Unsloth Releases Quantized Kimi K3, 1-bit Compresses to 594GB with ~79% Accuracy

Unsloth released quantized versions of Moonshot's Kimi K3 model, enabling local deployment of this massive model. The 1-bit quantized version compresses the original 1.56TB model to 594GB while retaining 78.9% accuracy, offering a feasible solution for local deployment of ultra-large AI models.

Confirmed

• Kimi K3 is an open-weight model with 2.8T parameters, 104B active, native vision capabilities, and 1M token context.
• Unsloth has uploaded multiple quantization options on Hugging Face, including a 1.5TB MXFP4 version and GGUF packages, along with multimodal projection files.
• Through 1-bit quantization, the model size is reduced from 1.56TB to 594GB (62% reduction) while retaining approximately 78.9% accuracy.
• The quantized Kimi K3 has been successfully run locally on a high-memory Mac Studio.

Why it matters

• Lower deployment barrier: Compressing a >1.5TB model to under 600GB allows developers and research institutions to run top-tier models locally on consumer or prosumer hardware (e.g., Mac Studio), reducing reliance on expensive cloud computing.
• Validates quantization techniques: 1-bit quantization achieves nearly 80% accuracy while drastically reducing size (62% reduction), demonstrating the practical potential of extreme quantization for ultra-large models.

2026-07-28 ~ 2026-07-30 · 6 related posts

• Unsloth Releases Kimi K3 Quantized Version — _akhaliq · 2026-07-28
• [source] Unsloth Releases Kimi K3 GGUFs: MXFP4 Version Reaches 1.5TB — _TheWolfOfWalmart_ · 2026-07-29
• Moonshot’s Kimi K3 now runs locally at 594 GB with 78.9% top-1 accuracy — danielhanchen · 2026-07-29
• Quantized Kimi K3 lands on Hugging Face in GGUF formats — victormustar · 2026-07-29
• [source] 1-bit Quantization Magic: Kimi K3 Successfully Runs Locally on Mac Studio — danielhanchen · 2026-07-30
• [source] Unsloth Releases Kimi K3 Quantized: 1-bit Compresses to 594GB Retaining 78.9% Accuracy — BankApprehensive7612 · 2026-07-30