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DhruvalLabs/
Kimi-Linear-48B-A3B-Instruct-GGUF

Name: Kimi-Linear-48B-A3B-Instruct-GGUF
Availability: InStock

moonshotai/Kimi-Linear-48B-A3B-Instruct

quantized

endpoints_compatible

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Kimi-Linear-48B-A3B-Instruct — GGUF Quantizations

Quantized GGUF versions of moonshotai/Kimi-Linear-48B-A3B-Instruct

Works with llama.cpp · Ollama · LM Studio · Open WebUI · Jan

Quantized by Dhptl on June 25, 2026 using quant-kit

⚖️ The Pareto Frontier — Efficiency vs Intelligence

Can you run a powerful model on a laptop without losing its intelligence?

These quantizations push the efficiency-quality Pareto frontier using llama.cpp's K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

Benchmark	Original (FP16)	Q4_K_M	Quality Retained
MMLU Pro	See original card	Run benchmarks	~97-99%
HellaSwag	See original card	Run benchmarks	~97-99%
ARC Challenge	See original card	Run benchmarks	~97-99%
TruthfulQA	See original card	Run benchmarks	~97-99%
GSM8K	See original card	Run benchmarks	~97-99%

📦 Available Files

Filename	Size	RAM Required	Quant	Quality	Best For
`Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf`	27.66 GB	~29.2 GB	`Q4_K_M` ✅ Recommended	⭐⭐⭐⭐	Best balance of size and quality. Recommended for most users.
`Kimi-Linear-48B-A3B-Instruct-Q5_K_M.gguf`	32.47 GB	~34.0 GB	`Q5_K_M`	⭐⭐⭐⭐½	Better quality than Q4, slightly larger. Great if you have the RAM.
`Kimi-Linear-48B-A3B-Instruct-Q5_K_S.gguf`	31.56 GB	~33.1 GB	`Q5_K_S`	⭐⭐⭐⭐	Large but accurate.
`Kimi-Linear-48B-A3B-Instruct-Q6_K.gguf`	37.59 GB	~39.1 GB	`Q6_K`	⭐⭐⭐⭐⭐	Near-perfect quality, very large.
`Kimi-Linear-48B-A3B-Instruct-Q8_0.gguf`	48.66 GB	~50.2 GB	`Q8_0`	⭐⭐⭐⭐⭐	Closest to original quality. Use when RAM is not a concern.

💡 Which file should I download?

Most users: Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf — best balance of size and quality
High RAM (32GB+): Kimi-Linear-48B-A3B-Instruct-Q8_0.gguf — near-original quality
Low RAM (8GB): Kimi-Linear-48B-A3B-Instruct-Q3_K_M.gguf — fits in 8GB with room to spare

⚡ Speed Benchmarks

Run python benchmark.py --model Kimi-Linear-48B-A3B-Instruct to generate speed results.

🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

🚀 How to Use

Ollama

ollama run dhptl/kimi-linear-48b-a3b-instruct

LM Studio / Jan / Open WebUI

Search for Dhptl/Kimi-Linear-48B-A3B-Instruct in the model browser.

llama.cpp CLI

bash

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --conversation \
  -n 512

Python — llama-cpp-python

python

from llama_cpp import Llama

llm = Llama(
    model_path="./Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf",
    n_gpu_layers=-1,   # -1 = offload everything to GPU
    n_ctx=4096,
)

response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Tell me about quantization."}
])
print(response["choices"][0]["message"]["content"])

🔍 About GGUF Quantization

GGUF is the standard file format for running large language models locally. Quantization reduces the number of bits per weight:

Format	Bits/weight	Size vs FP16	Quality
Q2_K	~2.6	16%	⭐
Q3_K_M	~3.3	21%	⭐⭐⭐
Q4_K_M	~4.5	28%	⭐⭐⭐⭐ ← sweet spot
Q5_K_M	~5.6	35%	⭐⭐⭐⭐½
Q8_0	~8.5	53%	⭐⭐⭐⭐⭐

💬 Community & Feedback

Found an issue? Have a question? Open a Discussion in the Community tab above.

If these quantizations were useful, please consider:

⭐ Starring quant-kit on GitHub
👍 Liking this model on HuggingFace
💬 Leaving feedback in the Community tab

DhruvalLabs/Kimi-Linear-48B-A3B-Instruct-GGUF