GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall specifications with 37B activated for each token. To attain effective reasoning and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely verified in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training goal for stronger efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its abilities. Comprehensive evaluations expose that DeepSeek-V3 outperforms other open-source models and achieves efficiency comparable to leading closed-source models. Despite its outstanding efficiency, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training process is incredibly stable. Throughout the entire training procedure, 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 efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free strategy for load balancing, which lessens the efficiency deterioration that arises from motivating load balancing.
– We examine a Multi-Token Prediction (MTP) goal and show it advantageous to design efficiency. It can likewise be utilized for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 blended precision training structure and, for the very first time, validate the feasibility and efficiency of FP8 training on an incredibly large-scale model.
– Through co-design of algorithms, structures, and hardware, we conquer the interaction traffic jam in cross-node MoE training, nearly achieving full computation-communication overlap.
This considerably boosts our training efficiency and decreases the training expenses, allowing us to even more scale up the design size without additional 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 currently greatest open-source base model. The subsequent training phases after pre-training need only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

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

3. Model Downloads

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

To make sure ideal performance and flexibility, we have partnered with open-source communities and to supply multiple methods to run the model in your area. For step-by-step assistance, take a look at Section 6: How_to Run_Locally.

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

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are displayed in strong. Scores with a space not exceeding 0.3 are considered to be at the exact same level. DeepSeek-V3 accomplishes the very best efficiency on a lot of standards, specifically on math and code tasks. For more assessment details, please check our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are examined in a configuration that limits the output length to 8K. Benchmarks consisting of fewer than 1000 samples are checked several times using varying temperature level settings to obtain robust results. 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 conversation examinations. 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 site: chat.deepseek.com

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

6. How to Run Locally

DeepSeek-V3 can be deployed locally using the following hardware and open-source neighborhood software application:

DeepSeek-Infer Demo: We offer a simple and light-weight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables effective FP8 and BF16 inference for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support coming quickly.
vLLM: Support DeepSeek-V3 design with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs by means of 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 framework, we just offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the supplied conversion script to perform the improvement.

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 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 reasoning folder and install dependences listed in requirements.txt. Easiest method is to use a package supervisor like conda or uv to produce a new virtual environment and set up 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 design weights to a particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch reasoning on a provided file:

6.2 Inference with SGLang (advised)

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing modern latency and throughput efficiency among open-source structures.

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

Multi-Token Prediction (MTP) remains in advancement, and progress can be tracked in the optimization strategy.

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

6.3 Inference with LMDeploy (recommended)

LMDeploy, a flexible and high-performance reasoning and serving framework customized for large language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online release abilities, flawlessly incorporating with PyTorch-based workflows.

For extensive detailed guidelines on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (recommended)

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

6.5 Inference with vLLM (suggested)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard strategies, vLLM offers pipeline parallelism allowing you to run this model on several devices linked by networks. For in-depth assistance, please refer to the vLLM instructions. Please feel totally free to follow the enhancement plan also.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have accomplished Day-One support for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For in-depth assistance, please refer to the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has actually successfully adapted 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 accredited under the MIT License. Using DeepSeek-V3 Base/Chat designs is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports industrial usage.