--- title: "Kimi-K2-Instruct | Aguasto" description: "Research reference for Kimi-K2-Instruct: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented." canonical: "https://www.aguasto.com/models/kimi-k2-instruct" last-updated: "2026-10-12" --- # Kimi-K2-Instruct | Aguasto Canonical HTML: [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) > Research reference for Kimi-K2-Instruct: official checkpoint, license and memory considerations. Documented in Aguasto Research; no Aguasto deployment or API availability is represented. [Aguasto](https://www.aguasto.com/) [Model Index](https://www.aguasto.com/models) Kimi-K2-Instruct [Resources](https://www.aguasto.com/resources) [GPU Library](https://www.aguasto.com/gpus) [Model Index](https://www.aguasto.com/models) [Inference Stack](https://www.aguasto.com/inference-stack) [Learn](https://www.aguasto.com/learn) [Insights](https://www.aguasto.com/insights) [Research](https://www.aguasto.com/research) [Engineering tools](https://www.aguasto.com/tools) Moonshot AI / Open-weight checkpoint reference ## Kimi-K2-Instruct Documented in Aguasto Research. This checkpoint has not been evaluated or deployed by Aguasto and is not available through an Aguasto API. Original Aguasto conceptual illustration. Not manufacturer geometry, an operating Aguasto asset, or a measured benchmark. Aguasto Research documents third-party hardware, models and software. References do not represent Aguasto inventory, evaluated deployments or available inference services. ### Checkpoint identity Official identifier moonshotai/Kimi-K2-Instruct[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Architecture Mixture-of-experts language model with Multi-head Latent Attention[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Total parameters 1,000 billionPublished rounded counts; total stored weights determine the preliminary footprint, including all MoE experts.[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Active parameters 32 billion per tokenActive parameters are a compute-related count, not the model's full stored weight count.[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Modalities text-input, text-output[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Context 131,072 tokensNative configuration where directly verified. Extensions, advertised K-labels and configuration differences are preserved in the notes below.[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Release date Exact release date not independently established in the reviewed artifacts[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Download access Public checkpoint reference[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) License Modified MITMIT-style permission and copyright notices have an added commercial attribution condition: products or services exceeding 100M monthly active users or US$20M monthly revenue must prominently display Kimi K2 in their interface. This is not plain MIT or an unconditional commercial approval.[Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) ### Theoretical weight-memory footprint Using the publisher's approximately 1000 billion total parameters at 16 bits per parameter gives **2,000 GB** (1862.6 GiB) of nominal weights. weight GB = total parameters in billions × bits per weight / 8 This estimate excludes KV cache, activations, quantization metadata, non-weight buffers and serving overhead. It is not a tested GPU fit. MoE routing activates only part of the network per token while the complete expert weights still need storage. [Explore the memory estimator](https://www.aguasto.com/tools) #### Quantization The selected checkpoint configuration uses blockwise FP8. Quantized derivatives and runtime kernels need independent compatibility and quality checks. [Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) ### Serving considerations - 1T total and 32B active are publisher-rounded approximate counts; the numeric total is 1,000 billion. All expert weights, cache and communication must be accounted for in deployment sizing. - The card states 128K context and its configuration specifies 131,072 positions. This Instruct checkpoint is distinct from later Kimi K2 variants. - Tool use needs Kimi-native parsing and correctly formatted tool messages. Aguasto has not evaluated or deployed the model; no facility or serving-capacity claim follows from this reference. [Moonshot AI Kimi-K2-Instruct model card (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct) · [Kimi-K2-Instruct checkpoint configuration (external source)](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) · [Kimi K2 Modified MIT License (external source)](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) #### Framework documentation These are third-party documentation starting points. Support depends on the exact checkpoint, runtime version and hardware. ### Continue the research [vLLM](https://www.aguasto.com/inference-stack/vllm) [SGLang](https://www.aguasto.com/inference-stack/sglang) [Compare model references](https://www.aguasto.com/models/compare) [GPU specification library](https://www.aguasto.com/gpus) [Weights, cache and serving overhead](https://www.aguasto.com/learn/model-weights-and-runtime-memory) ### Sources & verification Reference revision 1 · Reviewed October 12, 2026. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [Moonshot AI Kimi-K2-Instruct model card ↗](https://huggingface.co/moonshotai/Kimi-K2-Instruct) 1T total / 32B activated rounded counts; text MoE architecture with MLA; 128K advertised context; native tool-parser requirement; Modified MIT declaration; open access; vLLM / SGLang references. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [Kimi-K2-Instruct checkpoint configuration ↗](https://huggingface.co/moonshotai/Kimi-K2-Instruct/blob/main/config.json) 131072 max\_position\_embeddings; blockwise FP8 config; kimi\_k2 architecture. Publication: date not stated in the reviewed source. Verified: 2026-10-12. - [Kimi K2 Modified MIT License ↗](https://github.com/MoonshotAI/Kimi-K2/blob/main/LICENSE) Added attribution threshold is more than 100M monthly active users OR more than US$20M monthly revenue; copyright and permission-notice preservation. Publication: date not stated in the reviewed source. 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