| # Kimi K3 — Open-Source Capability Inventory |
|
|
| > Every open-sourced component of the Kimi K3 release, **independently verified against its live |
| > HuggingFace or GitHub page** (2026-07-29) rather than transcribed from the technical report. |
| > Where the report and the repository disagree or the repository adds detail, the repository wins. |
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|
| The headline is that **the weights are the smallest part of the release**. Six engineering |
| repositories ship under MIT or Apache-2.0, covering the attention kernels, the expert-parallelism |
| library, and the sandbox platform that powered agentic RL — all usable without the weights, at |
| scales far below 2.8T. |
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|
| --- |
|
|
| ## 1. The inventory |
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|
| | # | Artifact | Location | License | What it is | Maps to | |
| |---|---|---|---|---|---| |
| | 1 | **Kimi K3 weights** | [`moonshotai/Kimi-K3`](https://huggingface.co/moonshotai/Kimi-K3) | **Kimi K3 License** (not MIT — §2) | Safetensors, F32/BF16/U8. **MXFP4 weights / MXFP8 activations (quantization-aware training)**. Community has produced 1 adapter, 9 finetunes, **16 quantizations**, 7 Spaces. | The model | |
| | 2 | **Kimi-K3 (GitHub)** | [`MoonshotAI/Kimi-K3`](https://github.com/MoonshotAI/Kimi-K3) | Kimi K3 License | Architecture table, evaluation tables, deployment guidance (**vLLM / SGLang / TokenSpeed**), the XTML chat template and tool-calling format. API is OpenAI- and Anthropic-compatible. | Deployment entry point | |
| | 3 | **FlashKDA** | [`MoonshotAI/FlashKDA`](https://github.com/MoonshotAI/FlashKDA) | **MIT** | CUTLASS-based high-performance CUDA kernels for Kimi Delta Attention. **Requires SM90+, CUDA 12.9+, PyTorch 2.4+.** Ships H20 and GB200 benchmark docs. | Report §5.1.1 (chunkwise kernel) | |
| | 4 | **MoonEP** | [`MoonshotAI/MoonEP`](https://github.com/MoonshotAI/MoonEP) | **MIT** | Perfectly-balanced expert parallelism via dynamic redundant experts. API: `dispatch` / `combine` / `prefetch_weight` / `reduce_grad`; zero-copy and traditional modes. Needs multi-GPU with NVLink; tests need 8+ GPUs. | Report §5.2.1 | |
| | 5 | **AgentENV** | [`kvcache-ai/AgentENV`](https://github.com/kvcache-ai/AgentENV) | **MIT** | Firecracker microVM sandbox platform with OverlayBD on-demand image loading. **Boot/resume < 50 ms, pause < 100 ms, incremental snapshot < 100 ms** even under heavy disk modification. Supports fork; ballooning reclaims guest memory to sustain high overcommit. Ubuntu 24.04 script or Docker; CLI on Linux/macOS × x86_64/arm64. | Report §5.3.2 | |
| | 6 | **Kimi-Linear** | [`MoonshotAI/Kimi-Linear`](https://github.com/MoonshotAI/Kimi-Linear) | **MIT** | The original KDA work. Two checkpoints — **Kimi-Linear-48B-A3B-Base / -Instruct**, 5.7T training tokens, 1M context. 3:1 KDA-to-MLA ratio, **up to 75% KV-cache reduction**; claims up to **6.3× throughput vs MLA at 1M tokens**; RULER@128k = 84.3. | KDA's predecessor | |
| | 7 | **MiniTriton** | [`MoonshotAI/minitriton`](https://github.com/MoonshotAI/minitriton) | **Apache-2.0** | Python-embedded DSL → MLIR → PTX tile compiler, plus an eager tensor library sharing one DSL compiler and runtime. Reverse-mode autograd, NN modules, **distributed primitives over NCCL**, sparse and visualization primitives. `examples/vecadd.py` for end-to-end compile; `examples/train_gpt.py` trains a ~50M GPT on the repo's own source as corpus. | Report §7 (case study) | |
| | 8 | **nano-kpu** | [`MoonshotAI/nano-kpu`](https://github.com/MoonshotAI/nano-kpu) | **Apache-2.0** | Verilog RTL for a nano-scale hybrid inference chip: KDA linear attention, NoPE-MLA, sigmoid-routed MoE, attention-residual mixing, int4 group-128 weights. Toolchain: Verilator 5.x + yosys ≥0.64 + Nangate45. `python3 harness/evaluate.py --quick` for functional sim. | Report §7 (case study) | |
