What are the hardware requirements for Kimi K2.7 Code GGUF?

Kimi K2.7 Code GGUF requires approximately 1 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Kimi K2.7 Code GGUF and how large is it?

Kimi K2.7 Code GGUF is developed by unsloth. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate Kimi K2.7 Code GGUF?

Kimi K2.7 Code GGUF is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

Kimi K2.7 Code GGUF by unsloth — VRAM 1GB

Name: Kimi K2.7 Code GGUF
Author: unsloth

Kimi K2.7 Code GGUF is an open-source language model by unsloth. Features: LLM, VRAM: 1GB, License: other, Quantized, LLM Explorer Score: 0.39.

Base model:moonshotai/kimi-k2.... Base model:quantized:moonshota... Compressed-tensors Conversational Custom code Deploy:azure Endpoints compatible Feature-extraction Gguf Image-text-to-text Imatrix Kimi k25 Quantized Region:us Unsloth

Model Card on HF 🤗: https://huggingface.co/unsloth/Kimi-K2.7-Code-GGUF

Kimi K2.7 Code GGUF Parameters and Internals

LLM Name	Kimi K2.7 Code GGUF
Repository 🤗	https://huggingface.co/unsloth/Kimi-K2.7-Code-GGUF
Base Model(s)	moonshotai/Kimi-K2.7-Code moonshotai/Kimi-K2.7-Code
Required VRAM	1 GB
Updated	2026-08-10
Maintainer	unsloth
Model Type	kimi_k25
Model Files	1.0 GB 1.0 GB 1.9 GB
GGUF Quantization	Yes
Quantization Type	gguf
Model Architecture	KimiK25ForConditionalGeneration
License	other
Transformers Version	5.13.0.dev0
Torch Data Type	bfloat16

Rank the Kimi K2.7 Code GGUF capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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