DeepSeek-V3-0324 GGUF Quantizations
| Property | Value |
|---|---|
| Original Model | DeepSeek-V3-0324 |
| Quantization Types | Q8_0 to IQ1_S |
| Model URL | https://huggingface.co/bartowski/deepseek-ai_DeepSeek-V3-0324-GGUF |
| Author | bartowski |
What is deepseek-ai_DeepSeek-V3-0324-GGUF?
This is a comprehensive collection of GGUF quantizations of the DeepSeek-V3-0324 model, offering various compression levels to accommodate different hardware capabilities and memory constraints. The quantizations range from the highest quality Q8_0 (713.29GB) to the most compressed IQ1_S (133.56GB), each optimized for specific use cases.
Implementation Details
The model uses llama.cpp release b4944 for quantization and implements a specific prompt format: <｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜><｜end▁of▁sentence｜><｜Assistant｜>. The quantizations utilize imatrix options with specialized datasets for optimal performance.
- Comprehensive range of quantization options from Q8_0 to IQ1_S
- Support for online repacking for ARM and AVX CPU inference
- Special optimizations for embed/output weights in certain variants
- Compatible with LM Studio and any llama.cpp based project
Core Capabilities
- High-quality compression with Q6_K and Q5_K variants offering near-perfect performance
- Optimized performance for different hardware architectures (ARM/AVX)
- Memory-efficient options with IQ4_XS and IQ3_XXS variants
- Enhanced tokens/watt performance on Apple silicon
Frequently Asked Questions
Q: What makes this model unique?
The model offers an exceptionally wide range of quantization options, from extremely high quality (Q8_0) to highly compressed versions (IQ1_S), allowing users to balance quality and resource requirements. It also implements advanced features like online repacking for optimal performance on different hardware architectures.
Q: What are the recommended use cases?
For most general use cases, the Q4_K_M variant (404.43GB) is recommended as it offers a good balance of quality and size. For high-end systems, Q6_K (550.80GB) provides near-perfect quality, while systems with limited RAM can benefit from the IQ4_XS (357.13GB) or lower variants.