| | 9 | **KDA context parallelism (upstream)** | [`fla-org/flash-linear-attention` PR #691](https://github.com/fla-org/flash-linear-attention/pull/691) | FLA's license | Context-parallel support for GatedDeltaNet and KDA: `FLACPContext`, communication primitives, `fla/ops/cp/`, CP-aware Triton kernels, `CausalConv1dFunctionCP` / `GatedDeltaNetWithCP` / `KimiDeltaAttentionWithCP`, plus distributed tests and a benchmark harness. **Merged 2026-01-20.** | Report §5.1.2 (KCP) | |
|
|
| ### Notes that only the repositories tell you |
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|
| - **FlashKDA auto-dispatches.** It registers itself as the backend for `chunk_kda` in |
| `flash-linear-attention >= 0.5.0`. If you install FLA at that version on SM90+ hardware you get |
| FlashKDA whether or not you asked; opt out with `FLA_FLASH_KDA=0`. **[verified]** |
| - **The upstream CP numbers are large.** PR #691 reports, against all-to-all context parallelism: |
| GDN forward **+60%** (vs 29%), GDN forward+backward **+68%** (vs 37%), KDA forward+backward |
| **+86%** (vs 55%). **[verified]** |
| - **MoonEP's advantage is flatness, not peak.** Its communication latency is below DeepEP v2 at every |
| imbalance level, but the load-bearing claim is that **iteration time stays flat as routing imbalance |
| grows** while DeepEP degrades steadily — and that static shapes prevent the fragmentation that |
| causes OOM under high imbalance. **[verified]** |
| - **`minitriton` and `nano-kpu` disclaim themselves.** Both repos state they are demonstrations of |
| K3's capability — "not a Moonshot product, and not for production use". Their value is as |
| **calibration evidence** for what autonomous model-driven engineering currently reaches, not as |
| tools. **[verified]** |
|
|
| --- |
|
|
| ## 2. The Kimi K3 License — read this before building on the weights |
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|
| The weights and the K3 repository code use the **Kimi K3 License**, which is *not* MIT. It grants |
| MIT-style breadth — "use, copy, modify, merge, publish, distribute, sublicense, and/or sell", and |
| explicitly permits creating derivative works — but adds commercial conditions standard MIT does not |
| have. **[verified]** |
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|
| | Condition | Trigger | Obligation | |
| |---|---|---| |
| | **Attribution / branding** | Product with **> 100 million monthly active users**, **or > $20M monthly revenue** | Must display **"Kimi K3" prominently on the user interface** | |
| | **Model-as-a-Service** | **> $20M (or equivalent) total revenue over any consecutive 12 months** | Must **enter a separate commercial agreement with Moonshot AI before use** | |
| | **Exemption** | Internal use; access via Moonshot AI's official products or **certified inference partners** | Exempt from the above | |
|
|
| - **Commercial use is permitted.** **Derivative works, including derivative model training and |
| distillation, are permitted** — but the same restrictions **propagate to the derivatives**. |
| - **Practical read:** below both thresholds this behaves like a permissive license. The thing to |
| track is that if you distill from K3 and redistribute the resulting weights, the conditions travel |
| with them. That is a materially different legal posture from artifacts 3–8, which are plain MIT or |
| Apache-2.0. **Do not treat the release as uniformly licensed.** |
|
|
| --- |
|
|
| ## 3. What is *not* open |
|
|
| Worth being explicit, because the release is broad enough that it is easy to assume more is included |
| than is: |
|
|
| - **No training data.** Neither the pre-training corpus, the SFT dataset, the RL task sets, nor the |
| knowledge graph used for task synthesis (report §4.2.2) is released. |
| - **No RL training code.** The environments described in report §4.2 — the unified white-box harness, |
| the verifiable agentic problems, the kernel-optimization suite with its reward-hacking detector, |
| the personal-assistant mocks, Autonomous Execution Tasks, the web-development suite — are described |
