GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B overall specifications with 37B activated for each token. To achieve efficient inference and cost-efficient training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely validated in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token prediction training goal for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to fully harness its capabilities. Comprehensive examinations reveal that DeepSeek-V3 outperforms other open-source designs and accomplishes efficiency comparable to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its complete training. In addition, its training process is incredibly stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.

2. Model Summary

Architecture: Innovative Load Balancing Strategy and Training Objective

– On top of the effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free technique for load balancing, which reduces the efficiency destruction that develops from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it beneficial to model performance. It can also be used for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 blended accuracy training structure and, for the very first time, verify the expediency and effectiveness of FP8 training on a very massive design.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the communication bottleneck in cross-node MoE training, nearly achieving complete computation-communication overlap.
This considerably boosts our training effectiveness and minimizes the training expenses, allowing us to further scale up the design size without extra overhead.
– At an affordable cost of just 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently strongest open-source base design. The subsequent training phases after pre-training need just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious methodology to distill reasoning abilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series designs, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably improves its reasoning performance. Meanwhile, we likewise keep a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The total size of DeepSeek-V3 models on Hugging Face is 685B, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To guarantee optimal efficiency and flexibility, we have actually partnered with open-source communities and hardware suppliers to supply multiple methods to run the design in your area. For detailed assistance, take a look at Section 6: How_to Run_Locally.

For developers looking to dive much deeper, we recommend checking out README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is currently under active development within the community, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Benchmarks

Best results are revealed in vibrant. Scores with a gap not surpassing 0.3 are thought about to be at the exact same level. DeepSeek-V3 achieves the very best performance on the majority of benchmarks, particularly on math and code jobs. For more evaluation information, please examine our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well throughout all context window lengths as much as 128K.

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are evaluated in a setup that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are tested multiple times utilizing differing temperature level settings to derive robust final outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise shows competitive performance against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion assessments. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek’s main website: chat.deepseek.com

We likewise provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed in your area utilizing the following hardware and open-source community software application:

DeepSeek-Infer Demo: We offer a basic and lightweight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 assistance coming soon.
vLLM: Support DeepSeek-V3 design with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design on AMD GPUs by means of SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend devices.
Since FP8 training is natively embraced in our framework, we just supply FP8 weights. If you need BF16 weights for experimentation, you can use the supplied conversion script to perform the transformation.

Here is an example of converting FP8 weights to BF16:

Hugging Face’s Transformers has not been straight supported yet. **

6.1 Inference with DeepSeek-Infer Demo (example just)

System Requirements

Note

Linux with Python 3.10 only. Mac and Windows are not supported.

Dependencies:

Model Weights & Demo Code Preparation

First, clone our DeepSeek-V3 GitHub repository:

Navigate to the inference folder and install dependences noted in requirements.txt. Easiest method is to use a bundle supervisor like conda or uv to develop a brand-new virtual environment and install the dependences.

Download the model weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.

Model Weights Conversion

Convert Hugging Face model weights to a specific format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference on a given file:

6.2 Inference with SGLang (advised)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing modern latency and throughput performance amongst open-source frameworks.

Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely versatile and robust solution.

SGLang likewise supports multi-node tensor parallelism, allowing you to run this design on several network-connected devices.

Multi-Token Prediction (MTP) remains in development, and development can be tracked in the optimization plan.

Here are the launch guidelines from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

6.3 Inference with LMDeploy (suggested)

LMDeploy, a flexible and high-performance inference and serving framework tailored for large language designs, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation abilities, effortlessly integrating with PyTorch-based workflows.

For thorough step-by-step instructions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (advised)

TensorRT-LLM now supports the DeepSeek-V3 model, using precision choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be released quickly. You can access the custom branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the brand-new features directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (advised)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard methods, vLLM provides pipeline parallelism enabling you to run this model on numerous makers linked by networks. For in-depth guidance, please refer to the vLLM directions. Please feel complimentary to follow the enhancement plan as well.

6.6 Recommended Inference Functionality with AMD GPUs

In collaboration with the AMD team, we have attained Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For comprehensive guidance, please refer to the SGLang directions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend neighborhood has actually effectively adjusted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the guidelines here.

7. License

This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat designs undergoes the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports commercial usage.