DeepSeek-V3-GGUF
| Property | Value |
|---|---|
| Total Parameters | 671B |
| Activated Parameters | 37B |
| Context Length | 128K |
| License | MIT (Code), Custom Model License |
| Paper | arXiv:2412.19437 |
What is DeepSeek-V3-GGUF?
DeepSeek-V3-GGUF is a state-of-the-art Mixture-of-Experts (MoE) language model that represents a significant advancement in efficient AI model design. With 671B total parameters but only 37B activated for each token, it achieves remarkable performance while maintaining computational efficiency. The model features innovative architectures including Multi-head Latent Attention (MLA) and DeepSeekMoE, trained on 14.8 trillion diverse tokens.
Implementation Details
The model is available in various quantization formats, from Q2_K_XS (207GB) to Q8_0 (712GB), offering flexibility in deployment based on hardware constraints. It utilizes an auxiliary-loss-free strategy for load balancing and implements Multi-Token Prediction for enhanced performance.
- Supports multiple quantization levels for different hardware configurations
- Implements efficient K quantization for optimal performance
- Features automatic llama.cpp offloading capability
- Requires special tokens: <｜User｜> and <｜Assistant｜> for proper functioning
Core Capabilities
- Outperforms other open-source models in benchmarks
- Achieves strong performance in math, code, and reasoning tasks
- Supports 128K context length with stable performance
- Compatible with multiple inference frameworks including SGLang, LMDeploy, and TensorRT-LLM
- Runs on various hardware including NVIDIA, AMD GPUs, and Huawei Ascend NPUs
Frequently Asked Questions
Q: What makes this model unique?
DeepSeek-V3 stands out for its efficient MoE architecture that activates only 37B of its 671B parameters per token, combined with innovative features like auxiliary-loss-free load balancing and Multi-Token Prediction. It achieves performance comparable to leading closed-source models while maintaining efficient resource usage.
Q: What are the recommended use cases?
The model excels in various tasks including complex mathematics, code generation, and general language understanding. It's particularly well-suited for applications requiring strong reasoning capabilities, long-context understanding, and multi-lingual support, while being deployable across different hardware configurations through various quantization options.
