GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall specifications with 37B triggered for each token. To achieve efficient inference and cost-effective training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly verified in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free strategy 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 high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities. Comprehensive assessments expose that DeepSeek-V3 outperforms other open-source models and accomplishes efficiency equivalent to leading closed-source models. Despite its exceptional efficiency, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its full training. In addition, its training process is incredibly steady. 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 leader an auxiliary-loss-free method for load balancing, which decreases the performance destruction that occurs from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it useful to model efficiency. It can also be utilized for speculative decoding for reasoning acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 blended precision training structure and, for the very first time, confirm the feasibility and efficiency of FP8 training on an extremely massive model.
– Through co-design of algorithms, structures, and hardware, we conquer the communication traffic jam in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly enhances our training efficiency and decreases the training expenses, enabling us to even more scale up the model size without additional overhead.
– At a cost-effective cost of only 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently greatest open-source base design. The subsequent training phases after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an innovative approach to distill reasoning abilities from the long-Chain-of-Thought (CoT) design, specifically from one of the DeepSeek R1 series designs, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly includes the confirmation and reflection patterns of R1 into DeepSeek-V3 and especially enhances its reasoning performance. Meanwhile, we also maintain a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The overall size of DeepSeek-V3 designs on Hugging Face is 685B, which consists of 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To guarantee optimal efficiency and versatility, we have actually partnered with open-source neighborhoods and hardware vendors to provide numerous methods to run the design in your area. For detailed guidance, take a look at Section 6: How_to Run_Locally.

For designers aiming to dive deeper, we recommend exploring README_WEIGHTS. md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is presently under within the neighborhood, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are shown in bold. Scores with a gap not going beyond 0.3 are thought about to be at the exact same level. DeepSeek-V3 accomplishes the best efficiency on most benchmarks, specifically on mathematics and code tasks. 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 approximately 128K.

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are assessed in a configuration that limits the output length to 8K. Benchmarks consisting of fewer than 1000 samples are checked numerous times using varying temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source design, and also shows competitive efficiency versus frontier closed-source models.

Open Ended Generation Evaluation

English open-ended conversation assessments. For AlpacaEval 2.0, we utilize 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 released locally using the following hardware and open-source community software application:

DeepSeek-Infer Demo: We provide a simple and lightweight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 reasoning for regional and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support coming soon.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively embraced in our structure, we only provide FP8 weights. If you need BF16 weights for experimentation, you can utilize the supplied conversion script to perform the transformation.

Here is an example of transforming FP8 weights to BF16:

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

6.1 Inference with DeepSeek-Infer Demo (example only)

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 set up dependences listed in requirements.txt. Easiest method is to use a package supervisor like conda or uv to create a new virtual environment and install the dependencies.

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

Model Weights Conversion

Convert Hugging Face design weights to a particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch reasoning on a given file:

6.2 Inference with SGLang (recommended)

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

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

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

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

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

6.3 Inference with LMDeploy (advised)

LMDeploy, a versatile and high-performance reasoning and serving framework tailored for big language models, now supports DeepSeek-V3. It provides both offline pipeline processing and online deployment abilities, perfectly incorporating with PyTorch-based workflows.

For extensive step-by-step directions on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 model, offering precision alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be released quickly. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the new features straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (advised)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM uses pipeline parallelism allowing you to run this model on numerous devices connected by networks. For comprehensive guidance, please refer to the vLLM guidelines. Please do not hesitate to follow the improvement plan also.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD group, we have achieved Day-One assistance for AMD GPUs using SGLang, with complete compatibility for both FP8 and BF16 precision. For in-depth assistance, please describe the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend neighborhood has actually successfully adjusted the BF16 variation of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the instructions here.

7. License

This code repository is certified under the MIT License. Using DeepSeek-V3 Base/Chat designs is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports business usage.