| but not shipped. AgentENV is the *sandbox runtime* underneath them, not the environments. |
| - **No in-house benchmarks.** Kimi Code Bench 2.0, Kimi Webdev Bench, MIRA, KAET, CLIF, Swarm Bench, |
| Deep Research Bench, KWV, DECK, Agent Behavior Bench and the rest of report §6.2.1 are internal. |
| - **No scaling-law data.** The 2.5× efficiency claim over K2 is presented as a fitted curve |
| (report Fig. 7) without the underlying runs, and the report does not decompose how much comes from |
| architecture vs. data vs. training recipe. |
|
|
| --- |
|
|
| ## 4. Reuse guide by scale |
|
|
| Sorted by how usable each artifact is to a team that is *not* training a 3T model. |
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| **Usable at small scale, today** |
|
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| - **`Kimi-Linear-48B-A3B` (MIT weights)** — the practical entry point to KDA. If the question is |
| "does a linear-attention hybrid hold up on my long-sequence workload", this is a runnable checkpoint; |
| you do not need to start from K3's 2.8T. |
| - **AgentENV (MIT)** — arguably the most broadly reusable piece. The pause/resume/fork/snapshot |
| semantics target a real and general problem: an agent waiting on model inference can account for |
| **up to 98% of a sandbox's lifetime**, and pausing costs nothing. Fork gives side-effect-free reward |
| judging. Sub-50 ms resume makes fine-grained branch evaluation practical. None of this requires |
| a large model. |
| - **FlashKDA (MIT)** — free if you already use FLA ≥ 0.5.0 on SM90+. Check your hardware first; |
| pre-Hopper GPUs are out. |
|
|
| **Requires real scale** |
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|
| - **MoonEP (MIT)** — needs multi-GPU NVLink and an actual expert-parallel MoE training job; tests |
| alone need 8+ GPUs. Worth reading the `E/R` redundant-expert bound proof (report Appendix E) even |
| if you never run it: it is a clean argument that a feasible balanced plan *always* exists, which is |
| what lets training never stall. |
|
|
| **Evidence, not tools** |
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|
| - **`minitriton`, `nano-kpu`** — read to calibrate what autonomous model-driven engineering currently |
| achieves. nano-kpu was built, optimized and verified in **a single 48-hour autonomous run**; the |
| design closes timing at 100 MHz within a 4 mm² analytical area budget and reaches over 8,700 tokens/s |
| simulated decode throughput with 1.46M standard cells and 0.277 MiB SRAM. MiniTriton's from-scratch |
| tensor-core matmul approaches cuBLAS at the largest shapes — about **90% of measured machine roofline** |
| — and its full-model gradients differ from torch autograd by no more than torch's own fp32 rounding |
| error (10⁻⁴), measured against an fp64 reference. **[report]** |
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| --- |
|
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| ## 5. Deployment |
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|
| Per the model card, the recommended inference engines are **vLLM** (recipes at `recipes.vllm.ai`), |
| **SGLang** (`docs.sglang.io` cookbook), and **TokenSpeed** (`lightseek.org/tokenspeed`). Hosted API |
| access is at `platform.kimi.ai` with OpenAI- and Anthropic-compatible endpoints. The recommended agent |
| framework is the Kimi Code CLI. **[verified]** |
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|
| Two usage details that bite if missed, both from the model card: thinking is enabled with a |
| configurable `reasoning_effort` (`low` / `high` / `max`), and **multi-turn conversations require |
| passing complete assistant messages back verbatim — including `reasoning_content` and `tool_calls`**. |
| The chat template only supports *preserved thinking*: the `think` channel stays in history even when |
| empty, so the model sees a consistent message structure every turn (report Appendix F). |
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