 rows keep the KV cache at INT8 (typical serving default).
Quality benchmarks
Vendor-reported scores from the release blog or tech report. Every row cites its source.
| Benchmark | Score | Source | Date |
|---|---|---|---|
| MMLU (5-shot) | 89.5 | Moonshot Kimi K2 tech report | 2025-07-11 |
| HumanEval | 85.7 | Moonshot Kimi K2 tech report | 2025-07-11 |
| MATH (0-shot) | 82.5 | Moonshot Kimi K2 tech report | 2025-07-11 |
Speed and cost to serve
What cloud providers currently charge to serve Kimi K2. Tokens per second is what the provider publishes on their own page. Batch size, prompt length, and inference engine can shift these numbers a lot; treat as directional.
| Provider | Hardware | Speed | $/M in | $/M out | As of |
|---|---|---|---|---|---|
| Moonshot API | internal | — | $0.15 | $2.50 | 2025-08-01 |
Cheapest option in this list for output-heavy workloads: Moonshot API at $2.50/M out. Fastest published: not enough data.
License and variants
- License
- Modified MIT
- The gotcha
- Modified MIT requires attribution of 'Kimi K2' when total product MAU exceeds 100M or revenue exceeds $20M/mo.
- Variants
- Base · Instruct
- Context window
- 131,072 tokens (128K)
- Architecture
- MoE (1T total, 32B active per token)
Compare with
Model card: https://huggingface.co/moonshotai/Kimi-K2-Instruct · Release: Moonshot AI announcement
See also: every open-source LLM we track · all AI chips · rack designs.